Lead Software Engineer - Bee AI

What You'll Do

Lead the design, build, and extension of production AI agent systems for Bee, creating more curated ways for members to meet within a distinct AI-driven product experience

Architect and evolve Python-based services and agent workflows using PydanticAI and GCP, with a strong focus on reliability, extensibility, and maintainability

Define and improve how we build agents end to end, including prompt management, context handling, response schemas, fallback logic, and versioning practices

Establish robust evaluation approaches for agent quality, including offline and online evaluations, experimentation frameworks, telemetry, and clear success criteria for agent behaviour

Build repeatable patterns for monitoring, debugging, and managing agents in production, including observability, performance analysis, and continuous improvement loops

Partner closely with Product, Design, and Data within the Bee AI team to turn ambiguous opportunities into shipped features, while collaborating effectively with adjacent teams where integration is required

Apply strong technical judgment to responsible AI development by assessing outputs for quality, bias, safety, and transparency, ensuring human oversight where it matters most

Act as a technical leader by setting a high bar for code quality, system design, and delivery, collaborating with purpose, taking ownership, and demonstrating an agile mindset in line with our values of Courage, Respect, and Excellence

About you

Typically requires 8–10 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.

You bring deep software engineering experience in production systems, with strong Python expertise and a track record of building scalable backend services

You have hands-on experience building or guiding AI and ML-powered product experiences, especially around AI agents, prompt design, context orchestration, and evaluation strategies

You are comfortable designing schema-validated LLM interactions, managing prompts and response structures, and creating deterministic fallbacks and safeguards for production use

You have experience with cloud infrastructure on GCP and know how to design systems with strong observability, resilience, and operational clarity

You are skilled at defining technical approaches for ambiguous, high-impact problems, using independent judgment while influencing across teams and creating alignment

You know how to build evaluation and feedback loops for AI systems, including instrumentation, experimentation, monitoring, and iterative improvement of agent behaviour over time

You collaborate effectively across disciplines, take ownership of outcomes, and adapt quickly as priorities evolve, demonstrating an agile mindset and working with purpose

You use AI thoughtfully in your own work to improve speed, quality, and learning, while ensuring responsible, inclusive, and human-centred application

Location

This role is based in Austin, and we ask that you’re within a commutable distance to this office, so that you’re able to come onsite regularly to collaborate across engineering teams.

We have a hybrid environment that requires you to be in the office Monday - Wednesday.

Please note: We are unable to offer Visa sponsorship at this time