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Eucalyptus

Senior Manager, ML Engineering

Eucalyptus is hiring a Senior Manager, ML Engineering. Level rates it ; you can apply on Eucalyptus.

Level's read on this job

  • Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
  • The posting mentions pgvector, RAG and fine tuning among its tools and skills.
  • Level classes it as a senior role.
  • Eucalyptus has 90 other open roles on Level.
  • Posted in the last few days.

AI in this role

pgvector
ragfine-tuning

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

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.

About the role (What you'll be doing)

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.

About you (Who you are)

You're an ML engineer who ships. You've built production systems that serve real users, and you know the difference between a notebook prototype and something that runs reliably at scale. You care about system design as much as model quality, and you're as comfortable debugging a latency issue in a retrieval pipeline as you are tuning a fine-tuned model.

  • 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?

Euc is also behind a growing family of digital healthcare clinics (Pilot, Kin, Software, Compound) across men’s health and well-being, fertility, skincare, and preventative health.

Here’s what makes joining Euc unique:

  • What’s next - Our goal for the next three years is to support 1 million patients globally to live better for longer. We’re launching into new conditions, demographics, and geographies as we build a truly preventive healthcare ecosystem.
  • Build something world-changing - We’re on the path to becoming the world’s largest international digital healthcare company. It will be challenging, fast-paced, and deeply rewarding.
  • 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.

What’s ahead in Australia?

  • Grow with us globally - Get mentorship from leaders across Australia, the UK, Germany, and Japan, collaborate with teams in the Philippines and South Africa, and explore new markets or travel internationally. Recognised on Hatch’s Hotlist as one of Australia’s top employers, we support early-career professionals to become future global leaders.
  • Shape your career - Access learning budgets, conferences, certifications, peer shadowing, and a global knowledge-sharing culture.
  • Work where well-being matters - Experience catered wellness talks, exercise classes, free barista coffees, funded social clubs, and quarterly rooftop parties.
  • Innovate with purpose - Use state-of-the-art tools and contribute to bold, impactful solutions in healthcare.
  • Support at every stage - Benefit from parental and miscarriage leave, health, professional development, and personal days, and our Employee Assistance Program.

At Eucalyptus, we value individuals from all backgrounds, experiences, and perspectives, and we embrace the unique qualities each person brings. When you apply, please let us know of any reasonable adjustments you may need during the interview process. We use AI-assisted tools across our business to help our teams work more efficiently, including within our recruitment process. These tools support our team — all hiring decisions are made by real people, and every application is reviewed by a member of our recruitment team.

How we rate this

Senior Manager, ML Engineering at Eucalyptus rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGFine-tuningpgvector

Also common in machine learning engineer jobs at this level

Pay

Typical machine learning engineer job
$197k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  3. What are the limits of pgvector that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: RAG, Fine-tuning, and pgvector. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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