We're looking for a Senior/Staff Machine Learning Engineer to join our General Agents team. As a key member of our team, you'll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems.
The General Agents team builds robust general agents for customer use cases and applications. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers.
As a Senior/Staff Machine Learning Engineer, you'll 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.
Your responsibilities will include:
- Designing and implementing end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases.
- Building scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints.
- Developing evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings.
- Collaborating closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs.
- Productionizing frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems.
- Owning deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage.
- Contributing to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level.
To be successful in this role, you'll need:
- 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.
Nice-to-haves include 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, and interest in shaping the future of general-purpose enterprise agents and their real-world impact.
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