Senior ML Engineer — Real-Time Decisioning (Remote US) + Equity

Harnham; New York (NY); Remote; USD 180,000 - 200,000; Full time

Benefits offered by this job

Annual bonus / equity package

Fully remote in USA

4 day working week

Unlimited paid time off

Generous parental leave

Learning and development support

Job summary

Harnham is seeking a high-impact Machine Learning Engineer to build and scale production systems for real-time decisioning. You will join an AI Lab of 15 people and own end-to-end ML pipelines, from experimentation through deployment, delivering models that drive commercial outcomes at scale.

The role emphasizes distributed computing with Spark, Python and SQL, and close collaboration with Product, Engineering and Risk teams.

Qualifications

• Strong commercial experience building and deploying machine learning models from first principles.
• Hands-on experience working in distributed computing environments such as Spark.
• Proficiency in Python and SQL for data processing and model development.
• Experience designing and running experiments rather than relying on pre-built models or APIs.
• Ability to translate business problems into scalable machine learning solutions.

Responsibilities

• Designing and implementing machine learning models that drive real-time decisioning.
• Building and optimising scalable ML pipelines to enable rapid experimentation.
• Exploring and integrating new ML techniques to improve model accuracy and scalability.
• Collaborating closely with Product, Engineering, and Risk teams.
• Owning projects end to end, with full accountability for outcomes in production.

Skills

ML

Python

SQL

Spark

Experiment design

Business problem translation

Tools

Spark

Job description

Harnham is seeking a high-impact Machine Learning Engineer to build and scale production systems for real-time decisioning. You will join an AI Lab of 15 people and own end-to-end ML pipelines, from experimentation through deployment, delivering models that drive commercial outcomes at scale.

The role emphasizes distributed computing with Spark, Python and SQL, and close collaboration with Product, Engineering and Risk teams.