Member of Technical Staff (Machine Learning Engineer, Search & Agents)

Belgrade · Remotefull time

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Remote

senior

H1B: yes

Perplexity The Machine Learning Engineer advances AI systems for search, reasoning, and multi-agent coordination by developing models, training methods, and agent harnesses. This role involves end-to-end ownership of experiments and collaboration across AI, Search, Infrastructure, Data, and Product teams.

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.

Qualification

A strong sense of ownershipOther relevant experienceMulti-agent trainingBuilding agent harnessesLarge-scale retrieval

Required

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.

Preferred

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