Staff AI/ML Engineer, Burq

• 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
• You've shipped production AI/ML, not just prototypes — and dealt with the real tradeoffs of edge cases, quality, latency, cost, and reliability
• You have real depth on at least one of these, and working fluency across both:
• 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)
• Applied ML / decision intelligence — forecasting, optimization, matching/allocation, ranking, or prediction models that drive operational decisions with measurable business impact
• 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're deeply hands-on and ship fast — strong in Python, modern API/services (e.g., FastAPI), and sound ML-systems and architecture instincts
• 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

Desirable

• We're hiring a Staff AI/ML Engineer to help build the AI at the core of Burq — and we mean core, not cosmetic. We're pushing to be at the forefront of AI in logistics: building intelligent agents that automate work the industry still does by hand, and the prediction and optimization models that power smarter operational decisions in real time
• This is a high-ownership, hands-on role that spans two connected layers:
• The agentic layer — multi-agent systems and copilots that understand a request and execute the right workflow end to end, automating manual operational work
• The intelligence layer — forecasting, prediction, and optimization models that make and improve the decisions behind the product, getting smarter with every outcome
• You'll take ambiguous AI ideas from prototype to production, set the bar for what reliable AI looks like in an operationally demanding domain, and help define what the AI-native version of Burq becomes. You'll work closely with the founders, product, and engineering, with a high impact on the most important technical work in the company
• Design and ship production AI systems — multi-agent orchestration, routing, and specialized agents that take a request and carry it through to a reliable outcome
• Automate manual operational work across onboarding, support, exceptions, and document/data understanding — turning processes that take hours or days into seconds
• Build the models behind the decisions — forecasting, prediction, matching/allocation, optimization, and reliability scoring that ground the product in data instead of guesswork, exposed as services the agent layer can call
• Design the learning loop. Instrument decisions and their outcomes so models continuously improve, with the data and evaluation infrastructure to support it
• Own reliability and evaluation. Build the eval harnesses, tracing, observability, and guardrails for complex AI workflows where mistakes carry real operational and financial consequences — and prove a model or agent beats the status quo before it ships
• Make the build-vs-rules calls — know when a model genuinely wins, when an agent is the right tool, and when a simple rule is the smarter answer
• Raise the bar and help the team grow — push our prototyping-to-production pipeline forward and mentor engineers as the AI team scales