Senior Applied AI/ML Scientist - Compass
$211k - $291k • Remote • New York City, NY; San Francisco, CA
Posted 2mo ago
About the job
Faire is building the future of wholesale, connecting independent retailers with brands through its technology platform. The Compass team is developing a user-facing AI assistant that leverages proprietary data, agentic AI, and web search to help retailers make smarter buying decisions and take actions on their behalf. This role is a hands-on individual contributor position focused on driving agent quality through data, evaluation, and modeling, while also shipping product features end-to-end. You will work at the frontier of agentic AI, blending scientific rigor with cross-stack engineering to build the next generation of AI assistants.
Responsibilities
- Own the science and technical direction for Compass's agentic products, including data utilization, agent strategy, and quality measurement.
- Ship retailer-assistant features end-to-end across AI, ML, backend, and frontend components.
- Translate product ideas into actionable plans, prioritizing development based on impact and probability of success.
- Establish and improve evaluation and experimentation processes for agent quality.
- Make pragmatic engineering decisions for maintainable and scalable solutions.
- Collaborate with engineers on architecture and serving, and interface with adjacent teams.
- Enhance team's collective judgment through prototypes, analyses, and design reviews.
Requirements
- 5+ years of industry experience building and shipping production ML/AI systems with measurable business impact.
- Hands-on ownership of applied science aspects (data, evaluation, modeling, quality) in production ML/AI systems.
- Experience shipping agentic/LLM-powered features in a core production product.
- Deep understanding of agent design tradeoffs, including evaluation strategy, latency/cost/quality, tool-calling vs. context preload, guardrails, and failure containment.
- Strong applied ML/data science foundation, with experience reasoning from data, designing experiments/evals, and leveraging proprietary data.
- Proven track record of shipping quality features across multiple technology stacks (backend, data, frontend).
- Proficiency in using AI coding tools and agent workflows.
- Architectural maturity to design systems that are simple today but can evolve.
- High autonomy, resourcefulness, and good judgment.
- Sufficient engineering fluency to make sound architectural decisions.