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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
Phare Health New York, New York, USA Office
New York, New York, United States
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