4+ years of ML or AI engineering experience with a focus on production systems
Strong knowledge of LLMs, prompt engineering, evals, and model routing
Proven ability to build impactful tooling and systems for customers
Pragmatic approach to engineering trade-offs, prioritizing timely delivery
Ability to work in ambiguous environments with minimal direction
User-focused mindset, ensuring ML decisions benefit real customers
Strong team player with a focus on collaboration through code review and communication
Responsibilities
Build and maintain ML infrastructure for reliable and improvable AI systems
Deliver customer-facing AI features regularly while managing foundational work
Define the team's strategies for evals, routing, prompt engineering, and model selection
Establish standards that enhance quality without hindering progress
Guide ML technical direction by analyzing trade-offs and architectural choices
Benefits
Flexible work environment that supports remote collaboration
Opportunities for professional development and continued learning
Emphasis on team collaboration and knowledge sharing
Impactful work that directly influences customer satisfaction
Access to cutting-edge ML and AI technologies
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
