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AI Engineering Manager
USA-MiniMed Distribution Corp. 1018
6 hours ago
Full-time
On-site
New York City, New York, United States
We anticipate the application window for this opening will close on - 4 Dec 2026
About the Role
MiniMed is building a lean, high-leverage AI & Data Science team that designs, builds, and runs AI agents and models across the business. We are looking for a Manager, AI Engineering to lead our AI Agent Engineering team, including Principal AI Engineers, Senior AI Engineers, and contract engineers, and to turn a growing portfolio of AI use cases into reliable production systems. This is a player-coach role. Most of your time goes to leading the team and its delivery. You also stay close to the work, spending about a quarter of your time reviewing designs and code, prototyping, and working on hard production problems, so your technical decisions reflect how the systems actually work. Principal AI Engineers set the team's technical standards; you make sure the team has the people, plan, and quality bar to deliver against them. What success looks like
A strong, growing team. Engineers are hired well, developed, and retained, and Principal AI Engineers have room to lead technically. Predictable delivery. AI use cases move from approved idea to production on a plan the business can rely on. Reliable systems. What the team ships passes the readiness bar and holds up in production after handoff. This is a hybrid position based in Atlanta, GA or Northridge, CA A Day in the Life
Responsibilities may include the following and other duties may be assigned.
Lead the team and its delivery (about 45%)
Build and grow the team. Hire, onboard, and develop AI engineers; run performance and career conversations; give Principal AI Engineers the support and room to set the technical standard for the team. Run delivery. Turn the team's part of the AI roadmap into a sequenced, predictable plan; run sprint planning and backlog prioritization; manage capacity across concurrent initiatives; raise risks with leadership early. Work with product managers and the business. Partner with AI product managers, the enterprise transformation team, and business leaders to choose what to build, size the effort, and decide when a prototype is ready for engineering hardening. Represent the team. Report progress, risk, cost, and value to the Data Science & AI Lead and business stakeholders in clear, plain terms.
Own production readiness and standards (about 30%)
Set the production-readiness bar. Define what an agent needs before it ships: evaluation evidence, guardrails, human-in-the-loop where required, fallback behavior, monitoring, and an incident plan. You own this release gate for everything the team builds. Own escalation and shape the handoff. Serve as the escalation point for production issues in the team's systems. As our platform and operations functions take shape, help define how capabilities move from build to steady-state operation, including ownership, service levels, and runbooks. Set development standards. With the Principal AI Engineers, define testing, documentation, versioning, and release practices, and make sure agents built by enablement engineers inside the business follow the same standards. Build in security and compliance. Partner with AI governance and security on access controls, data flows, and prompt-injection safeguards. Turn the safety, privacy, and compliance requirements of a medical-device company into engineering controls, including auditability, documentation, and evaluation records.
Stay hands-on (about 25%)
Review designs and code. Review high-risk or cross-cutting work before it ships. Prototype selectively. Build proofs of concept and reference implementations on our internal AI platform (Databricks, LangGraph/LangSmith, and enterprise agent platforms) to de-risk new patterns, while critical-path delivery stays with the engineers who own it. Work the hard problems. Triage and debug production issues alongside the team when a system is failing and the stakes are high. Use AI in how the team works. Make AI coding and agent tools part of the team's standard workflow, and use them in your own management work.
Must Have: Minimum Requirements
Bachelor's degree in Computer Science, Engineering, or a related technical field and 5+ years of software/ML engineering experience, including 2+ years managing engineers (hiring, performance management, and career development). OR Advanced degree and 3+ years of the experience above, including 1+ years managing engineers. Delivered LLM-powered or agent systems to production against enterprise data and systems, including evaluation, guardrails, and monitoring. Hands-on architecture e
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MiniMed is building a lean, high-leverage AI & Data Science team that designs, builds, and runs AI agents and models across the business. We are looking for a Manager, AI Engineering to lead our AI Agent Engineering team, including Principal AI Engineers, Senior AI Engineers, and contract engineers, and to turn a growing portfolio of AI use cases into reliable production systems. This is a player-coach role. Most of your time goes to leading the team and its delivery. You also stay close to the work, spending about a quarter of your time reviewing designs and code, prototyping, and working on hard production problems, so your technical decisions reflect how the systems actually work. Principal AI Engineers set the team's technical standards; you make sure the team has the people, plan, and quality bar to deliver against them. What success looks like
A strong, growing team. Engineers are hired well, developed, and retained, and Principal AI Engineers have room to lead technically. Predictable delivery. AI use cases move from approved idea to production on a plan the business can rely on. Reliable systems. What the team ships passes the readiness bar and holds up in production after handoff. This is a hybrid position based in Atlanta, GA or Northridge, CA A Day in the Life
Responsibilities may include the following and other duties may be assigned.
Lead the team and its delivery (about 45%)
Build and grow the team. Hire, onboard, and develop AI engineers; run performance and career conversations; give Principal AI Engineers the support and room to set the technical standard for the team. Run delivery. Turn the team's part of the AI roadmap into a sequenced, predictable plan; run sprint planning and backlog prioritization; manage capacity across concurrent initiatives; raise risks with leadership early. Work with product managers and the business. Partner with AI product managers, the enterprise transformation team, and business leaders to choose what to build, size the effort, and decide when a prototype is ready for engineering hardening. Represent the team. Report progress, risk, cost, and value to the Data Science & AI Lead and business stakeholders in clear, plain terms.
Own production readiness and standards (about 30%)
Set the production-readiness bar. Define what an agent needs before it ships: evaluation evidence, guardrails, human-in-the-loop where required, fallback behavior, monitoring, and an incident plan. You own this release gate for everything the team builds. Own escalation and shape the handoff. Serve as the escalation point for production issues in the team's systems. As our platform and operations functions take shape, help define how capabilities move from build to steady-state operation, including ownership, service levels, and runbooks. Set development standards. With the Principal AI Engineers, define testing, documentation, versioning, and release practices, and make sure agents built by enablement engineers inside the business follow the same standards. Build in security and compliance. Partner with AI governance and security on access controls, data flows, and prompt-injection safeguards. Turn the safety, privacy, and compliance requirements of a medical-device company into engineering controls, including auditability, documentation, and evaluation records.
Stay hands-on (about 25%)
Review designs and code. Review high-risk or cross-cutting work before it ships. Prototype selectively. Build proofs of concept and reference implementations on our internal AI platform (Databricks, LangGraph/LangSmith, and enterprise agent platforms) to de-risk new patterns, while critical-path delivery stays with the engineers who own it. Work the hard problems. Triage and debug production issues alongside the team when a system is failing and the stakes are high. Use AI in how the team works. Make AI coding and agent tools part of the team's standard workflow, and use them in your own management work.
Must Have: Minimum Requirements
Bachelor's degree in Computer Science, Engineering, or a related technical field and 5+ years of software/ML engineering experience, including 2+ years managing engineers (hiring, performance management, and career development). OR Advanced degree and 3+ years of the experience above, including 1+ years managing engineers. Delivered LLM-powered or agent systems to production against enterprise data and systems, including evaluation, guardrails, and monitoring. Hands-on architecture e
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