Write production-quality code across the full stack, working across the entire software lifecycle from shaping features with stakeholders to deployment and optimization
Build new tools and products from scratch as part of the Labs team
prototype-hardening is a major part of this role, but you'll also own and ship your own projects end-to-end
Define the overall approach for how prototypes built by teams across BPI
often in tools like Replit
get evaluated, hardened, and moved into production (or don't)
Build and own the solutions that make this cycle repeatable: the mix of process, standards, tooling, and technical infrastructure that lets this work safely at scale
Establish evaluation, testing, and review frameworks for LLM-powered applications -including knowing when an AI-driven approach isn't the right one
Set technical direction and mentor other engineers and non-engineering builders as they work with LLM tools, helping them build prototypes that are easier to harden later
Work directly with stakeholders and end users to consult on their prototypes, shape what's worth investing in, and translate the best ideas into reliable, production-grade software

7+ years of experience in software development, building, launching, and maintaining full-stack applications
A strong foundation in software engineering: you write clean, well-tested code and care about architecture, security, and maintainability
Strong experience building production systems using Python/Django and React
Strong experience working with cloud infrastructure (AWS, GCP, or Azure), containers (Docker), and CI/CD pipelines
Experience with data tools like Snowflake/dbt is a significant plus
Plenty of experience with a wide variety of SDLCs, and a proven ability to influence process in pragmatic and non-dogmatic ways
Hands-on experience building with LLM APIs, agent frameworks, retrieval-augmented generation (RAG), and prompt engineering
you've shipped LLM-powered features or products, not just experimented
Product-focused thinking
you're comfortable in a room with stakeholders, shaping what to build, not just how to build it
A healthy skepticism about AI hype, grounded in practical concerns: you think carefully about evaluation and testing, data privacy and security, hallucination and reliability, and when not to use an LLM at all
Strong communication skills and the ability to work across a diverse set of collaborators, from engineers to non-technical end users

Staff Engineer – Labs

Key skills