AI Engineer

📋 Description

• • Join Starbridge as an AI Engineer and become the driving force behind our next-generation proposal-writing and search experiences. You will own the end-to-end lifecycle of AI features—from whiteboard sketches to production traffic—working shoulder-to-shoulder with product, design, and customer-success teams to turn cutting-edge research into revenue-generating products.
• • Architect and ship robust, scalable services that leverage OpenAI, Anthropic, and Gemini models. You will design prompt pipelines, implement retrieval-augmented generation (RAG) workflows, and fine-tune models to meet strict latency, cost, and quality SLAs for thousands of concurrent users.
• • Establish rigorous evaluation frameworks that measure model accuracy, hallucination rates, and user satisfaction. You will build automated test suites, run A/B experiments, and surface actionable insights that steer product strategy and justify roadmap decisions with hard data.
• • Champion MLOps best practices by integrating MLflow, DVC, and Weights & Biases into our CI/CD stack. You will containerize services, define infrastructure-as-code, and create reproducible training pipelines that allow any engineer to retrain and redeploy models in minutes, not days.
• • Continuously scan the horizon for breakthrough papers, open-source libraries, and emerging vendors. You will run spike projects, present findings to the entire company, and rapidly prototype improvements that keep Starbridge two steps ahead of competitors and customer expectations.
• • Collaborate in a fully remote, async-first culture where clear written communication is as important as clean code. You will author detailed RFCs, run blameless post-mortems, and mentor junior engineers through pair programming and thoughtful code reviews.
• • Influence the technical roadmap by translating customer pain points into ML opportunities. You will quantify ROI, scope MVPs, and iterate quickly—shipping behind feature flags, gathering telemetry, and doubling down only on what demonstrably moves the needle.
• • Uphold security and compliance standards for sensitive proposal data. You will implement PII redaction, encryption in transit and at rest, and audit trails that satisfy enterprise procurement requirements while preserving model performance.
• • Foster a culture of experimentation and psychological safety. You will celebrate failed hypotheses as learning opportunities, run internal hackathons, and create reusable templates that accelerate every team member’s ability to test bold ideas.