Perplexity
More roles at PerplexityMember of Technical Staff (Machine Learning Engineer, Search Agent RL)
Part remote, part office, you need to live within commuting distance of a named location.
Hybrid 路 Belgrade
Employer listed it 3 weeks ago 路 Added 3 weeks ago
Been open since 3 weeks ago, still being checked, but it has been live a while.
Salary
Not stated
Location
Hybrid 路 Belgrade
Timezone
Not stated
Contract
Full-time
Experience
Lead
Category
Data
The employer did not publish a salary, and we have too few comparable roles to estimate one.
What the advert says
Quoted word for word from the employer's advert. Where it says nothing, we say so.
- Working hours
- Not mentioned in the advert
- Where you can work from
- Location eligibility not confirmed
- Contract
- Not mentioned in the advert
- Experience asked for
- Not mentioned in the advert
- Pay
- Not mentioned in the advert
Remote flexibility
Hybrid
This role is only partly remote, the employer expects time in the office around Belgrade, London, Berlin, Hybrid, so you need to live within commuting distance.
What the employer says
- Source listing states candidate location: "Belgrade, London, Berlin, Hybrid"
- Listing mentions "Hybrid"
What Nomaders makes of it
- Not suitable if you plan to move between countries
The quotes above are the employer's own words; the reading is ours. Always check the original listing and employment terms before working from another country.
Before you apply
Questions worth asking: Nomaders interpretation
- This role involves office time, but the advert does not say how often. Ask how many days a week are expected on site.
- No pay is stated. Ask for the salary range before investing time in interviews.
- The advert does not say whether this is employment or a contractor arrangement. Ask, as it affects tax and benefits.
Produced automatically from this advert's text; not reviewed by a person. The employer's own page is the final word.
About the role
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.
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.
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.
Nice to have
Experience with 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.
Requirements
- 路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.
- 路Strong software engineering skills and the ability to work across model training, experimentation infrastructure, and production systems.
- 路Experience designing rigorous evaluations, diagnosing failures, and translating experimental results into practical improvements.
- 路Comfort with open-ended problems that require both research judgment and hands-on engineering.
- 路A strong sense of ownership, curiosity, and the drive to carry an idea through to a working system.
Benefits
No benefits package published with this listing. Ask about it at first interview.
How to apply
- 1Check the flexibility label above, hybrid, matches where you plan to live and work.
- 2Tailor your CV to the role at Perplexity, mentioning your remote working experience.
- 3Apply directly on the employer's careers page using the button below. Nomaders never handles your application.
Found 21d ago. Last checked today. Always confirm the details on the original posting, salary and location can change after publication.
Listing sourced from Company boards.
Pay not stated 路 You'll be taken to the employer's careers page.