ML platform Engineer

Capital Fund Management (CFM); Paris; Hybride; EUR 90 000 - 130 000; Plein temps

Résumé du poste

Capital Fund Management (CFM) seeks an ML Platform Engineer to join the ML Platform team, working with large-scale market data and building tools for ML workflows. You will be the primary ML Platform contact for a functional team, enabling their ML work and collaborating with the core platform group.

The role focuses on faster, safer ML workflows through Python-first tooling, production readiness, and occasional C++ collaboration.

Qualifications

• Must have strong Python engineering and testing practices.
• Experience running production services with reproducibility and monitoring.
• Fluency with containers and Linux environments.
• Experience with AWS and cloud workloads.
• Ability to read and debug C++ components when needed.
• Experience handling large-scale time-series data and evaluation pipelines.
• Comfort with iterative Agile delivery and clear ownership.
• Strong ability to communicate complex concepts to researchers and engineers.

Responsabilités

• Enable and accelerate a functional team working with full-scale market data across the ML lifecycle.
• Drive adoption of ML Platform tools through hands-on integration support and guidance.
• Shape ML Platform tooling based on user needs, validate with users, and ship changes.
• Establish standards for ML development: reproducibility, quality, auditability, and maintenance.
• Build self-service tooling to reduce platform dependencies.
• Improve production readiness of ML systems: CI/CD, environments, monitoring, and safe rollouts.
• Mentor junior team members and share knowledge via docs and office hours.
• Advocate best practices in ML software engineering across the company.

Connaissances

Python engineering

Production software experience

Linux/Containers

AWS cloud experience

C++ reading & debugging

Large-scale time series experience

Agile / iterative delivery

Communication across audiences

Outils

Docker

Kubernetes

CI/CD tooling

Monitoring tooling

Description du poste

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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 platform engineer within the ML Platform team, dedicated to teams working with large scale market data, i.e. a software engineer building tools for ML workflows.

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

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, and typical production challenges (reproducibility, CI/CD, lifecycle management, monitoring, incident/debug workflows).


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


• AWS experience, deploying and operating 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 working with large scale time series and understanding the common pitfalls in evaluation and deployment.


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


• 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 building and operating ML systems


• Experience as a Data scientist (useful for empathy with Research workflow and evaluation practices)


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


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


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

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.