Staff AI/ML Engineer

Join Burq as a Staff AI/ML Engineer and play a crucial role in building the core AI technology for the company. This hands-on position involves designing and shipping production AI systems, automating manual operational work, and building models for decision-making. You will work closely with the founders, product, and engineering teams, making a significant impact on the company's technical direction. Ideal candidates have experience in shipping production AI/ML systems, strong programming skills, and a background in logistics or supply chain.

• 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 the models behind the decisions, including forecasting, prediction, matching/allocation, optimization, and reliability scoring.
• You design and trust your own evaluation — offline and online, tied to business outcomes, with safe rollout (e.g., shadow mode) and drift monitoring
• You've shipped production AI/ML, not just prototypes — and dealt with the real tradeoffs of edge cases, quality, latency, cost, and reliability
• You're deeply hands-on and ship fast — strong in Python, modern API/services (e.g., FastAPI), and sound ML-systems and architecture instincts
• You have real depth on at least one of these, and working fluency across both:
• We care more about evidence of work than pedigree or years of experience — but most strong candidates will have shipped production AI/ML systems at scale
• Generative / agentic AI — multi-agent orchestration, tool/function calling, RAG, structured outputs, and the modern stack (e.g., LangGraph/LangChain, MCP), across providers (Amazon Bedrock, Azure OpenAI, Anthropic, OpenAI)
• You've built for operationally complex or high-stakes environments where quality and reliability genuinely matter
• You communicate clearly, make decisions quickly, and can lead technical work without needing heavy process
• Applied ML / decision intelligence — forecasting, optimization, matching/allocation, ranking, or prediction models that drive operational decisions with measurable business impact
• Background in logistics, supply chain, transportation, marketplaces, mobility, or fulfillment
• Operations research / optimization, or reinforcement learning / bandits for sequential decision-making
• Multimodal / document understanding, computer-use, or browser automation
• Real-time / streaming systems, feature stores, and production MLOps at scale
• Patents or peer-reviewed publications, or experience as an early/founding engineer