Senior Applied AI/ML Engineer

Pivotal Health•Los Angeles, CA

About The Position

Pivotal Health is seeking a Senior Applied AI/ML Engineer to build and enhance AI systems that are integral to product and operational workflows. This role focuses on applied systems, bridging the gap between models, software, product, and business outcomes. The engineer will work on areas such as document generation, dynamic offer engines, and decisioning systems, with an emphasis on product orientation, rapid iteration, and measurable value. The ideal candidate will be AI-first in their approach, utilizing AI to accelerate their own work and foster an AI-native team culture. The position reports to the Head of AI/ML Engineering.

Requirements

Excited by applied AI and ML problems that sit close to product and business outcomes.
Strong technical skills, with a focus on shipping applied systems rather than research in isolation.
Ability to work across experimentation, implementation, debugging, and productionization.
Comfort with ambiguity and ability to move from rough opportunity to concrete execution quickly.
Product-oriented mindset, caring about system usefulness.
AI power user, actively using AI in day-to-day work with good judgment.
Experience operating in fast-moving environments with short feedback loops.
Candidates must be authorized to work in the United States without current or future employer sponsorship.

Nice To Haves

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.