Staff Engineer, AI Platform

Pivotal Health•Los Angeles, CA

About The Position

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. 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. Pivotal Health combines software, data, and service into a seamlessly integrated, AI-driven platform that simplifies these complex reimbursement workflows. 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. 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. This is a hands-on technical leadership role for someone who wants to build platform infrastructure, not just advise on it. 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. 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. 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.

Requirements

You are a strong software engineer first, with staff-level judgment and a track record of owning technical systems that multiple teams rely on.
You have deep experience building platform, infrastructure, or developer tooling, ideally in environments where reliability and usability both matter.
You have 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.

Nice To Haves

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 building internal developer platforms, workflow platforms, or reusable application scaffolding
experience in healthcare, payments, or other operationally complex domains where software quality directly affects business outcomes

Responsibilities

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.
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.
Improve how we compare prompts, providers, retrieval strategies, model versions, and workflow designs.
Build greenfield internal tooling that accelerates company-wide AI adoption, especially around MCP-style tools, app-builder patterns, and reusable internal AI primitives.
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.
Turn fragile or manual workflows into well-instrumented platform capabilities with good defaults and clear ownership.
Partner with engineers and product teams to identify the highest-leverage shared investments, then drive them from design through production.
Raise the technical bar through architecture work, design reviews, implementation, mentoring, and strong engineering judgment.