Senior machine learning engineer

Responsibilities

• Build and deliver agentic search capabilities for AI assistants and developer workflows through APIs, command-line tools, and protocols such as the Model Context Protocol (MCP).
• Develop reproducible benchmarks and evaluation tools that reflect realistic tasks across models and agent environments. Enable trustworthy performance comparisons, make failures easier to diagnose, and accelerate experimentation.
• Use evaluation results, production signals, and developer feedback to improve the full search system—from search strategies and model behavior to tool interfaces, input and output schemas, and context selection.
• Build agentic search systems that interpret user intent, plan and execute searches, select appropriate sources and tools, and adapt based on retrieved evidence.
• Explore and apply advances in LLMs and agent systems, including prompting, model selection, training data improvements, and fine-tuning where appropriate. Use evidence to decide which approaches to bring into production.
• Own projects from problem definition and technical design through experimentation, deployment, and ongoing measurement, building reliable systems that respect enterprise permissions and data boundaries.
• Collaborate across product, search, and AI teams to shape technical direction and integrate agentic search into customer experiences.
• Contribute to technical design and code reviews, mentor engineers, and help the team evolve its engineering practices, including effective use of AI tools.
• Your background
• On the first day, we’ll expect you to have

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