We're driven by the belief that good health should last a lifetime, and that to get there we need to deliver the personalised, high quality and empathetic care that the traditional system cannot support.
You'll partner with our care teams of prescribers, nurses, health coaches, pharmacists and dietitians to support patients across preventative health, weight management and specific women's and men's health conditions.
We joined Hims & Hers so that you can do all of the above on a bigger scale, with more ambition, a stronger global brand and the resources to deliver care to patients in dozens of countries.
We're looking for people who appreciate and are excited by the enormity and uniqueness of this challenge, and want to work in the most ambitious, smart, fast moving and positive sum environment of their lives to transform how healthcare is delivered globally.
We hope that's you.
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. You'll partner closely with Product, Engineering, and the broader data team to ship reliable, high-impact ML systems at the core of the product.
In this role, you'll balance technical leadership with hands-on delivery — owning the architecture and engineering of production ML systems, while setting the standards that ensure they're performant, observable, and maintainable in the long run.
- 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.