engineering
Posted Apr 23Staff Engineer, AI Platform
Santa Monica, United StatesHybrid
Responsibilities
- - Build the observability layer for AI systems, including tracing, prompt and model version visibility, tool-call telemetry, cost tracking, latency, failure modes, and fallback behavior.
- - Create practical dashboards, alerts, and operational workflows that let us catch regressions before the business feels them.
- - Lead the design of our experimentation and evaluation platform for model-backed and agent-backed systems.
- - Build greenfield internal tooling that accelerates company-wide AI adoption, especially around MCP-style tools, app-builder patterns, and reusable internal AI primitives.
Requirements
- Pivotal Health combines software, data, and service into a seamlessly integrated, AI-driven platform that simplifies these complex reimbursement workflows.
- ABOUT THE ROLE We’re hiring a Staff Engineer, AI Platform to build the shared engineering foundations that make AI systems at Pivotal reliable, observable, and easy to adopt across the company.
- You’ll own key parts of our AI platform surface, including our Agent SDK, AI observability stack, experimentation and evaluation systems, and greenfield internal tooling for MCPs and AI-powered app builders.
- You’ll report to the Head of AI/ML Engineering and work closely with applied AI, backend, data, product, and operations partners.
- Just as important, this role is about helping Pivotal become an AI-first organization.
- We want someone who is personally excellent at using AI in their own day-to-day work and can turn that instinct into shared workflows, tooling, and habits that raise the company’s overall capability.
- - Help define what great AI-enabled work looks like inside the company by modeling strong usage patterns and turning them into scalable defaults for other teams.
- WHAT SUCCESS LOOKS LIKE In the first 6 to 12 months, strong outcomes in this role would include: - a clearly adopted internal Agent SDK with defaults, documentation, and developer ergonomics - standardized tracing and observability across AI workflows - a lightweight but real release process for model and prompt changes - stronger experiment and regression tooling for both statistical ML systems and agent workflows - less notebook- and runbook-driven operational work - new internal platform capabilities -
- experience with AI or ML platform problems such as agent runtimes, LLM tooling, inference infrastructure, experimentation systems, evaluation frameworks, or model observability. - You know how to design APIs, SDKs, and reusable abstractions that improve developer velocity without hiding important complexity. - You are an AI power user yourself.
- You actively use AI to accelerate engineering work, investigation, debugging, design, and knowledge work, and you have strong judgment about where it helps and where it does not. - You are comfortable operating in ambiguous, greenfield areas and can make pragmatic scope decisions without overbuilding. - You can work cross-functionally and lead through influence, clarity, and execution rather than title alone. - You enjoy being close to the code and architecture, even when operating at broad technical scope.
- WE’D BE ESPECIALLY EXCITED IF YOU HAVE - strong Python experience in production systems -
- experience with FastAPI or similar backend frameworks -
- experience with GCP or comparable cloud infrastructure -
- experience with LLM platforms, agent frameworks, or prompt/version management systems -
- experience with experimentation, evals, A/B testing, or statistical decision systems -
- experience with observability stacks such as OpenTelemetry, Langfuse, LangSmith, Braintrust, Helicone, Datadog, Grafana, or similar tooling -
- experience in healthcare, payments, or other operationally complex domains where software quality directly affects business outcomes WHY THIS ROLE IS INTERESTING - The problems are real, not speculative.
- You’ll be working on AI systems that sit in important operational workflows, not side experiments. - The scope is unusually high leverage.
- Part of the job is making Pivotal much better at using AI across functions, not just building backend systems in isolation. - You can stay deeply technical.
Benefits
- ABOUT PIVOTAL HEALTH Pivotal Health is the leading technology platform that helps healthcare providers get paid fairly in an increasingly complex reimbursement landscape.
- Today, many providers face persistent underpayment from health insurance companies, despite delivering high-quality care.
- Benefits Include: - 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.
- Equal Employment Opportunity Pivotal Health is an Equal Opportunity Employer.
Additional details
- While processes like IDR (Independent Dispute Resolution) were designed to promote fairness, they’re often administrative-heavy, time-consuming, and difficult to navigate without the right tools.
- We help providers efficiently dispute underpaid claims, reduce administrative burden, and recover the reimbursement they’re entitled to; without adding more work to already stretched teams.
- Our full-service IDR solution is just the starting point.
- We’re building solutions that enable providers to operate with clarity, control, and confidence across the reimbursement journey.
- This is a hands-on technical leadership role for someone who wants to build platform infrastructure, not just advise on it.
- This role is intentionally designed as a builder-first staff role.
- Over time, you may help shape and guide a team around these systems, but success in the role does not depend on moving into people management.
- We see technical leadership and long-term IC growth as first-class paths.
- WHAT YOU’LL DO - Own the evolution of our shared Agent SDK and adjacent developer-facing libraries.
- - Define the default engineering patterns for agent runtime behavior, tool use, structured outputs, context management, retries, testing, and deployment.