Senior/Staff Machine Learning Research Engineer, General Agents, Enterprise GenAI

As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you'll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle-from model and system design to evaluation, deployment, and iteration-bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments.

- Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases.

- Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints.

- Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings.

- Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs.

- Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems.

- Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage.

- Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level.

- 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases.

- Strong engineering fundamentals, supported by a Bachelor's and/or Master's degree in Computer Science, Machine Learning, AI, or equivalent practical experience.

- Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design.

- Proven proficiency in Python, including writing production-quality, testable, and maintainable code.

- Experience building systems that integrate models with external tools, APIs, databases, and services.

- Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.

- Strong communication skills and comfort working in customer-facing or cross-functional environments.

- Hands-on experience building AI agents using modern generative AI stacks (OpenAI APIs, commercial or open-source LLMs).

- Experience with agent frameworks, orchestration layers, or workflow systems (e.g., tool calling, planners, multi-agent setups).

- Familiarity with evaluation, monitoring, and observability for LLM-powered systems in production.

- Experience deploying ML systems in cloud environments and operating them at scale.

- Experience fine-tuning or adapting foundation models using methods like supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and low-rank adaptation (LoRA) to improve agent performance on domain-specific tasks.

- Interest in shaping the future of general-purpose enterprise agents and their real-world impact.