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MiniMed

Develops automated insulin delivery systems

AI Engineering Manager

Engineering LeadershipPosted on Nov 12
Full-time

About the Job

Requirements

  • A bachelor's degree in Computer Science, Engineering, or a related technical field and at least 5 years of software or machine-learning engineering experience, including at least 2 years managing engineers through hiring, performance management, and career development; alternatively, an advanced degree and at least 3 years of that experience, including at least 1 year managing engineers.
  • Experience delivering large language model-powered or agent systems to production against enterprise data and systems, including evaluation, guardrails, and monitoring.
  • Hands-on architecture experience with large language model and agent systems, including retrieval-augmented generation, tool calling, and agent orchestration, with the depth to review designs and code and debug production issues.
  • Experience leading senior engineers and running agile delivery, including sprint planning, backlog prioritization, and predictable releases.
  • Ability to communicate clearly with non-technical stakeholders and turn ambiguous requests into buildable plans.

Responsibilities

  • Hire, onboard, and develop AI engineers; run performance and career conversations; and support Principal AI Engineers in setting the team's technical standards.
  • Turn the team's portion of the AI roadmap into a sequenced, predictable plan; run sprint planning and backlog prioritization; manage capacity across concurrent initiatives; and raise risks with leadership early.
  • Partner with AI product managers, the enterprise transformation team, and business leaders to choose what to build, size the effort, and determine when a prototype is ready for engineering hardening.
  • Report progress, risk, cost, and value to the Data Science & AI Lead and business stakeholders in clear terms.
  • Define production-readiness requirements for agents, including evaluation evidence, guardrails, human-in-the-loop measures where required, fallback behavior, monitoring, and an incident plan, and own the release gate for team deliverables.
  • Serve as the escalation point for production issues in the team's systems and help define the transition from build to steady-state operation, including ownership, service levels, and runbooks.
  • Define testing, documentation, versioning, and release practices with Principal AI Engineers, and ensure agents built by enablement engineers in the business follow the same standards.
  • Partner with AI governance and security on access controls, data flows, and prompt-injection safeguards; translate medical-device safety, privacy, and compliance requirements into engineering controls, including auditability, documentation, and evaluation records.
  • Review high-risk or cross-cutting designs and code before release.
  • Build proofs of concept and reference implementations on the internal AI platform, including Databricks, LangGraph/LangSmith, and enterprise agent platforms, to de-risk new patterns.
  • Triage and debug production issues alongside the team when a system is failing and the stakes are high.
  • Make AI coding and agent tools part of the team's standard workflow and use them in management work.

Desired Qualifications

  • Experience leading staff- or principal-level individual contributors.
  • Experience managing a blended team of employees, contractors, and external delivery partners.
  • Experience building or running MLOps/LLMOps practices, including CI/CD for agents, evaluation gates, observability, cost management, and incident management.
  • Experience working in a regulated industry such as medical devices, healthcare, or financial services, including HIPAA, healthcare privacy, validation, and audit requirements.
  • Familiarity with Databricks, LangGraph, LangSmith, MLflow, vector databases, and managed retrieval.
  • Experience taking traditional machine-learning models to production.
  • Experience with forward-deployed or embedded engineering inside business functions.

About the Company

MiniMed

Develops automated insulin delivery systems

MiniMed develops insulin delivery and automated insulin delivery systems, plus related sensors, pens, and software. Its products couple glucose sensors with pumps or smart pens to automatically adjust insulin or simplify dosing. It differentiates itself with an integrated ecosystem of devices and software and a large share of recurring revenue from sensors, consumables, software and services across more than 80 countries. Its goal is to be a leading global provider of insulin delivery and glucose monitoring solutions that help people with diabetes achieve better glycemic control.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Los Angeles, CA
Founded
1983

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