Job Specifications
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
The ML Platform team is focused on enhancing researcher productivity in training machine learning (ML) models by developing libraries, services, and best practices, and ensuring robust ML production at scale.
The aim is to provide a seamless path from experimentation to production that integrates with existing systems and boosts efficiency for model deployment.
The team mandate consists of addressing pain points, advancing organizational ML capabilities, and leveraging platform engineering with a modular, self-service approach.
It balances immediate user needs with sustainable technical decisions, emphasizing best practices and automation.
The team is composed of profiles working on cross-functional projects as well as engineers supporting functional teams in their ML journey.
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.
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.
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 building and operating ML systems
Experience as a Data scientist (use