Senior Machine Learning Engineer

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 looking for a Senior Machine Learning Engineer who loves solving real-world problems, takes initiative, and thrives in an early-stage environment. You’ll work closely with product, engineering, and operations to design, build, and productionize machine learning systems that directly power our platform and improve outcomes for our users. This role is ideal for someone who enjoys owning ML systems end to end—from data exploration and modeling through deployment, monitoring, and iteration in production. You’re curious by nature, comfortable operating in ambiguity, and unafraid to propose new approaches when something doesn’t feel quite right. You bring a low-ego, collaborative mindset, value feedback, and care more about impact and learning than rigid process or perfect abstractions. While this role is scoped at the Senior level, we’re open to candidates with broader experience and impact who may naturally operate at a higher level. We care most about ownership, judgment, and the ability to ship durable ML systems.

Requirements

Bachelor’s degree in Computer Science or a related field, or equivalent practical experience. Advanced degrees may be considered in lieu of some professional experience.
4+ years of experience building and shipping machine learning or data-driven systems in real-world environments.
Strong Python experience in production, including building services, pipelines, or model-backed APIs.
Solid understanding of machine learning fundamentals and how to apply them pragmatically to product problems.
Experience working with data stores and pipelines (SQL databases, data warehouses, or similar).
Comfortable collaborating in a modern cloud environment (AWS or GCP), including CI/CD and deployment workflows.
Able to operate independently, ask good questions, and make thoughtful tradeoffs in ambiguous situations.

Nice To Haves

Experience working in healthcare, fintech, payments, or other highly regulated domains, where correctness and auditability matter.
Experience with model monitoring, evaluation, and iteration post-deployment.
Experience in an early-stage or growth-stage startup environment.

Responsibilities

Ownership: Own machine learning systems end to end, from problem definition and modeling through production deployment and ongoing improvement.
Production ML: Build, deploy, and maintain production-grade ML systems in Python with a strong focus on reliability, observability, and maintainability.
Impact: Solve meaningful, real-world problems by applying machine learning in practical, scalable ways that directly support users and the business.
Innovation: Iterate based on real-world feedback and model performance, continuously improving systems after they’re live in production.
Collaboration: Partner closely with Product, Engineering, and Operations to align on goals, constraints, and success metrics.
Transparency: Make thoughtful tradeoffs in ambiguous situations, prioritizing clarity, simplicity, and long-term maintainability.
Culture: Contribute to a healthy, low-ego team environment that values empathy, growth, and mutual respect, while fostering collaboration, learning, and knowledge-sharing