Staff Software Engineer - Kilo Code

About Anaconda

Be at the center of AI

Anaconda is built to advance AI with open source at scale, giving builders and organizations the confidence to increase productivity, and save time, spend and risk associated with open source. 95% of the Fortune 500 including Panasonic, AmTrust, Booz Allen Hamilton and over 50 million users rely on the value The Anaconda AI Platform delivers through a centralized approach to sourcing, securing, building, and deploying AI. With 21 billion downloads and growing, Anaconda has established itself as the gold standard for Python, data science, and AI and the enterprise-ready solution of choice for AI innovation. Anaconda is backed by world-class investors including Insight Partners. Learn more at .

Summary:

We're looking for a Staff Software Engineer to lead the evolution of Kilo Code, our flagship VS Code extension with over 5 million downloads processing more than 10 trillion tokens per month. You'll join a 15-person engineering team (average 10+ years of experience) working on one of the most-used AI-assisted coding tools in the world. You'll ship your first PR on day one and have hands-on ownership of high-impact features at the velocity we expect from everyone on this team. You'll set the technical direction that lets the team move faster on everything after, including performance optimization at scale, defining architectural patterns for advanced AI-assisted workflows, and elevating the team's engineering practices while maintaining the velocity and quality our users expect.

What You'll Do:

- Set technical direction for performance optimization initiatives across startup time, memory footprint, and CLI communication, establishing patterns and instrumentation that the team can leverage

- Design, build, and ship advanced features including granular auto-approval rules, context optimization, and Smart Apply workflows, then extract the patterns so the team can reuse them

- Drive cross-team initiatives that span the extension, backend services, and AI infrastructure, translating business priorities into technical roadmaps and breaking down complex problems into shippable increments

- Mentor engineers on the team through code review, architectural discussions, and pairing sessions, raising the bar for code quality, testing practices, and performance rigor

- Own end-to-end delivery of high-impact features from technical design through production deployment, including rollout strategy, telemetry design, and post-launch iteration based on user feedback

- Establish engineering standards for AI coding workflows including context window optimization, token compression strategies, and chat interface patterns that serve 6T+ tokens per month

- Identify and prioritize technical debt and performance bottlenecks proactively, balancing feature velocity with long-term system health on a mature codebase serving millions

- Partner with Product and Design to shape roadmap priorities, advocating for technical feasibility and incremental delivery while representing engineering constraints and opportunities

Your Impact Will Be Measured Through:

- Feature impact: user engagement and performance metrics for high-complexity features you own, including adoption rates, performance benchmarks, and production stability

- Delivery cadence: your first PR on day one, then a sustained pace of high-complexity features shipped to production, setting the tempo for the team

- Technical leverage: measurable improvements in team velocity, code quality, or system performance that compound over time through patterns, tooling, or mentorship you introduce

- Architectural decisions: adoption and success of technical approaches you champion, measured through feature reliability, performance gains, and team consensus

- Cross-functional influence: successful delivery of initiatives requiring coordination across Product, AI Services, and other engineering teams with clear outcomes and minimal friction

What You