ML Engineer - low/mid frequency trading

Paris, 75, FR

ABOUT CFM

Founded in 1991, we are a global quantitative and systematic asset management firm applying a scientific approach to finance to develop alternative investment strategies that create value for our clients.
We value innovation, dedication, collaboration, and the ability to make an impact. Together, we create a stimulating environment for talented and passionate experts in research, technology, and business to explore new ideas and challenge existing assumptions.

Your Role

This position is for an ML engineer within the ML Platform team, dedicated to teams working with large scale market data.

You will be the primary ML Platform contact for a functional team at CFM, dedicating a majority of your time to enabling that team’s ML work, and the remaining time to collaborating with the core ML Platform group.

The goal of the role is to make the default ML workflow faster and safer for large scale market data users by:

building and improving Python-first tooling and patterns,

ensuring solutions are production-ready (MLOps, reliability, monitoring),

and occasionally diving into C++ parts of the stack to debug issues, investigate performance bottlenecks, or contribute fixes in collaboration with owners.

This is an enablement role: success is measured by team productivity, fewer recurring failures, and adoption of shared patterns, not by isolated heroics.

Key Responsibilities

Enable and accelerate a functional team working with full scale market data by supporting their end-to-end ML lifecycle (data → training → evaluation → deployment).
Drive adoption of ML Platform tools and services through hands-on integration support, examples, and pragmatic guidance.
Guide the evolution of ML Platform tooling based on real user needs (identify friction, propose improvements, validate with users, help ship changes).
Establish and promote standards for ML development: reproducibility, quality, auditability, and maintainability (testing, versioning, documentation).
Build self-service tooling (libraries, templates, reference implementations, automation) to reduce dependency on the platform team.
Improve production readiness of ML systems: CI/CD, environment consistency, monitoring/alerting, incident readiness, and safe rollout practices.
Mentor junior team members as the team expands; teach by building (docs, examples, office hours, paired debugging).
Advocate for industry best practices in ML-related software engineering across the company.

Your Skills

We are looking for candidates with experience working on mid-to-low-frequency strategies and/or with strong exposure to trading environments.

We are looking for candidates with 7+ years of experience in finance and machine learning, including 3+ years working with production-grade ML.

Mandatory:

Technical:

Strong Python engineering skills and software development best practices (maintainable code, testing strategy, packaging, typing, profiling/performance awareness).

Experience building and operating software in production environments, including ML systems and typical MLOps challenges (reproducibility, CI/CD, model lifecycle, monitoring, incident/debug workflows).

Containers + Linux/UNIX fluency: ability to build/debug container images and troubleshoot runtime/environment issues.

AWS experience, deploying and operating ML workloads and supporting services in cloud environments.

C++ working knowledge: ability to read/debug/patch C++ components when needed and collaborate effectively with C++ owners (deep specialization not required, but you must be comfortable going there).

Experience applying ML techniques to large scale time series and understanding the common pitfalls in evaluation and deployment.

Work methodology:

Comfortable with iterative delivery: pragmatic Agile practices (small increments, fast feedback, clear ownership), not process for process’ sake.

Soft skills:

Ability to simplify and communicate technical concepts clearly to multiple audiences (researchers, engineers, leadership).

Strong product/platform mindset: keep a user-focused approach while avoiding short-term fixes that create long-term platform debt.

Ability to influence without authority: inspire and help teams adopt best practices through enablement, examples, and good defaults.

Prioritize overall team productivity and resilience via skill-sharing, documentation, and reusable building blocks.

Nice to have

Technical:

Experience as a Data Scientist (useful for empathy with research workflows and evaluation practices).

Experience with inference servers (e.g., Triton) or building production model-serving services (HTTP/gRPC, scaling, latency/throughput tradeoffs).

Platform design / software architecture experience (APIs, multi-tenant systems, shared libraries, backwards compatibility).

Experience with C++/Python interoperability (e.g., bindings) and performance profiling across language boundaries.

“Design thinking” applied to platform work: identifying user journeys, reducing cognitive load, making the right thing the easy thing.

If you don’t meet every requirement but believe you’d be a great fit, feel free to reach out to us.

EQUAL OPPORTUNITIES STATEMENT

We are continuously striving to be an equal opportunity employer and we prohibit any discrimination based on sex, disability, origin, sexual orientation, gender identity, age, race, or religion. We believe that our diversity, breadth of experience, and multiple points of view are among the leading factors in our success.
CFM is a signatory of the Women Empowerment Principles.