Senior Machine Learning Engineer, ML Platform

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About the role

The Senior Machine Learning Engineer, ML Platform is responsible for building, running, and evolving the ML monorepo used by the X-ray, CT, and MRI machine learning group at DeepHealth. The monorepo covers the full path from experimentation to product: dataset and sample definitions, training and evaluation code, experiment tracking, model export, and the inference servers that run in production. Everyone on the team contributes to the monorepo; this role is the single point of accountability for it. This hands-on role owns the architecture, tooling, and engineering standards of that codebase for a distributed team of about twenty machine learning engineers and scientists, working with model developers and software engineers to make the team’s work fast, better, and easier to audit. It also leads the team's agentic coding strategy, shaping how AI coding agents are used across the codebase. The role reports to the Leader of the ML for X-ray/CT/MRI group. Essential Duties and Responsibilities • Own the architecture and roadmap of the ML Monorepo: • Module boundaries, shared abstractions for datasets and samples, training and evaluation loops, configuration, and experiment tracking. • Build and run the engineering tooling around the codebase: • Continuous integration, testing, dependency management, containers, artifact and model registries, and GPU job scheduling on cloud (GCP) and HPC infrastructure. • Own the path from trained checkpoint to deployable model: • Model packaging, inference servers with enforced API contracts, reproducibility, and performance and memory budgets on target hardware. • Define and enforce engineering standards (code review, testing, documentation) in a way that accelerates research rather than slowing it down. • Ensure traceability of data, code, and model versions across experiments and releases. • Investigate and resolve performance, reliability, and usability issues in the stack raised by the team and reduce their recurrence through shared tooling. • Drive the team's agentic coding strategy: define how AI coding agents are used in the monorepo, set up the tooling, guardrails, and conventions that make them effective, and help colleagues adopt them. • Review code, mentor colleagues on engineering practices, and communicate technical decisions and trade-offs clearly. PLEASE NOTE: This is not an exhaustive list of all duties, responsibilities and requirements of the position described above. Other functions may be assigned and management retains the right to add or change duties at any time. Minimum Qualifications, Education and Experience • Master's degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience (required). • 5+ years of software engineering experience, including a significant portion building infrastructure or tooling used by machine learning teams, such as an ML platform or a training framework (required). • Strong Python skills and software engineering fundamentals: API design, testing, refactoring, and attention to developer experience (required). • Solid PyTorch knowledge and practical experience with model training and profiling (required). • Experience running ML workloads on a cloud platform, ideally GCP (Vertex AI, Artifact Registry, IAM, storage), and with containerization (required). • Experience taking models to production: model packaging, inference serving, and performance and reliability constraints (required). • Ability to work closely with researchers, translate recurring pain points into shared tooling, and push back constructively when needed (required). • Ability to work autonomously in a distributed, multi-site team, with clear verbal and written communication in English (required). • Medical imaging

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