Senior ML Platform Engineer

Senior ML Platform Engineer

Location: London - hybrid, with three days per week in the office

An established quantitative research and trading business is looking for a Senior ML Platform Engineer to shape its research and machine learning infrastructure.

This newly created role offers the opportunity to improve how quantitative researchers build, test and deploy models. You’ll make the systems around their work faster, more reliable and easier to maintain, helping them spend more time on research.

The role

Working closely with quantitative research, development, infrastructure and data teams, you’ll own improvements from identifying the problem through to delivery, operation and measuring the results.

Your responsibilities will include:

• Designing and improving platform architecture across on-premises and cloud compute.
• Building shared tools that reduce repetitive engineering work for researchers.
• Profiling and optimising simulation, model training, validation and back-testing workloads.
• Improving CPU, memory and I/O performance, alongside effective use of parallel processing.
• Creating reliable routes from research notebooks to production, supported by testing, monitoring and reproducible environments.
• Evaluating and introducing technologies where they offer measurable benefits.
• Documenting technical decisions and sharing knowledge across teams.

About you

You’ll bring strong Python skills and a track record of making numerical or data-intensive systems measurably faster and more reliable.

You should also have:

• Experience designing and owning systems used by researchers, data scientists or analysts.
• Practical experience with profiling, vectorisation, parallelism and tools such as Cython or Numba.
• Sound architectural judgement, including knowing when to build, reuse or retain existing solutions.
• Strong production engineering practices, including testing, observability, continuous integration and reproducible environments.
• The confidence to define your own direction when requirements are open-ended.
• Clear communication skills and the ability to bring colleagues with you through technical changes.
• Experience with simulation or backtesting, ML pipelines, MLOps, quantitative finance, trading, Docker or Kubernetes would be useful.

Technology

The environment includes Python, Cython, pandas, NumPy, SciPy, PyArrow, Jupyter, Parquet, Snowflake, PostgreSQL, GitLab and Bash, alongside some legacy HDF5 systems.