ML Engineer: LLM Post-Training & Multi-Agent Systems

Perplexity; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

Perplexity is seeking an experienced Machine Learning Engineer to advance how AI systems search, reason, and collaborate to solve complex problems. We control the full stack from models to harnesses and search infrastructure, enabling new approaches across all three training models to improve retrieval and system reliability.

The role focuses on building and evaluating ML systems, including reinforcement learning for reasoning, and coordinating multi-agent setups to deliver measurable gains in

Qualifications

• Strong track record of building and shipping ML systems, with deep experience in LLM post-training, reinforcement learning, search and retrieval, or agent systems.
• Strong software engineering skills 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.

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 around 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.

Skills

ML systems

LLM post-training

Reinforcement learning

Search retrieval

Agent systems

Software engineering

Experiment design

Ownership curiosity

Job description

Perplexity is seeking an experienced Machine Learning Engineer to advance how AI systems search, reason, and collaborate to solve complex problems. We control the full stack from models to harnesses and search infrastructure, enabling new approaches across all three training models to improve retrieval and system reliability.

The role focuses on building and evaluating ML systems, including reinforcement learning for reasoning, and coordinating multi-agent setups to deliver measurable gains in