Member of Technical Staff (Machine Learning Engineer, Search & Agents)
Join Perplexity as a Machine Learning Engineer to enhance AI systems for search and problem-solving in a full-stack environment.
Posted by employer 1 day ago
First seen on Joblaze 1 hour ago
Last verified on the company career page 1 hour ago
Skills & Technologies
What you'll build
- Push search and agent quality forward
- Develop LLM post-training methods
- Train and evaluate multi-agent systems
- Design and build agent harnesses
- Improve retrieval and ranking models
Must have
- Strong track record of building and shipping ML systems
- Deep experience in LLM post-training, reinforcement learning, search and retrieval, or agent systems
- Strong software engineering skills
Nice to have
- 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
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Joblaze summary
In this role, the Machine Learning Engineer focuses on enhancing AI systems for search and multi-agent collaboration, driving improvements in model training, evaluation, and system design. Key skills include expertise in LLM post-training, reinforcement learning, and search infrastructure, alongside strong software engineering capabilities. This position is ideal for someone with a solid background in machine learning systems and a hands-on approach to problem-solving. The team operates at the intersection of AI and search technology, emphasizing innovation and practical application.
Joblaze insights
- Listed today — first seen on Joblaze October 7, 2026. Last confirmed on Perplexity's careers page October 7, 2026.
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: Machine Learning, Multi-Agent Systems, Retrieval, Search, reinforcement learning.
- What seniority level is this role?
- Perplexity targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Member of Technical Staff (Machine Learning Engineer, Search & Agents) role at Perplexity.
From the original posting
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: 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.