Staff AI/ML Engineer

Burq, Inc.

1d•Remote

About The Position

Burq is seeking a Staff AI/ML Engineer to play a core role in building the AI systems that power the company's logistics solutions. This is a high-ownership, hands-on role focused on two key areas: the agentic layer, which involves multi-agent systems and copilots to automate operational workflows, and the intelligence layer, which includes forecasting, prediction, and optimization models for real-time operational decisions. The engineer will be responsible for taking AI ideas from prototype to production, establishing standards for reliable AI in a demanding operational environment, and shaping the AI-native future of Burq. This role involves close collaboration with founders, product, and engineering teams, offering significant impact on the company's most critical technical work.

Requirements

Shipped production AI/ML systems at scale, dealing with tradeoffs of edge cases, quality, latency, cost, and reliability.
Deep expertise in at least one of the following, with working fluency across both: Generative/agentic AI (multi-agent orchestration, tool/function calling, RAG, structured outputs, modern stack like LangGraph/LangChain, MCP, across providers like Amazon Bedrock, Azure OpenAI, Anthropic, OpenAI) OR Applied ML/decision intelligence (forecasting, optimization, matching/allocation, ranking, or prediction models driving operational decisions).
Experience designing and implementing evaluation systems (offline and online) tied to business outcomes, with safe rollout and drift monitoring.
Deeply hands-on with strong Python skills, modern API/services (e.g., FastAPI), and sound ML-systems and architecture instincts.
Experience building for operationally complex or high-stakes environments where quality and reliability are critical.
Clear communication, ability to make decisions quickly, and lead technical work without heavy process.

Nice To Haves

Background in logistics, supply chain, transportation, marketplaces, mobility, or fulfillment.
Experience with operations research/optimization, or reinforcement learning/bandits for sequential decision-making.
Experience with multimodal/document understanding, computer-use, or browser automation.
Experience with real-time/streaming systems, feature stores, and production MLOps at scale.
Possession of patents or peer-reviewed publications, or experience as an early/founding engineer.

Responsibilities

Design and ship production AI systems, including multi-agent orchestration, routing, and specialized agents.
Automate manual operational work across onboarding, support, exceptions, and document/data understanding.
Build models for forecasting, prediction, matching/allocation, optimization, and reliability scoring.
Design and implement learning loops for continuous model improvement, including data and evaluation infrastructure.
Own reliability and evaluation, building eval harnesses, tracing, observability, and guardrails for complex AI workflows.
Make build-vs-rules decisions, determining the appropriate solution (model, agent, or rule) for specific problems.
Mentor engineers and advance the AI team's prototyping-to-production pipeline.