Pivotal Health - Senior Applied AI/ML Engineer
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Requirements
• You are excited by applied AI and ML problems that sit close to product and business outcomes. • You are strong technically, but you are not looking for a research-only role detached from shipping. • You can work across experimentation, implementation, debugging, and productionization. • You are comfortable with ambiguity and can move from rough opportunity to concrete execution quickly. • You have a product-oriented mindset and care about whether a system is actually useful, not just whether it is technically interesting. • experience with applied ML, LLM systems, agent workflows, or decisioning systems in production • experience designing and interpreting experiments, evals, or optimization loops • experience in domains like marketplaces, pricing, ad tech, credit decisioning, lending, or revenue optimization • experience with retrieval, prompt engineering, structured generation, model routing, or tool-using agent workflows • experience with Python and backend systems that support production AI workflows • experience turning messy operational data and product requirements into shipped systems • experience with healthcare or other operationally complex industries
Responsibilities
• Own and improve applied AI and ML systems that influence important business workflows. • Build and refine production workflows around generation, decisioning, retrieval, evaluation, and orchestration. • Work on concrete systems such as position statement generation, offer engine improvements, open negotiation agents, and configurable rules or decisioning systems. • Design and run experiments that improve model quality, workflow quality, and business outcomes. • Partner with product, operations, and engineering teammates to define success metrics and translate messy real-world problems into buildable systems. • Improve prompt, model, and workflow behavior through tight feedback loops and practical iteration. • Contribute to the engineering quality of these systems, including instrumentation, testing, rollout safety, and operational visibility. • Use AI as a force multiplier in your own work and help the team move faster by bringing strong AI-native habits. • Balance speed and rigor in an environment where shipping matters and iteration is constant. • What Success Looks Like • In the first 6 to 12 months, strong outcomes in this role would include: • making clear improvements to position statement generation, offer engine behavior, open negotiation workflows, or related AI systems • designing and running high-value experiments with real product or business impact • reducing failure modes and increasing confidence in production AI workflows • helping create stronger feedback loops between model behavior, operational workflows, and business outcomes • becoming a trusted technical owner for an important applied AI surface • contributing to a team culture that is both highly practical and highly capable with AI • You like operating in fast-moving environments where the feedback loop between work and impact is short.
Benefits
• The work is applied and consequential. These systems matter to core company workflows. • You’ll have direct ownership over real AI surfaces, not just supporting infrastructure. • The team is operating in a fast-moving window where a strong engineer can materially change outcomes in a short amount of time. • The problems span models, prompts, systems, product, and operations, which makes the work unusually rich. • We care about shipping, not just novelty. • There is room for different strengths here, including stronger ML engineering profiles and stronger data science or optimization profiles, as long as you are excited to build. • Example Technical Environment • Our current environment includes technologies and patterns like: • Python-based services and internal ML tooling • FastAPI-backed product and platform services • model evaluation and experimentation workflows • LLM and agent systems grounded in structured internal data • event-driven and workflow-oriented operational systems • You do not need to match this stack perfectly, but you should be comfortable learning quickly and shipping in this kind of environment. • We’re a collaborative, low-ego team on a mission to make healthcare reimbursement fairer for providers. While we primarily hire around our core hubs–Los Angeles and New York–we remain open to exceptional talent outside those regions. Remote and hybrid flexibility varies by role and team, and is outlined in each job description. • If you’re excited by solving complex problems and making a real-world impact, we’d love to hear from you. • Competitive compensation, including equity • Full health, dental, and vision coverage • Retirement savings plan through 401(k) • Flexible time off • Opportunities for company-wide connection and events • Ready to Make an Impact? • We’re building something meaningful; and we want you on the team. • Bring your ideas, curiosity, and drive, and let’s transform healthcare reimbursement together. • Employment Information • Work Authorization • Candidates must be authorized to work in the United States without current or future employer sponsorship. • Equal Employment Opportunity
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