Member of Technical Staff (Machine Learning Engineer, Search & Agents)
Remote • Belgrade • FullTime
Posted 10h ago
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Agent Engineer
About the job
Perplexity is seeking an experienced Machine Learning Engineer to advance how AI systems search, reason, and work together to solve complex problems. 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, giving us the freedom to develop new approaches across models, agent harnesses, and search infrastructure. You will help turn this freedom into better systems, taking ideas from experiments through training and evaluation to production.
Responsibilities
- Advance search and agent quality through improvements to models, training data, tools, and system design.
- Develop LLM post-training methods, including reinforcement learning, to enhance reasoning, search, tool use, and task completion.
- Train and evaluate multi-agent systems, focusing on work division, information sharing, and coordination.
- Design and build agent harnesses, including tools, context management, execution environments, and orchestration for reliable multi-step work.
- Improve retrieval and ranking models and the search interfaces used by agents for information discovery and assessment.
- Build datasets, reward signals, and evaluations to identify failures and guide improvements.
- Own experiments end-to-end, from 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.
Requirements
- 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 with experience 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 requiring research judgment and hands-on engineering.
- Strong sense of ownership, curiosity, and drive to carry an idea through to a working system.