Top Tech Jobs & Startup Jobs

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Build and maintain production ML systems: training and inference pipelines, model packaging and serving, feature pipelines, monitoring for drift and degradation, automated retraining, and reproducibility/versioning across data and models.

The Technical Talent Sourcer will build candidate pipelines for technical roles, collaborate with recruiters, manage interview scheduling, and maintain records in ATS.

Convert ambiguous healthcare and operational problems into well-scoped ML/AI, ranking, optimization, NLP, or LLM tasks; build and evaluate models, run error analysis, design honest evaluations, partner with engineering and clinical stakeholders, and productionize reliable models with clear communication of tradeoffs and limitations.

Own and build canonical data models and metric definitions using dbt and cloud warehouses. Create tests, documentation, lineage, and data quality checks; reconcile metrics across teams and payer customers; partner with analysts, data scientists, product, engineering, and finance; optimize warehouse performance, cost, and PHI-aware access patterns.

Drive company-wide AI adoption by embedding with cross-functional teams to identify use cases and build production AI workflows, agents, internal tools, prompt/skill libraries, evaluation frameworks, and safe deployment patterns. Provide training, office hours, and enablement while partnering with security, IT, legal, and clinical stakeholders to ensure PHI-safe guardrails and measure adoption, productivity, and quality improvements.

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.