Driver

Backend Engineer

• Contribute to building an efficient and scalable backend data model.
• Build and maintain critical backend integrations (e.g., VCS providers).
• Build and maintain the backend web server.
• Design, build, and maintain internal APIs for our web application.
• Build and maintain backend APIs for our Model Context Protocol (MCP) products.
• Bring strong architectural instincts to the team.
• Embrace AI-assisted development.
• Communicate effectively with team members and across key team interfaces.

Software Engineer – Backend

DevOps Engineer

• Design, deploy, and maintain cloud infrastructure (primarily AWS)
• Build and automate CI/CD pipelines and deployment systems
• Write code (Python, Go, or similar) to improve tooling and workflows
• Manage and optimize Linux-based systems in production
• Configure and troubleshoot networking (DNS, firewalls, routing, etc.)
• Implement monitoring, logging, and alerting systems
• Improve developer experience and internal tooling
• Leverage AI tools (Claude, ChatGPT, etc.) to accelerate operations and engineering workflows
• Continuously refine infrastructure for performance, reliability, and cost-efficiency
• Manage bi-weekly releases
• Work with sales engineering to optimize customer onboarding and automation.

Data Scientist

• Own the LLM evaluation strategy at Driver — from first principles to production infrastructure.
• Define quality metrics and build evaluation datasets.
• Establish what 'good' looks like for each content type across the pipeline.
• Build and curate gold-standard evaluation datasets across languages and repo archetypes (monorepos, microservices, libraries, applications).
• Design rubrics that capture accuracy, completeness, usefulness, and readability.
• Build benchmarking and experimentation infrastructure.
• Create automated evaluation pipelines that score output against reference datasets.
• Instrument the content generation pipeline to support A/B comparisons — run the same codebase through two strategies and compare results.
• Build tooling for LLM-as-judge evaluation and regression detection.
• Integrate evaluation into CI so pipeline changes come with quality evidence.
• Develop automated quality signals at scale.
• Build quality checks that flag degraded output without requiring human review of every document.
• Monitor content quality trends over time.
• Design sampling strategies for human review that maximize signal with minimal annotation effort.
• Quantify tradeoffs and inform decisions.
• Run experiments on model selection, context strategies, and pipeline architecture changes.
• Quantify cost/quality/latency tradeoffs.
• Partner with the engineering team to turn evaluation insights into shipped improvements.

Software Engineer – Backend

DevOps Engineer

Applied Data Scientist, LLM Evaluation