Machine Learning & AI Engineer

About the Role

• You will work with leading companies across industries, helping them shape their hybrid cloud and AI journeys.
• This job can be performed from anywhere in the US.

Key Responsibilities

• Own a workstream end-to-end: taking an ambiguous ask, shaping it into a concrete design and plan, and delivering it with minimal guidance.
• Responsible for the quality and correctness of everything in your workstream.
• Begin to lift the engineers around you.
• Own a workstream: translate an ambiguous business need into a concrete technical approach, choose ML / AI methods and architecture, and defend technical implementation based on performance and trade-offs.
• Design for maintainability: make design decisions that hold up over time; define the tests, validation, and checks that keep your workstream correct as it evolves and guarantee quality.
• Design AI-accelerated workflows: decide where modern AI/LLM tooling fits (and where it doesn't) in your workstream, and set up those workflows for repeatable results.
• Mentor: guide 1–2 junior engineers — review their work, unblock them, raise their bar.
• Engage stakeholders: work directly with business/domain stakeholders to gather requirements and report progress.
• Interpret Data and Communicate Results: Clearly and effectively communicate the results of Machine Learning initiatives to stakeholders, providing actionable insights and recommendations.

Requirements & Tech Stack

• Strong software engineering: designs maintainable, well-architected solutions; sets and enforces testing/validation for own scope; fluent with version control workflows and reviews.
• End-to-end ML/AI delivery: has independently taken ML/AI work from ambiguous problem to delivered solution, including method & algorithm selection and performance/validation.
• Judgment under ambiguity: frames problems, weighs trade-offs, and makes appropriate decisions.
• Detail & execution: plans and sequences a workstream; manages its risks and dependencies to on-time delivery.
• Modern AI tooling: effective, deliberate use of LLM/agentic tools to accelerate a team's work.
• Emerging leadership: experience mentoring or reviewing others' work.
• Communication & bridging business and technology: engages business/domain stakeholders directly; turns their requirements into a technical approach and explains trade-offs back in terms they understand.

Qualifications

• Master's Degree