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Lead Machine Learning Engineer
New York City, NY – Hybrid (3 days per week onsite)

$200,000 – $220,000 Base Salary+ Bonus + RSU package available

THE COMPANY

We are partnering with a leading consumer technology and financial services organization that operates at global scale and serves hundreds of millions of users. The business leverages advanced data, machine learning, and real-time decisioning systems to deliver highly personalized customer experiences across a broad portfolio of digital products.

This is an exciting opportunity to join a rapidly expanding machine learning engineering team at a pivotal stage of growth. The organization is investing heavily in recommendation systems, real-time personalization, machine learning platforms, and next-generation AI capabilities, offering engineers the opportunity to work with large-scale distributed systems and production-grade ML infrastructure.

RESPONSIBILITIES

• Design, build, and maintain scalable infrastructure supporting machine learning training, deployment, and inference workloads.
• Develop and optimize backend services, microservices, and cloud-native applications that power real-time machine learning systems.
• Own and enhance ML platform capabilities across cloud infrastructure, model serving, monitoring, and operational tooling.
• Partner closely with Data Scientists to productionize machine learning models and support real-time recommendation and personalization use cases.
• Improve CI/CD pipelines, infrastructure-as-code frameworks, observability, reliability, and system scalability.
• Participate in operational ownership, incident response, and support for critical production services.

SKILLS AND EXPERIENCE

Must-Have

• 7+ years of software engineering or machine learning engineering experience.
• Strong backend engineering expertise with experience building distributed systems at scale.
• Proven experience developing Scala-based microservices and production-grade backend applications.
• Deep knowledge of AWS cloud services, including machine learning infrastructure and managed platforms.
• Hands-on experience with Kubernetes, Docker, Terraform, and modern CI/CD practices.
• Strong Python programming skills.
• Track record of owning production systems, reliability, monitoring, and operational excellence.

Nice-to-Have

• Experience with machine learning infrastructure, MLOps, or model-serving platforms.
• Knowledge of recommendation systems, personalization engines, or CTR optimization.
• Experience with Datadog observability and monitoring.
• Background in adtech, fintech, e-commerce, or other high-scale consumer platforms.
• Exposure to real-time machine learning applications and online inference systems.

BENEFITS

• Competitive base salary and annual bonus
• Equity participation through RSUs
• Hybrid working model
• Opportunity to work on cutting-edge AI and machine learning initiatives
• Significant career growth and technical leadership opportunities
• Exposure to large-scale, real-time production systems

HOW TO APPLY

Please register your interest by submitting your CV via the Apply link on this page.

KEY TERMS

Lead Machine Learning Engineer | Machine Learning Engineering | ML Infrastructure | Scala | Python | AWS | SageMaker | Kubernetes | Docker | Terraform | CI/CD | Distributed Systems | Recommendation Systems | Real-Time Systems | MLOps | Backend Engineering | Cloud Infrastructure | Platform Engineering | Datadog | Fintech | Personalization | ML Platform | Software Engineering | Hybrid NYC | Technical Leadership

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Lead Data Scientist

London – 4x a week

The Company
They are a fast-growing technology organisation that helps businesses unlock value from complex data through advanced analytics, machine learning, and AI-driven decision-making. Their products support a range of commercial use cases, with a strong focus on delivering measurable business outcomes. As they continue to scale, they are investing heavily in their Data & AI capabilities.

The Role

• Lead, mentor, and develop a team of Data Scientists while defining best practices across the function
• Design and deploy machine learning models across forecasting, customer segmentation, and propensity modelling
• Own end-to-end model development including feature engineering, validation, deployment, and monitoring
• Partner with Engineering and Product teams to build scalable, production-ready data science solutions
• Translate analytical outputs into clear commercial recommendations for senior stakeholders
• Drive the adoption of AI tooling and robust model governance across the data science lifecycle

Your Skills & Experience

• Strong commercial experience in Data Science, Machine Learning, or Applied AI
• Experience leading and developing Data Science teams
• Advanced Python and SQL skills
• Expertise in supervised and unsupervised machine learning techniques
• Experience deploying models into production environments using MLOps practices
• Strong communication skills with the ability to influence technical and non-technical stakeholders

What They Offer

• Performance-related bonus
• Private healthcare and additional benefits package
• Opportunity to shape and scale a Data Science function
• Exposure to cutting-edge AI and machine learning projects
• Clear career progression in a high-growth environment

How to Apply
If you’re looking for a Lead Data Scientist opportunity that combines leadership, technical depth, and business impact, please apply today through Harnham.

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ML Engineering Manager

US-based remote

$220,000-$240,000 base + annual bonus + equity

THE COMPANY

Join a high-growth technology company at the forefront of machine learning and risk intelligence. The organization develops advanced AI and machine learning solutions that power critical decision-making at scale, helping businesses manage risk, improve outcomes, and unlock new opportunities.

This is an opportunity to lead a highly technical team operating at the intersection of machine learning, software engineering, experimentation, and applied statistics. The environment is collaborative, research-driven, and focused on delivering measurable business impact through data-informed innovation.

RESPONSIBILITIES

Lead and Develop a High-Performing Team

• Lead, mentor, and develop a team of machine learning engineers and applied scientists, supporting career growth and technical excellence.
• Foster a culture of experimentation, knowledge sharing, continuous learning, and constructive feedback.
• Conduct regular 1:1s, performance discussions, and coaching to help team members achieve their professional goals.
• Drive hiring efforts, participate in technical interviews, and help maintain a high talent bar as the team scales.
• Identify opportunities to improve team processes, collaboration, and delivery effectiveness.

Drive Machine Learning Innovation and Delivery

• Partner with technical leads to shape and refine the roadmap for machine learning initiatives and model improvements.
• Oversee a portfolio of experiments, balancing near-term business objectives with longer-term research and innovation opportunities.
• Apply rigorous evaluation frameworks to ensure model improvements translate successfully from offline testing to production environments.
• Make informed prioritization decisions regarding resource allocation, experimentation, and delivery timelines.
• Guide teams through the full lifecycle of model development, validation, deployment, and release.

Partner with Business and Technical Stakeholders

• Collaborate closely with business stakeholders to define, measure, and deliver against performance objectives.
• Work with platform and infrastructure teams to enhance experimentation capabilities, training pipelines, and ML tooling.
• Translate complex technical concepts and model performance metrics into clear business impact and strategic recommendations.
• Present results, insights, and recommendations to senior leadership and cross-functional stakeholders.

SKILLS AND EXPERIENCE

• 5+ years of experience in Machine Learning, Data Science, Applied AI, or related software engineering disciplines.
• At least 2 years of people management experience (3+ direct reports), including coaching, performance management, and team development; owning team KPIs
• Strong technical depth in machine learning, applied statistics, software engineering, or a combination of these disciplines.
• Experience evaluating and deploying production ML models in large-scale environments, ideally in credit risk, fraud detection, payment processing or similar.
• Proven ability to lead teams through ambiguity, experimentation, and data-driven decision-making.
• Strong understanding of model performance evaluation, experimentation methodologies, and specifically with statistical rigor – Bayesian, causal inference, etc.
• Excellent written and verbal communication skills, with the ability to influence both technical and non-technical audiences.
• Demonstrated ability to balance research initiatives with delivery commitments in a fast-paced environment.
• Passion for engineering quality, reliability, reproducibility, and operational excellence.

BENEFITS

• Competitive compensation package.
• Annual bonus.
• Equity participation opportunities.
• Comprehensive health and wellness benefits.
• Flexible and hybrid working arrangements.
• Professional development and learning opportunities.
• Opportunity to work on cutting-edge machine learning challenges with significant business impact.

HOW TO APPLY

Please register your interest by sending your CV via the Apply link on this page.

KEY TERMS

ML Engineering Manager | Machine Learning Manager | AI Engineering | Data Science Leadership | Applied Machine Learning | MLOps | Experimentation | Statistical Modeling | Team Leadership | Risk Analytics | Predictive Modeling | Production ML | Engineering Management | AI Research | Machine Learning Infrastructure | Python | Model Evaluation | Distributed Teams | Technical Leadership | Data-Driven Decision Making | Bayesian Statistics | Causal Inference | Fraud Detection | Anomaly Detection | Credit Risk | Payment Processing

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Principal Machine Learning Engineer

Location: Paris (Remote / Hybrid – Fully Remote or 2 Days in the Office)
Salary: €135,000 Base + €15,000 Bonus (OTE €150,000)

THE COMPANY

This profitable French AdTech scale-up is transforming digital advertising through machine learning. By building AI-powered technology that determines which users are most likely to engage with an advert, they help leading mobile apps drive customer acquisition and re-engagement through highly targeted advertising.

Following approximately 60% growth over the past two years, the business has expanded to around 140 employees across offices in Paris, Barcelona, Seoul and San Francisco, with more than 25 open positions supporting continued international growth.

You’ll be joining a highly technical engineering team, working directly alongside the CTO to shape the future of machine learning across the business.

THE ROLE

As a Principal Machine Learning Engineer, you’ll define the technical direction of large-scale production machine learning systems while remaining hands-on throughout the full model lifecycle. This is an individual contributor leadership role focused on delivering complex technical projects rather than managing people.

You’ll lead end-to-end machine learning initiatives across recommendation systems, audience targeting, bidding optimisation and customer lifecycle modelling, partnering closely with senior engineering and business stakeholders to translate commercial objectives into production-ready AI solutions. The role combines technical strategy, platform engineering and applied machine learning within a high-scale real-time environment.

YOUR SKILLS AND EXPERIENCE

• Extensive experience building and deploying production machine learning systems at scale
• Proven technical leadership, defining architecture and technical direction while remaining hands-on
• Strong experience developing recommendation systems, audience targeting models, LTV or churn models
• Experience building robust MLOps platforms including model monitoring, automated retraining and production deployment
• Excellent Python and modern machine learning framework experience (PyTorch, TensorFlow or similar)
• Strong software engineering and distributed systems knowledge
• Ability to communicate complex technical concepts to senior stakeholders
• AdTech or Demand Side Platform (DSP) experience highly desirable
• PhD in a STEM discipline is advantageous

THE BENEFITS

• €135,000 base salary plus approximately €15,000 annual bonus (OTE €150,000)
• Longevity bonus every two years
• 4.5-day working week with Friday afternoons off
• Fully remote working or hybrid (2 days per week if based near an office)
• Two company meet-ups each year
• Work directly alongside the CTO
• Join a profitable, high-growth international AI business with significant technical ownership opportunities

INTERVIEW PROCESS

• 15-minute HR conversation
• Take-home technical assessment
• Technical interview and assessment review with senior ML engineers
• CTO interview
• Executive Committee interview

HOW TO APPLY

Please register your interest by sending your CV to Max Beadle at Harnham via the Apply link on this page.

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Principal Machine Learning Engineer

Europe Remote | Up to €135,000 Base + Bonus (c.€150,000 OTE)

This is an opportunity to join a growing, profitable AI-driven technology business where machine learning is central to the product and commercial strategy. The role offers a rare blend of technical leadership and hands-on engineering, allowing you to shape the future direction of ML systems while solving complex challenges at scale.

The Company

They are an established technology scale-up that develops advanced machine learning solutions to power highly personalised digital experiences for millions of users worldwide. Operating internationally, the business has grown significantly in recent years and continues to invest heavily in its data and AI capabilities.

Their machine learning teams work on large-scale real-time systems where model performance directly impacts commercial outcomes. With a collaborative engineering culture and strong leadership support, they offer an environment where innovation and experimentation are encouraged.

The Role

As a Principal Machine Learning Engineer, you will provide technical leadership across the machine learning function while remaining closely involved in model development and production systems.

Key responsibilities include:

• Defining technical direction for machine learning systems and frameworks.
• Designing, developing and optimising machine learning models in a large-scale production environment.
• Leading the development of recommendation engines, bidding algorithms and audience targeting systems.
• Building mobile attribution models and solutions focused on customer lifetime value and churn prediction.
• Driving best practice across MLOps, model monitoring, observability and automated retraining.
• Enhancing machine learning infrastructure, experimentation platforms and deployment processes.
• Partnering with engineering leaders and business stakeholders to align machine learning initiatives with commercial objectives.
• Mentoring engineers and contributing to architectural decisions across the wider technology organisation.

Your Skills & Experience

• Strong commercial experience building and owning machine learning systems in production.
• Experience developing and deploying machine learning models at scale.
• Advanced Python skills alongside experience with modern machine learning frameworks such as PyTorch or TensorFlow.
• Excellent understanding of MLOps, model lifecycle management and production machine learning environments.
• Experience working with recommendation systems, predictive modelling, LTV modelling, churn prediction or related machine learning applications.
• Strong software engineering fundamentals with a focus on scalability, performance and reliability.
• Ability to communicate technical concepts effectively to both technical and non-technical stakeholders.
• Experience within AdTech, programmatic advertising, real-time bidding or demand-side platforms would be highly beneficial.

Nice to Have

• PhD in a STEM discipline.
• Experience within DSP environments.
• Exposure to mobile advertising, attribution modelling or related performance marketing technologies.

What They Offer

• 4.5-day working week with Friday afternoons off.
• Flexible remote working policy.
• Longevity bonus awarded every two years.
• Strong work-life balance and employee wellbeing initiatives.
• The opportunity to shape machine learning strategy within a growing and profitable technology business.
• Clear opportunities for progression and increased technical influence.

Interview Process

1. Introductory conversation with the Talent Acquisition team.
2. Take-home technical assessment.
3. Technical interview and assessment review with senior machine learning engineers.
4. Interview with the CTO.
5. Final executive leadership interview.

How to Apply

If you are an experienced Principal Machine Learning Engineer looking to combine technical leadership with hands-on machine learning development, please apply to learn more about this opportunity.

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Senior MLOps Engineer

New York, New York – 5 days onsite

$160,000 – $180,000 + bonus

THE COMPANY

Harnham is partnering with a top financial services company in the NYC area, which is looking for an experienced MLOps Engineer. This person will be at the forefront of scaling AI automation applications for new ways at identifying cost-saving opportunities. You’ll partner with executive teams across the company and own generative AI, LLM and reinforcement learning application infrastructure deployment at scale.

RESPONSIBILITIES

• You will be responsible for machine learning model deployment and scalability for the company’s AI platform
• You will report directly to senior leadership and work closely on technical direction
• Own AI infrastructure and quickly build into production, particularly focusing on novel AI and LLM applications
• You will implement and design code and build out to production using various machine learning and LLM techniques, owning machine learning workflow operations and distributed systems
• Own CI/CD pipelines for MLOps / LLMOps
• You will play an integral role of building out the AI team and scaling out its product
• Act as a thought leader role for AI across the business

SKILLS AND EXPERIENCE

• 5+ years of commercial experience preferred with a focus on deploying and scaling machine learning and LLM models
• Expertise in Python (TensorFlow, PyTorch) for production-grade work
• Commercial experience building novel AI platforms with large datasets
• History of working with and managing real-time AI applications in production settings
• Fluency with low-latency distributed systems is preferred
• Cloud experience in AWS, Azure or GCP
• DevOps experience with CI/CD pipelines required; Gitlab Pipelines, CDK or Terraform
• Container experience with Kubernetes, Docker or similar
• History of working on models from concept to production / end-to-end / 0-1
• Software engineering foundation: debugging, testing, maintainable code design
• Domain experience in fintech or similar a plus
• History of partnering with non-technical stakeholders required
• Experience owning projects directly preferred
• BS or MS degree in Computer Science, Software Engineering, Computer Engineering or similar

BENEFITS

The compensation package contains a base salary, bonus and a comprehensive benefits package.

HOW TO APPLY

Please register your interest by sending your CV via the Apply link on this page.

KEY TERMS

Artificial Intelligence | Generative AI | GenAI | Machine Learning | ML Engineer | Engineering | Deployment | Production | Real Time | Enterprise | Statistics | Mathematics | Financial Services | Banking | Python | Anomaly Detection | Fintech | Agentic AI | AI Agents | MLOps | ML Operations | Deployment | Deploy | Production | CI/CD | Container | Docker | Gitlab Pipelines | Terraform | Kubernetes

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ML Scientist / Researcher

Oncology AI · Foundation Models · Life Sciences

Remote

About the Role

We are building foundation models trained on human tumor biology – one of the most consequential and technically demanding challenges at the intersection of AI and medicine. As an ML Scientist, you will be a core research contributor designing and training these models across multimodal omics datasets, partnering closely with biologists and fellow research scientists to advance the state of the art in oncology AI.

This is a research-forward role for scientists who want their work to matter. We are looking for people with a track record of research excellence – those who have gone deep on model architecture, training dynamics, and rigorous experimental design. If you have built models from the ground up and published findings, we want to talk.

What You’ll Do

• Design and train large-scale foundation models on multimodal biological datasets, including genomics, transcriptomics, and other omics modalities
• Collaborate deeply with computational biologists, research scientists, and domain experts to translate biological questions into tractable modeling problems
• Drive the full research lifecycle: hypothesis formation, experimental design, model development, and rigorous analysis of results
• Contribute to agentic AI systems that reason over complex biological data
• Communicate findings internally and, where appropriate, through peer-reviewed publication

What We’re Looking For

Must-Haves

• Strong research background, typically evidenced by a PhD in machine learning, computational biology, statistics, physics, or a related quantitative field – or equivalent industry research experience
• Demonstrated ability to build and train models end-to-end, including experimental analysis and iteration
• Research excellence: first-author publications at top ML, AI, or computational biology venues are a strong positive signal
• Deep familiarity with foundation model concepts: pretraining, self-supervised learning, attention mechanisms, and large-scale training
• Comfort working at the intersection of biology and machine learning – even without a formal biology degree

Nice-to-Haves

• Experience with biological or omics data (genomics, proteomics, pathology imaging, etc.)
• Prior work in multimodal learning or multi-omics integration
• Familiarity with agentic AI systems or tool-use frameworks
• Background in oncology or disease biology

What This Role Is Not

This is not a production ML engineering or MLOps role. We are not looking for candidates whose primary experience is model deployment, serving infrastructure, or engineering-heavy systems work. The emphasis here is firmly on research depth and model development.

Compensation & Location

Base Salary: $250,000 – $288,000 (depending on experience) + equity

Location: Remote-friendly; office in South San Francisco, CA

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AI Engineer
London (Hybrid, 3 days per week)

This is an opportunity to join a newly formed AI function at a pivotal stage of growth. You’ll work on high-impact AI initiatives across machine learning, Generative AI and Agentic AI, helping to shape how AI is adopted and scaled across a major organisation.

The Company
Our client is investing significantly in AI as part of a large-scale transformation programme. Operating through a central AI Centre of Excellence, they are building scalable, production-ready AI solutions that deliver measurable business value. This is a chance to join early, influence standards and best practice, and play a key role in a growing AI engineering team.

The Role
You will:

• Design, build and deploy AI and machine learning solutions from concept to production
• Develop applications using LLMs, Generative AI and Agentic AI frameworks
• Deliver predictive, optimisation and automation use cases
• Build robust MLOps and LLMOps processes, including monitoring and evaluation
• Work closely with engineers, data scientists, product teams and business stakeholders
• Help establish AI engineering standards, governance and responsible AI practices
• Contribute to the long-term technical direction of the AI team

Your Skills & Experience

• Strong Python and software engineering background
• Commercial experience delivering machine learning models into production
• Hands-on experience with GenAI, LLMs and AI application development
• Knowledge of MLOps, LLMOps, CI/CD and containerisation
• Experience with cloud platforms, ideally Azure
• Understanding of model evaluation, monitoring and responsible AI
• Strong communication skills with the ability to translate business requirements into technical solutions

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AI Engineer
London (Hybrid – 3x a week)

The Company
They are a large, data-driven organisation investing heavily in artificial intelligence and advanced analytics. Their AI team is focused on building innovative solutions that improve business operations, enhance customer experiences, and drive measurable commercial value. With access to large and complex datasets, they offer an environment where experimentation, collaboration, and technical excellence are encouraged.

The Role

• Design, build and deploy scalable AI and machine learning solutions, taking projects from concept through to production.
• Develop AI-powered applications using Generative AI, Large Language Models and orchestration frameworks.
• Write high-quality Python code and contribute to cloud-native applications and services.
• Apply MLOps best practices, including CI/CD, containerisation, monitoring and observability.
• Partner with data scientists, engineers and business stakeholders to translate business challenges into impactful AI solutions.
• Monitor, evaluate and improve deployed models while ensuring responsible, secure and reliable AI deployment.

Your Skills & Experience

• Strong commercial experience in AI engineering, machine learning engineering or software engineering with applied AI.
• Proven experience building and deploying Generative AI applications using LLMs and orchestration frameworks.
• Strong Python programming skills and understanding of modern backend architectures.
• Experience working with cloud platforms and production-grade AI systems.
• Knowledge of MLOps, CI/CD, containerisation and model monitoring.
• Experience building data pipelines for model training, fine-tuning and inference.
• Solid grounding in machine learning, statistics and model evaluation techniques.
• Strong communication skills with the ability to engage technical and non-technical stakeholders.
• Commitment to responsible and ethical AI development.

What They Offer

• Competitive salary and comprehensive benefits package.
• Hybrid working model.
• Exposure to large-scale AI and machine learning projects.
• Opportunity to shape the direction of a growing AI capability.
• Ongoing professional development and clear career progression.
• Collaborative environment working alongside experienced AI, data and engineering professionals.

How to Apply
If you’re an AI Engineer looking to build and deploy innovative AI solutions in a high-impact environment, please apply today to learn more about this opportunity.

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Senior Machine Learning Engineer

$140,000-$190,000

Overview
A growing healthcare-focused technology organisation is seeking a Senior Machine Learning Engineer to help expand and support enterprise AI and machine learning capabilities. This role combines machine learning engineering, MLOps, cloud infrastructure, and generative AI operations.
The ideal candidate has extensive experience deploying, monitoring, and scaling production machine learning systems and is comfortable supporting both traditional ML models and modern large language model (LLM) applications. This position is focused on operational excellence, reliability, scalability, and measurable business outcomes rather than research.
Key Responsibilities

• Machine Learning Engineering & Platform Development
• Design, build, and maintain production-grade machine learning systems.
• Deploy and support machine learning models in live environments.
• Develop scalable training and inference workflows.
• Improve system reliability, performance, and operational efficiency.
• Partner with data science teams to productionise models and analytics solutions.
• MLOps & Infrastructure
• Develop and maintain feature engineering pipelines.
• Build and enhance workflow orchestration solutions.
• Establish best practices for model deployment and lifecycle management.
• Implement monitoring, observability, and alerting frameworks.
• Support model governance and operational compliance requirements.
• Generative AI & LLM Operations
• Deploy and maintain applications powered by large language models.
• Monitor model performance, quality, and reliability.
• Optimise prompts, inference processes, and operational workflows.
• Support cloud-hosted foundation model services.
• Implement improvements that enhance model effectiveness and user experience.
• Business Impact
• Contribute to predictive and decision-support solutions within healthcare and life sciences domains.
• Support projects involving optimisation, forecasting, classification, and recommendation systems.
• Deliver scalable solutions that create measurable value for end users and stakeholders.

Required Qualifications

• Technical Skills
• Strong Python development experience.
• Advanced SQL proficiency.
• Hands-on experience with AWS cloud services.
• Experience with machine learning platforms such as SageMaker.
• Experience using cloud-based LLM and foundation model services.
• DBT experience.
• Workflow orchestration experience with Prefect or similar platforms such as Airflow.
• Experience
• Approximately 6+ years of Machine Learning Engineering experience.
• Proven experience deploying machine learning models into production.
• Experience supporting model training and inference workloads.
• Strong model monitoring and observability experience.
• Ownership of MLOps processes and production ML environments.
• Experience designing and maintaining feature engineering pipelines.
• Infrastructure-focused engineering mindset with strong software engineering practices.
• Healthcare industry experience is required, including familiarity with:
• Regulated environments.
• Compliance and security requirements.
• Model governance practices.
• Responsible AI considerations, including fairness and bias mitigation.
• Preferred Qualifications
• Experience building ML-powered products used by external customers.
• Experience supporting models after deployment in production environments.
• Exposure to both traditional machine learning and LLM-based applications.
• Strong communication and stakeholder management skills.
• Demonstrated ability to deliver measurable business results through machine learning solutions.

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