Senior Applied AI/ML Engineer

Pivotal Health•Los Angeles, CA

2d•$180,000 - $230,000

About The Position

Pivotal Health is investing heavily in AI as a core strategic capability to transform healthcare operations. We are building intelligent systems that automate complex workflows, improve decision-making, and drive measurable business outcomes across the organization. As a Senior Applied AI/ML Engineer, you will design, build, and improve production AI systems that solve complex healthcare and operational challenges. You will work at the intersection of machine learning, software engineering, product development, and business operations to deliver scalable AI-powered solutions. This is an applied AI engineering role—not research in isolation. You will own systems end-to-end, from experimentation and model development through deployment, evaluation, monitoring, and continuous improvement. Your work will directly influence critical products, workflows, and business decisions across Pivotal Health. We are looking for product-oriented engineers who thrive in ambiguous environments, move quickly from ideas to execution, and are excited to build AI systems that create measurable impact. Whether your background is rooted in machine learning engineering, LLM applications, agentic systems, or intelligent workflow automation, success in this role requires strong technical judgment, a bias toward shipping, and a commitment to continuous improvement.

Requirements

5–8+ years of experience building software systems in production environments, with a strong track record of owning and delivering complex technical solutions.
Experience building and operating applied AI, machine learning, LLM-powered, or agentic systems in production.
Strong software engineering background, particularly with Python and backend systems that support production-scale AI applications.
Experience designing and interpreting experiments, evaluation frameworks, benchmarks, or performance measurement systems.
Proven ability to translate complex operational requirements and real-world data into reliable, scalable, and shipped products.
Understanding of production engineering practices, including testing, monitoring, observability, system reliability, and safe rollouts.
Product-oriented mindset with the ability to navigate ambiguity, make thoughtful technical decisions, and drive initiatives from concept through deployment.
Strong communication and collaboration skills, with the ability to partner effectively across engineering, product, data, and operational teams.

Nice To Haves

Experience building AI-powered products in healthcare, insurance, financial services, or another regulated industry.
Experience working with healthcare data, clinical documentation, insurance claims, or healthcare operational workflows.
Familiarity with explainable AI and systems where accuracy, transparency, fairness, and defensibility are important.
Experience with model fine-tuning, embedding models, vector databases, retrieval systems, or AI evaluation platforms.
Experience building document intelligence, decision-support, workflow automation, or human-in-the-loop AI systems at scale.

Responsibilities

Own and improve production AI systems. Design, build, deploy, and optimize AI/ML solutions that support critical healthcare products and operational workflows.
Build LLM-powered applications. Develop production systems involving large language models, retrieval, structured generation, tool use, orchestration, and agentic workflows.
Develop intelligent workflow automation. Create AI-powered systems that automate complex, multistep processes and improve the speed, consistency, and quality of operational decision-making.
Own solutions end-to-end. Take projects from problem definition and experimentation through development, deployment, monitoring, and ongoing optimization.
Design evaluation and experimentation frameworks. Create testing methodologies, benchmarks, and feedback loops that improve model quality, system performance, reliability, and business outcomes.
Translate business problems into scalable systems. Partner with product, operations, data, and engineering teams to define success metrics and turn complex requirements into production-ready AI solutions.
Optimize model and workflow behavior. Improve prompts, retrieval strategies, model selection, system logic, and application performance through continuous experimentation.
Build reliable AI infrastructure. Contribute to the architecture, observability, testing, versioning, rollout safety, and operational reliability of AI-powered systems.
Work with complex healthcare data. Build solutions that operate effectively across structured and unstructured information while maintaining high standards for accuracy, privacy, security, and compliance.
Accelerate AI adoption across the organization. Apply AI tools and best practices to improve team productivity and help establish scalable, AI-native ways of working.
Balance speed with rigor. Deliver impactful systems quickly while maintaining high standards for quality, reliability, safety, and maintainability.