Responsibilities

• Identify and diagnose quality issues in the crawling pipeline
• Develop small models to optimise specific aspects of the pipeline, such as parsing quality
• Create datasets for model training, including LLM-as-a-judge labelling pipelines
• Enhance page selection algorithms for indexing
• Design and analyse experiments to validate improvements

Qualifications

• 4+ years of experience as a data analyst, ML engineer, or in a related role
• Strong coding skills, with the ability to write production-grade code at the level of a mid-level backend engineer
• Experience designing metrics from scratch
• Experience training ML models that have shipped to production with measurable metric improvements

Nice to have

• Direct experience working on web crawling or indexing pipelines

XML job scraping automation by YubHub

Our work spans search and retrieval, LLM post-training, multi-agent training, and the harnesses that make these systems effective.

We control the full stack: the models, the agent harnesses, and the search infrastructure underneath. That gives us the freedom to develop new approaches across all three,training models to use search more effectively, designing tools and execution environments around learned behavior, and improving retrieval to support how agents actually work. You’ll help turn that freedom into better systems, taking ideas from experiments through training and evaluation to production.

Responsibilities

• Push search and agent quality forward through improvements to models, training data, tools, and system design.
• Develop LLM post-training methods, including reinforcement learning, to improve reasoning, search, tool use, and task completion.
• Train and evaluate multi-agent systems, exploring how agents divide work, share information, and coordinate effectively.
• Design and build agent harnesses: the tools, context management, execution environments, and orchestration that support reliable work over many steps.
• Improve retrieval and ranking models and the search interfaces agents use to find and assess information.
• Build datasets, reward signals, and evaluations that expose meaningful failures and guide improvements.
• Own experiments end to end, from a clear hypothesis to scalable training, deployment, and measurable gains in quality, latency, and cost.
• Collaborate with AI, Search, Infrastructure, Data, and Product teams to bring new capabilities into production.

Qualifications

• A strong track record of building and shipping ML systems, with deep experience in one or more of LLM post-training, reinforcement learning, search and retrieval, or agent systems.
• Strong software engineering skills and the ability to work across model training, experimentation infrastructure, and production systems.
• Experience designing rigorous evaluations, diagnosing failures, and translating experimental results into practical improvements.
• Comfort with open-ended problems that require both research judgment and hands-on engineering.
• A strong sense of ownership, curiosity, and the drive to carry an idea through to a working system.

Other relevant experience

• Training models to use tools or complete tasks over many steps.
• Multi-agent training, coordination, or evaluation.
• Building agent harnesses, distributed training systems, or scalable inference infrastructure.
• Large-scale retrieval, ranking, or recommendation systems.

We value depth in a relevant area and the ability to learn across the stack; we don’t expect prior expertise in every area above.

XML job scraping automation by YubHub