Senior Manager, ML Engineering

We're the world's leading consumer healthcare platform, supporting millions of patients across the US, Australia, the UK, Germany, Japan, Spain, and Canada.

As the ML Engineering Lead at Eucalyptus, you'll lead the design and delivery of production ML systems that power our clinical and product experiences , from patient context management and personalisation, to AI-native features across our five clinics.

Responsibilities:

• Deliver production ML systems: Own the end-to-end design, build, and deployment of ML-powered features , retrieval systems, fine-tuned models, inference pipelines, and online and offline evaluation frameworks , that run reliably in production.
• Lead our RAG and retrieval architecture: Design and evolve our retrieval-augmented generation systems, including vector stores (pgvector), embedding pipelines, chunking strategies, and retrieval evaluation , ensuring quality and latency are production-grade.
• Drive model fine-tuning and evaluation: Define our approach to fine-tuning foundation models on clinical and behavioural data, with rigorous evals that gate production deployments , covering both offline benchmarks and online runtime evaluation.
• Own the ML systems stack: Set engineering standards across the stack , Postgres/pgvector, model serving, feature pipelines, and inference infrastructure , ensuring systems are observable, scalable, and maintainable.
• Collaborate cross-functionally: Partner with Product, Engineering, and Growth to translate ambiguous clinical and product problems into well-scoped ML system designs with clear success criteria.
• Mentor the team: Build ML engineering capability across the team through code review, architecture feedback, and technical guidance.

About you:

• 10+ years of experience in ML engineering or applied ML, with at least 2 years leading technical work in a production environment.
• Proven track record of shipping ML-powered features to production , not just building models, but owning the full system from data pipeline to serving layer.
• Deep expertise in RAG and retrieval systems: hands-on experience with vector databases (pgvector strongly preferred), embedding pipelines, entity extraction, retrieval strategies, and evaluation frameworks for both retrieval and generation pipelines.
• Strong in model fine-tuning: experience fine-tuning LLMs or other foundation models on domain-specific data, including dataset construction, training infrastructure, and evaluation design.
• Runtime evaluation as a discipline: you treat evals as a first-class engineering concern , designing online eval frameworks, defining quality metrics, and building feedback loops that gate production deployments.
• Production ML systems engineering: strong fundamentals in Postgres, pgvector, Python, and model serving; experience with cloud ML infrastructure (preferably GCP); familiarity with feature pipelines and inference optimisation.
• Leadership experience building and mentoring high-performing ML engineering teams, with the ability to set technical direction and hold a high bar on system quality.
• Excellent communication , able to translate complex ML system trade-offs into clear decisions for product and engineering partners.

Why join Eucalyptus?

• What’s next - Our goal for the next three years is to support 1 million patients globally to live better for longer.
• Build something world-changing - We’re on the path to becoming the world’s largest international digital healthcare company.
• Make real impact - You will deliver work that directly shapes patient outcomes and scales evidence-based care across markets.
• Accelerate your growth - You will have high ownership, continuous feedback, and dedicated development support.
• Join a motivated team - You will collaborate with talented peers to solve complex clinical and operational problems at scale.