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Principal Data Scientist
Full Remote (From the US)
Salary up to $210K base + bonus

A large, global technology-led organisation operating in a mission-driven, data-intensive domain. The business designs and scales complex digital platforms used by millions of end users worldwide, combining advanced data science, AI, and product engineering to drive measurable real-world impact.

Mission

• Act as technical lead for high-impact Data Science and AI initiatives across the organisation
• Define architectures, methodologies, and best practices for complex DS/AI and GenAI problem spaces
• Lead the design and delivery of production-grade AI systems, with particular focus on LLMs and generative AI
• Drive strategic initiatives by aligning DS/AI priorities with business and product objectives
• Mentor senior and junior data scientists, setting technical direction and raising engineering standards
• Own multiple projects simultaneously: scoping, prioritisation, delivery timelines, and stakeholder alignment

Profile

• PhD or equivalent in Data Science, Applied Mathematics, Statistics, or related field
• Senior-level experience: typically 6-10+ years in DS/AI roles, including leadership at Principal / Staff level
• Demonstrated technical leadership: defining strategy, owning complex initiatives, and mentoring teams
• Deep hands-on experience with production-scale DS/AI systems, with a strong recent focus on Generative AI / LLMs
• Expertise in LLM architectures, orchestration frameworks, and AI governance (guarding, scaling, monitoring, reliability)
• Strong understanding of MLOps / LLMOps in enterprise environments

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Senior ML Engineer
Full Remote
$180K-$200K base

A fast-growing AI product company operating in real-time decisioning, large-scale machine learning, and data-driven optimization. The business builds advanced AI systems used by major brands to automate and optimize high-impact decisions at massive scale. Strong engineering culture, modern ML infrastructure, and real product ownership in a highly technical environment.

Mission

• Architect, train, and maintain distributed machine learning systems powering core AI products
• Build and optimize neural network models for large-scale prediction, classification, and optimization
• Develop feature engineering pipelines on big data using Spark and modern data platforms
• Own the full ML lifecycle: training, tuning, evaluation, batch inference, and observability
• Design and operate production-grade ML pipelines with a focus on reliability, automation, and reproducibility

Profile:

• 5-10 years of industry experience building and deploying ML systems at scale
• Hands-on experience with distributed training and large-scale data processing (e.g., Ray, Spark, multi-GPU)
• Deep understanding of MLOps best practices: model versioning, experiment tracking, monitoring, reproducibility
• Familiarity with cloud platforms (AWS or equivalent), modern data stacks, and CI/CD pipelines
• Bonus: experience in adtech, optimization systems, reinforcement learning, LLMs, or large-scale embeddings

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Senior Machine Learning Engineer

Location: Fully Remote (Hybrid option in NYC)
Compensation: $190,000-$200,000 + Equity
Type: Full-Time

About the Company
We are partnering with a fast-growing, profitable SaaS company that is redefining programmatic advertising through AI-driven solutions. Their platform empowers brands to deploy custom algorithms across major DSPs (The Trade Desk, DV360, Meta, YouTube), enabling smarter, real-time ad-buying decisions. Backed by leading industry investors, this company is building ML infrastructure and solving problems that don’t have off-the-shelf solutions.

Why Join?

• Work on distributed ML infrastructure using Ray + PyTorch on Databricks.
• Build solutions that create a new software layer in ad-tech.
• Flexible working hours, fully remote, and equity participation.
• Opportunity to grow into Principal ML Engineer later this year.

Role Overview

As a Senior Machine Learning Engineer, you’ll own the ML lifecycle end-to-end, focusing on productionization and robust MLOps practices. You’ll work closely with data science and engineering teams to deploy models at scale and build automation for training, inference, and observability.

Key Responsibilities

• Deploy ML models into production using CI/CD best practices.
• Monitor and manage ML drift; retrain models as needed.
• Build automation for ML lifecycle (training, inference, observability).
• Contribute to internal API development for future projects.
• Collaborate across teams to productionize models for client and internal use cases.

Must-Have Skills

• Strong experience with CI/CD (GitHub Actions, build automation, packaging).
• Expertise in MLOps (MLflow, model versioning, monitoring).
• Hands-on with Databricks (Delta Lake, Unity Catalog, Asset Bundles).
• Proficient in Python and PySpark.
• 3-6 years of relevant experience.

Nice-to-Have Skills

• Kubernetes and containerized environments.
• API development exposure.
• Distributed training (Ray) and observability tools (Prometheus, Grafana).
• Familiarity with embedding models and Databricks Clean Room.

Screening Process

1. Intro (with Director of ML or Senior Engineer) + short live coding exercise.
2. Take-home exercise (CI/CD-focused).
3. Final interview with leadership.
4. CEO check-in.

Interested?
Apply now to join a team building brand-new solutions in ad-tech and work with technologies like PyTorch, Ray, Databricks, and MLflow.

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Job Opportunity: Principal AI Engineer | Remote or Hybrid

Location: Remote or Hybrid (HQ in [City])

Role Type: Full-Time

Seniority: Principal / Expert

Start Date: ASAP

About the Company

The company is expanding its AI capabilities to build innovative, intelligent systems that power next-generation products and experiences. With an ambitious roadmap centered on generative AI, advanced machine learning, and large-scale automation, they are seeking a highly experienced Principal AI Engineer to lead technical strategy, guide system architecture, and drive innovation across the organization.

This is a pivotal role that will shape the long-term AI direction, influence key engineering decisions, and deliver solutions that have significant impact on the company’s technical evolution.

The Role

The Principal AI Engineer will act as a hands-on technical leader, responsible for designing, developing, and scaling advanced AI systems. The role involves high-level architectural thinking, applied research, and deep technical execution, including oversight of model development, evaluation, deployment, and optimization.

The successful candidate will collaborate with engineering, data, and product stakeholders to define the AI vision while also mentoring teams and setting engineering standards that enable excellence and innovation.

Key Responsibilities

• Lead the design and development of complex AI systems, including generative AI models, LLM-powered applications, and large-scale machine learning infrastructures.
• Define and drive the company’s AI technical strategy, ensuring alignment with business goals.
• Oversee model experimentation, fine-tuning, evaluation, and deployment into production environments.
• Set best practices for ML engineering, model lifecycle management, security, and governance.
• Provide expert guidance on architecture decisions across data, ML, and application layers.
• Collaborate with cross-functional teams to identify high-impact AI opportunities and translate them into technical solutions.
• Mentor and support engineering teams, elevating technical capabilities across the organization.
• Stay ahead of industry developments, research breakthroughs, and emerging AI frameworks.

Required Experience

• 7+ years of experience in AI/ML engineering, with a strong track record of delivering production-grade AI systems.
• Deep expertise in Python and modern ML frameworks (PyTorch, TensorFlow, JAX).
• Extensive hands-on experience working with large language models, generative AI architectures, and related tooling.
• Strong knowledge of model serving, orchestration, distributed training, and scalable inference systems.
• Experience architecting and deploying AI solutions on cloud platforms (AWS/GCP/Azure).
• Proven ability to lead complex AI initiatives, influence technical direction, and guide multidisciplinary teams.
• Strong understanding of data engineering, MLOps, model governance, and ML lifecycle best practices.
• Ability to thrive in a fast-paced environment and navigate ambiguous, greenfield challenges.

What the Company Offers

• A strategic role with significant influence over the AI roadmap and technical foundations.
• Competitive salary, equity, and comprehensive benefits.
• Flexible remote or hybrid working environment.
• Opportunities to work with state-of-the-art AI technologies and contribute to groundbreaking products.
• A culture that values innovation, autonomy, and continuous learning.

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Senior Machine Learning Engineer – MLOps

Series A Adtech

REMOTE

$190,000-210,000 base salary + equity

THE COMPANY

Harnham is partnering with a unicorn AI adtech that builds AI platforms which enhance performance by delivering real-time relevance at critical points in the customer journey. Using intelligent systems and a global digital commerce framework, the platform powers high-volume interactions between millions of users and major brands. Following a significant valuation increase in early 2025, they are expanding rapidly across international markets, particularly in AI. The AI engineering team builds scalable, data-driven solutions that personalize user experiences and equip marketers with actionable insights, all while working with large-scale infrastructure and diverse technologies.

THE ROLE

• 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

YOUR SKILLS AND EXPERIENCE

The successful Senior Machine Learning Engineer – MLOps will likely have the following skills and experience:

• 5+ years of commercial experience preferred with a focus on deploying and scaling machine learning and LLM models
• Experience working in a scaling startup is highly preferred (having seen multiple funding rounds ideally)
• 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; Databricks and GitHub Actions preferred
• History of working on models from concept to production / end-to-end / 0-1
• Experience in settings wearing multiple hats
• Domain experience in adtech or similar a plus
• History of partnering with non-technical stakeholders required
• Experience owning projects directly preferred
• BS or MS degree in Computer Science, Statistics, Applied Mathematics, Computer Engineering or similar

THE BENEFITS

A competitive base salary of $190,000-210,000 + benefits + equity

HOW TO APPLY

Please register your interest by sending your résumé to Tim Jonas via the Apply link on this page.

KEYWORDS

Machine Learning | AI | Artificial Intelligence | Technology | MLOps | LLMOps | Adtech | Advertising | Unicorn | Startup | Deployment | Production | LLMs | LLM | Large Language Models | GenAI | Gen AI | Generative AI | Distributed Systems | Low Latency | Real-time | Scale | Scalability | Infrastructure | Architecture | CI/CD | Continuous Integration | Continuous Deployment | Real Time Bidding | Auction | Recommendation Engine | Recommender System | Personalization | Bespoke | Target Audience

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Senior MLOps Engineer

Chicago, IL

$160,000-180,000 base salary + bonus

THE COMPANY

Harnham is partnering with an innovative health-tech startup building patient-focused agentic AI applications. The AI engineering team builds scalable, data-driven solutions that personalize user experiences and equip care providers with actionable insights, all while working with large-scale infrastructure and diverse technologies.

THE ROLE

• You will be responsible for machine learning model deployment and scalability for the company’s agentic AI product
• 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

YOUR SKILLS AND EXPERIENCE

The successful Senior MLOps Engineer will likely have the following skills and experience:

• 5+ years of commercial experience preferred with a focus on building and deploying machine learning and LLM models, ideally with voice or chatbot systems
• Experience working in a scaling startup is highly preferred
• 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
• Cloud experience in AWS, Azure or GCP
• DevOps experience with CI/CD pipelines required
• History of working on models from concept to production / end-to-end / 0-1
• Software engineering background preferred
• Experience in settings wearing multiple hats
• Domain experience in heathcare, health-tech, med-tech or similar a plus
• History of partnering with non-technical stakeholders required
• Experience owning projects directly preferred
• BS or MS degree in Computer Science, Statistics, Applied Mathematics, Computer Engineering or similar

THE BENEFITS

A competitive base salary of $160,000-180,000 + benefits + equity

HOW TO APPLY

Please register your interest by sending your résumé to Tim Jonas via the Apply link on this page.

KEYWORDS

Machine Learning | AI | Artificial Intelligence | Technology | MLOps | LLMOps | Health-tech | Healthcare | Medical | Startup | Deployment | Production | LLMs | LLM | Large Language Models | GenAI | Gen AI | Generative AI | Voice Bots | Chatbots | Natural Language Processing | Infrastructure | Architecture | CI/CD | Continuous Integration | Continuous Deployment | EHR | Electronic Health | Electronic Medical | HEOR | Claims Data | Patient

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Senior AI Scientist

London – (Hybrid, 3 days a week in office)

Up to £80,000 + benefits

About the Role

Our client is a data-led organisation that has made significant investment in modern data and AI capabilities. With a mature data platform and strong executive buy-in, they are now focused on embedding advanced AI into both customer-facing products and internal decision-making tools.

As a Senior AI Scientist, you’ll play a key role in designing, building and deploying applied AI systems, with a particular focus on large language models (LLMs), retrieval-augmented generation (RAG), and conversational AI. This is a hands-on role operating across the full AI lifecycle, from experimentation and prototyping through to production deployment.

You’ll work closely with product, engineering and business teams to ensure AI solutions are practical, scalable and deliver clear commercial and customer impact.

Key Responsibilities

• Leading the development of AI proof-of-concepts and production systems, including LLM-powered assistants and chatbots.
• Designing and implementing RAG pipelines using vector search, embeddings and knowledge retrieval strategies.
• Fine-tuning, evaluating and deploying language models, with a focus on response quality, reliability and performance.
• Building agentic workflows and conversational flows for both customer-facing and internal use cases.
• Defining evaluation frameworks and quality metrics for AI-generated outputs.
• Collaborating with product and engineering teams to integrate AI solutions into live systems.
• Contributing to AI architecture decisions and mentoring more junior engineers and data scientists.

Your work will focus on delivering high-impact AI solutions, including:

• Customer and internal chatbots powered by LLMs.
• Intelligent knowledge retrieval and question-answering systems.
• Voice and chat-based automation to reduce cost-to-serve.
• AI tools that improve customer experience, retention and operational efficiency.

What We’re Looking For

• Strong experience building applied AI or machine learning systems in production environments.
• Advanced Python skills and hands-on experience with modern AI tooling (e.g. LLM frameworks, vector databases, orchestration libraries).
• Practical experience with LLMs, RAG architectures, embeddings and transformers.
• Experience deploying models using cloud infrastructure and working with microservices or APIs.
• Familiarity with MLOps concepts such as monitoring, evaluation and continuous improvement.
• Strong communication skills and the ability to translate business problems into AI-driven solutions.
• A collaborative mindset and experience working with both technical and non-technical stakeholders.

If this role looks of interest, apply below.

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Machine Learning Engineer

London – (3 days a week in office)

Up to £90,000

About the Role

Our client is a data-driven organisation focused on delivering measurable operational and financial improvements across a range of industries. They combine deep technical expertise with real-world delivery to design, build and deploy machine learning solutions that create tangible business impact.

As a Machine Learning Engineer, you’ll work closely with Data Scientists, Data Engineers and delivery teams to productionise models and build robust, scalable ML systems within client environments. This role is highly hands-on, with a strong focus on deployment, performance and reliability.

Key Responsibilities

• Translating data science models into scalable, production-ready machine learning solutions.
• Designing and building end-to-end ML pipelines, from data ingestion to deployment and monitoring.
• Collaborating with data engineers on data architecture, pipelines and feature stores.
• Working closely with data scientists to productionise models and improve performance.
• Deploying, monitoring and maintaining machine learning models in live environments.
• Implementing testing, validation and monitoring frameworks to ensure model reliability and impact.

Your work will focus on delivering high-value ML solutions, including:

• Productionising optimisation and predictive models at scale.
• Building systems to anticipate and prevent operational downtime.
• Deploying churn and customer behaviour models into live decision-making systems.
• Enabling next-best-action and recommendation engines for commercial teams.
• Supporting geospatial and advanced analytical models with robust ML infrastructure.

What We’re Looking For

• 2+ years’ experience building and deploying production machine learning systems.
• A strong academic background (Bachelor’s degree 2:1 or above in a quantitative subject).
• Strong Python skills and experience with ML frameworks (e.g. scikit-learn, TensorFlow, PyTorch).
• Experience with model deployment, monitoring and ML pipelines (e.g. CI/CD, MLOps concepts).
• A collaborative mindset with strong communication skills and experience working in cross-functional teams.

If this role looks of interest, apply below.

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Job Opportunity: MLOps Engineer (Full-Lifecycle) | Early-Stage Startup

Location: Remote or Hybrid (HQ in [City])

Role Type: Full-Time

Start Date: ASAP

About the Company

The company is a small, fast-moving startup building [short description of product/mission]. With a lean, collaborative team focused on delivering real value through AI, they are seeking an experienced MLOps Engineer to own and optimize their end-to-end machine learning pipeline as they scale.

The Role

As the company’s first dedicated MLOps hire, the successful candidate will be responsible for establishing and maintaining the full MLOps lifecycle-from data ingestion and experimentation to deployment, monitoring, and continuous optimization. They will work closely with ML engineers and the product team to ensure models move into production smoothly, reliably, and efficiently.

Responsibilities

• Designing, building, and maintaining scalable ML pipelines (training, validation, deployment).
• Developing CI/CD workflows for ML models and data pipelines.
• Implementing model and data versioning, lineage tracking, and reproducibility best practices.
• Managing infrastructure for training and inference (cloud, containers, orchestration).
• Building monitoring systems for data drift, model performance, and operational reliability.
• Optimizing cost, performance, and automation across the ML lifecycle.
• Collaborating with ML engineers to transition prototypes into production-ready systems.
• Promoting engineering excellence, documentation, and observability.

Qualifications

• 3+ years of industry experience in MLOps, ML Engineering, or DevOps for ML systems.
• Proven experience managing the full ML lifecycle in production environments.
• Strong experience with cloud platforms (AWS/GCP/Azure) and infrastructure-as-code tools (Terraform, CloudFormation).
• Hands-on expertise with containerization and orchestration (Docker, Kubernetes).
• Familiarity with ML pipeline tools (Kubeflow, Airflow, MLflow, Vertex AI, SageMaker, etc.).
• Strong programming skills in Python and experience with CI/CD tools (GitHub Actions, GitLab CI, etc.).
• Understanding of monitoring, tracing, alerting, and model observability tools.
• A startup mindset: autonomous, resourceful, and comfortable building systems from scratch.

What the Company Offers

• High ownership and the opportunity to shape the ML infrastructure from the ground up.
• A collaborative team culture that values creativity, speed, and experimentation.
• Competitive salary + equity package.
• Flexible work environment.
• A chance to have significant impact on a product used by real customers.

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Machine Learning Engineer (Mid-Level) – Remote

Gaming Start-Up | Full-Time | Anywhere

Are you excited by the intersection of AI and gaming? Do you want to help shape the future of interactive entertainment at a fast-moving start-up? We’re looking for a Mid-Level Machine Learning Engineer to join our fully remote team and help us build intelligent, player-centric systems that power the next generation of gaming experiences.

About Them

They are an ambitious gaming start-up building innovative, data-driven gameplay systems. Our mission is to create games that feel alive-powered by adaptive AI, real-time insights, and scalable cloud technologies. As a small and collaborative team, every voice matters and every engineer has a direct impact on the product.

What You’ll Do

As a Machine Learning Engineer, you will:

• Design, build, and deploy ML models for gameplay, personalization, prediction, and player behavior analysis
• Collaborate with game designers, backend engineers, and data teams to integrate ML into live game systems
• Develop scalable ML pipelines using Google Cloud Platform (GCP) services (Vertex AI, BigQuery, Cloud Run, etc.)
• Work with real-time and batch data to support analytics and in-game decision systems
• Maintain and improve MLOps workflows for training, evaluation, deployment, and monitoring
• Contribute to a culture of experimentation, rapid iteration, and creative problem-solving

️ What They’re Looking For

Required:

• 2-4+ years of experience in Machine Learning or Data Science engineering roles
• Strong programming skills in Python
• Hands-on experience with GCP (Vertex AI, BigQuery, Dataflow, Cloud Storage, etc.)
• Experience building and deploying ML models in production
• Familiarity with modern ML frameworks (TensorFlow, PyTorch, scikit-learn)
• Understanding of data processing, pipelines, and MLOps best practices
• Ability to work independently in a remote environment and collaborate asynchronously

Nice to Have:

• Experience working in gaming or interactive media
• Knowledge of reinforcement learning, player modeling, or recommendation systems
• Experience with real-time systems or streaming data (e.g., Pub/Sub, Kafka)
• Passion for games and gameplay design#

Why You’ll Love Working With them

• Fully remote team – work from anywhere
• A chance to be an early team member in a rapidly growing gaming start-up
• High ownership, high impact, and opportunities to shape the direction of ML in our products
• Flexible hours, supportive culture, and a team that values creativity and innovation
• Competitive salary + equity package

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Senior Machine Learning Engineer

Location: Remote (with optional hybrid New York)
Salary: Salary up to $220,000

THE COMPANY
Our client is a fast-growing, profitable technology business operating at the cutting edge of AI-driven decisioning. They partner with global brands to deliver advanced machine learning solutions that optimize real-time strategies across digital platforms.

THE ROLE
As a Senior Machine Learning Engineer, you will play a key role in building scalable ML infrastructure and deploying production-grade models that directly impact business outcomes. This is an opportunity to work on distributed systems, cutting-edge neural network architectures, and MLOps best practices in a high-performance environment.

RESPONSIBILITIES

• You will architect and train neural network models for optimization and audience modeling using PyTorch and distributed training frameworks.
• You will build multi-GPU training pipelines and implement hyperparameter tuning strategies.
• You will develop feature engineering workflows using PySpark and embedding techniques.
• You will own the ML lifecycle from training to batch inference, ensuring automation and reproducibility.
• You will implement monitoring and observability solutions to track model performance and system health.
• Collaborate with cross-functional teams to align ML initiatives with product strategy and KPIs.

REQUIREMENTS

• MSc or PhD in Computer Science, Machine Learning, or related field.
• Strong ML engineering with strong proficiency in PyTorch.
• Hands-on experience with Databricks.
• Expert-level Python and PySpark skills for large-scale data processing.
• Familiarity with cloud platforms (AWS) and data warehousing (Snowflake).
• Strong understanding of CI/CD workflows and best practices.

BENEFITS

• Competitive salary and bonus structure.
• Comprehensive health coverage.
• Generous PTO and company holidays.
• Flexible remote working options.

HOW TO APPLY
If you’re passionate about building scalable ML systems and want to work in a dynamic, innovative environment, apply today to learn more.

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Senior Data Scientist – End-to-End Machine Learning & Behavioural Data

This organisation are seeking an experienced Senior Data Scientist to join their dynamic team. This role focuses on end-to-end productionising of machine learning models and leveraging behavioural data to drive actionable insights and innovative solutions.

The ideal candidate has a strong background in applied machine learning, including hands-on experience with Natural Language Processing (NLP), and is comfortable taking projects from concept through to production. They thrive in a collaborative environment, translating complex data into meaningful business outcomes and working closely with cross-functional teams to implement scalable solutions.

Key Responsibilities:

• Develop, deploy, and maintain end-to-end machine learning models, ensuring robust production performance.
• Analyse and derive insights from behavioural data to inform business decisions.
• Apply NLP techniques to extract value from text-based datasets.
• Collaborate with engineering and product teams to integrate models into live systems.
• Continuously monitor, evaluate, and optimise models to maintain accuracy, efficiency, and scalability.

Qualifications & Experience:

• Proven experience in end-to-end machine learning development and model deployment.
• Strong background in behavioural data analysis.
• Hands-on experience with NLP, including text preprocessing, embeddings, and relevant model architectures.
• Proficiency in Python and common ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Familiarity with cloud-based ML infrastructure and MLOps best practices.
• Excellent problem-solving skills and the ability to communicate complex findings to non-technical stakeholders.

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