Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Own the company’s financial model and reporting processes, build dashboards, conduct complex analyses, and provide strategic insights on unit economics, margins, patient growth, acquisition costs, provider capacity, and partnerships. The role partners cross-functionally with leadership in a fast-growing healthcare startup and requires strong financial modeling, communication, and analytical skills.

Build and maintain internal developer platforms, shared services, CI/CD pipelines, cloud infrastructure, observability systems, automation, and self-service tooling. Partner with engineering teams to improve development workflows, deployment reliability, operational efficiency, and developer productivity. Lead foundational technical projects from architecture through rollout and ongoing maintenance while balancing scalability, reliability, and developer experience.

Build and own backend and full-stack systems for partner integrations and patient access. Design scalable platform capabilities for multi-state healthcare programs, convert partner requirements into reusable systems, collaborate with product, data, operations, and clinical teams, and lead projects from architecture through rollout. The role also includes shaping technical direction and mentoring engineers.

Own the clinical platform foundation, including validated patient records, patient identity, service catalog, composition grammar, authoring tools, and external clinical partner integrations. Define the multi-year product vision and roadmap, create configurable healthcare workflows, version platform contracts, manage migrations and deprecations, and collaborate with clinical, partnerships, AI, engineering, and health-plan stakeholders to expand patient access at scale.

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.

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.

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.

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.