4+ years of experience in ML or AI engineering, demonstrating proficiency in production ML systems.
Strong hands-on expertise with LLMs, prompt engineering, evals, and model routing.
Background in developing tooling and systems with tangible customer impact.
Pragmatic approach to trade-offs in engineering decisions, favoring timely delivery over perfection.
Ability to navigate and resolve scoped problems in ambiguous settings, delivering effective solutions.
Customer-focused mindset, prioritizing user impact in ML decision-making.
Collaborative spirit, elevating team performance through code reviews and effective communication.
Responsibilities
Build and lead the ML infrastructure for reliable and improvable AI systems, including evaluation frameworks and model observability.
Deliver customer-facing AI features regularly while concurrently enhancing foundational infrastructure.
Define and execute the evaluation methods, LLM routing strategies, prompt engineering standards, and model selection processes.
Develop practical standards that enhance quality without hindering team speed.
Proactively contribute to the ML technical direction by presenting architectural options and assessing trade-offs.
Benefits
Opportunity to shape the future of AI systems and influence technical direction.
Work in a collaborative environment focused on impact and customer value.
Engagement with real-world applications, enriching professional experience in high-stakes settings.
Potential for involvement in regulated industries, offering unique challenges and advancements in your skill set.
Full Job Description
Your mission is to
Build and own the ML infrastructure that makes our AI systems reliable and improvable, including eval frameworks, prompt management, and model observability
Ship customer-facing AI features on a consistent cadence, balancing new capability delivery with foundational infrastructure work
Define and implement the team's approach to evals, LLM routing, prompt engineering, and model selection
Build pragmatic standards that improve quality without slowing the team down
Contribute to ML technical direction by proactively surfacing trade-offs and architectural options, helping the team make informed decisions on where ML is headed
Who You Are
4+ years of experience in ML or AI engineering, with a track record of shipping production ML systems
Strong hands-on expertise with LLMs, prompt engineering, evals, and model routing
Experience building tooling and systems that have real customer impact
Pragmatic about tradeoffs: knows when good enough is the right call and avoids over-engineering; would rather ship something useful today than design something perfect next quarter
Comfortable working with moderate direction in ambiguous environments, you can take a scoped problem, work through it, and deliver a shipped solution
Builds with the end user in mind; understands how ML decisions impact real customers and prioritizes customer value over technical elegance
Elevates teammates through code review, pairing, and clear communication about technical decisions
Bonus Points If You
Have worked in regulated industries (fintech, banking, healthcare) where compliance and reliability are first-class concerns
Have experience with RAG systems, fine-tuning, or open-source LLM deployment alongside closed models
Are comfortable across the stack, data pipelines through APIs, and can plug gaps where needed
Have used or built prompt management or ML observability tooling
