Faire

4 weeks ago

Senior Applied AI/ML Scientist - Compass

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 4 hours agoStatus: Live

Job Summary

Senior, hands-on AI/ML expert leading Compass, Faire’s context-aware retailer assistant. Own data strategy, evaluation, modeling, and end-to-end feature shipping across AI, ML, and backend, blending proprietary data with agentic AI and web search. Shape product direction from near-zero to shipped features, balancing speed with thoughtful architecture and guardrails.

Location: Waterloo, Toronto, New York, San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Seniority: Manager level

Key Responsibilities

•Own the science and technical north star for Compass’s agentic products and the retailer assistant today and future iterations, defining data strategy, agent+tool+context approaches, and metrics for agent quality.
•Ship retailer-assistant features end-to-end across the FLARE Python app, data plumbing, tool wrappers, and frontend surfaces using AI-native workflows.
•Translate ambiguous product bets into sequenced, de-risked tactical plans, identifying what to build now, what to defer, and the bets with highest impact and probability of success.
•Set and raise the bar for eval- and experiment-driven development, including offline eval suites, LLM-as-judge metrics, and surface-specific quality criteria.
•Make pragmatic engineering choices that ship now yet evolve over time, collaborating with architecture teams and interfacing with adjacent groups (Search, Personalization, Platform/FLARE).

Pay and Benefits

Salary: CAD 171,000 - 235,500 annually

Equity and Bonus:Equity

Perks:EquityHealth Insurance401kTrainingRemote WorkHybrid Work

Key Requirements

•5+ years of industry experience building and shipping production ML/AI systems with measurable business impact, including hands-on ownership of the applied-science side (data, evaluation, modeling, quality).
•Experience shipping agentic/LLM-powered features in a core production product with a deep grasp of agent design tradeoffs (eval strategy, latency/cost/quality, tool-calling vs. context preload, guardrails, failure containment).
•Strong applied ML/data science foundation with ability to derive product capabilities from data.
•Proven track record of shipping across multiple stacks (backend, data, and ideally frontend) with high quality; cross-stack fluency.
•AI-native approach: using AI coding tools and agent workflows as a force multiplier in day-to-day work.

Experience:5+ yearsAIMLAgentic AILLMData analytics

Skills:AutonomyCollaborationCommunicationProblem-solvingJudgment

Languages:English

Tech Stack:PythonLLMOpenAI Agents SDKSnowflakeFLAREData pipelinesBackendFrontend

Company Brief

Faire

Operates a wholesale marketplace connecting independent retailers with emerging and established brands, providing ordering, logistics, and payment solutions to help small businesses discover and stock unique products.

Industry: Online Marketplaces

Company Size: Enterprise (1,001+ employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Headquarters: San Francisco, United States

Founded: 2017

Website LinkedIn

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Faire

4 weeks ago

Senior Applied AI/ML Scientist - Compass

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 4 hours agoStatus: Live

Job Summary

Location: Waterloo, Toronto, New York, San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Seniority: Manager level

Key Responsibilities

•Own the science and technical north star for Compass’s agentic products and the retailer assistant today and future iterations, defining data strategy, agent+tool+context approaches, and metrics for agent quality.
•Ship retailer-assistant features end-to-end across the FLARE Python app, data plumbing, tool wrappers, and frontend surfaces using AI-native workflows.
•Translate ambiguous product bets into sequenced, de-risked tactical plans, identifying what to build now, what to defer, and the bets with highest impact and probability of success.
•Set and raise the bar for eval- and experiment-driven development, including offline eval suites, LLM-as-judge metrics, and surface-specific quality criteria.
•Make pragmatic engineering choices that ship now yet evolve over time, collaborating with architecture teams and interfacing with adjacent groups (Search, Personalization, Platform/FLARE).

Pay and Benefits

Salary: CAD 171,000 - 235,500 annually

Equity and Bonus:Equity

Perks:EquityHealth Insurance401kTrainingRemote WorkHybrid Work

Key Requirements

•5+ years of industry experience building and shipping production ML/AI systems with measurable business impact, including hands-on ownership of the applied-science side (data, evaluation, modeling, quality).
•Experience shipping agentic/LLM-powered features in a core production product with a deep grasp of agent design tradeoffs (eval strategy, latency/cost/quality, tool-calling vs. context preload, guardrails, failure containment).
•Strong applied ML/data science foundation with ability to derive product capabilities from data.
•Proven track record of shipping across multiple stacks (backend, data, and ideally frontend) with high quality; cross-stack fluency.
•AI-native approach: using AI coding tools and agent workflows as a force multiplier in day-to-day work.

Experience:5+ yearsAIMLAgentic AILLMData analytics

Skills:AutonomyCollaborationCommunicationProblem-solvingJudgment

Languages:English

Tech Stack:PythonLLMOpenAI Agents SDKSnowflakeFLAREData pipelinesBackendFrontend

Company Brief

Faire

Industry: Online Marketplaces

Company Size: Enterprise (1,001+ employees)

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Headquarters: San Francisco, United States

Founded: 2017

Website LinkedIn