Our mission is to make healthcare reimbursement transparent and fair (/phare), so providers can spend more time caring for patients and less time haggling over costs. We specifically focus on the most complex AI challenges that require novel R&D, with a team that blends AI researchers and engineers with clinicians, and payment experts. Backed by top healthcare investors including General Catalyst, we’re scaling quickly - join us!
The Role
You’ll own the architecture, training, and optimization of large-scale transformer-based pipelines, wrangling PyTorch, GPUs, and distributed infrastructure to push forward SOTA. Think “research lab rigor” fused with “startup shipping speed.” Expect to:
Design and iterate on new transformer and hybrid text architectures; scale promising ideas across multi-GPU / multi-node clusters with PyTorch.
Drive the research roadmap: propose experiments, benchmark against state of the art, and publish or open-source meaningful advances.
Build retrieval-augmented generation (RAG) pipelines and lightweight agent workflows, balancing accuracy, latency, and cost.
Convert research prototypes into reliable services with CI/CD, monitoring, and rollback.
Partner with product and design to translate model capabilities into intuitive user experiences.
3+ years training large transformer models in Python/PyTorch at scale.
Peer-reviewed publications or significant open-source work in text modelling.
Proven end-to-end ownership: architecture → distributed training → deployment.Fluency with Lightning/FSDP, Pytorch, Hugging Face, WandB, Ray/Kubeflow, Docker
Deep expertise in text modelling; clinical-text knowledge not required.
Bonus points
RLHF or policy-optimisation methods (PPO, TRPO, DPO).
Familiarity with healthcare ontologies or claims data.
Top-of-market compensation ($150-220k salary depending on seniority + equity)
Flexible PTO & hybrid culture (SoHo HQ 3 days/wk; exceptional remote considered)
Generous vacation policy, bonus birthday day-off, work from anywhere 1-month per year
Mission-driven, collaborative team with twice-a-year offsites and regular outings
Twice-yearly team off sites to align, build, and celebrate
401(k) retirement plan with contribution match up to 3%
ICHRA flexible health insurance plan to meet individuals' needs
Hiring Process
Initial application.
Intro call: Discuss your background, career goals, and our mission.
2 x Technical interviews: A programming or system design exercise focused on real-world data challenges.
Referees: We ask for 2 referees who can speak to your professional/technical work
Culture interview: Ways of working, and a chance to ask questions
Offer
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What We Do
Phare Health is bringing hospital administration and revenue cycle management into the large language model era. The firm develops AI technology for the back office to address the systemic challenges of healthcare finance and, by doing so, lead the industry in improving outcomes for hospitals, clinicians, and patients. Phare Health is backed by some of the best healthcare systems and investors in the world, with a founding team from DeepMind, Google and NYU.
