Role overview
500M+ downloads. 80M+ monthly users. A decade of building – and we're still accelerating.
Flo is the world's #1 health & fitness app worldwide on a mission to build a better future for female health. Backed by a $200M investment led by General Atlantic, we became the first product of our kind to reach a $1B valuation in 2024 – and we're not slowing down.
With 7M paid subscribers and the highest-rated experience in the App Store's health category, we've spent 10 years earning trust at scale. Now, we're building the next generation of digital health – AI-powered, privacy-first, clinically backed – to help our users know their body better.
The job
We are looking for a Senior Software Engineer with deep expertise in AI/ML infrastructure to join our AI Platform team and help build the GenAI platform that powers every AI feature at Flo.
You will bridge core infrastructure, data engineering, and LLM development to deliver production-grade medical safety judges, fine-tuning pipelines, evaluation frameworks, and real-time personalisation. The team operates 60+ LLM-based evaluation judges, develops proprietary fine-tuned health models, and maintains active partnerships with Databricks, Google, OpenAI, Anthropic, and AWS.
What you'll work on
We support impact with meaningful reward. Here's what that looks like:
- Competitive salary and annual reviews
- Opportunity to participate in Flo's performance incentive scheme
- Paid holiday, sick leave, and female health leave
- Enhanced parental leave and pay for maternity, paternity, same-sex and adoptive parents
- Accelerated professional growth through world-changing work and learning support
- In-person collaboration and work in a hybrid model, with 3 days per week spent in the office
- 5-week fully paid sabbatical at 5-year Floversary
- Flo Premium for friends & family, plus more health, pension and wellbeing perks
What we're looking for
- LLM evaluation frameworks (judges, graders, calibration methodology) or fine-tuning techniques (LoRA, RLHF/DPO, model distillation)
- ML data engineering: synthetic data generation, evaluation dataset design, annotation pipelines
- Healthcare, regulated industry, or safety-critical AI systems experience
- Prompt optimisation frameworks (DSPy or similar), feature stores (Tecton)
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