Level

Exa

Research, ML

Exa is hiring a Research, ML in San Francisco, United States. It pays $180k-$350k a year and Level rates it ; you can apply on Exa.

Level's read on this job

  • Builds AI ●●●● is how Level rates this role: the job is building AI systems.
  • Exa mentions visa sponsorship in this posting.
  • Tools and skills the posting cites include PyTorch, Cursor and fine tuning.
  • Level classifies it as a mid-level role.
  • Level lists 56 other open roles at Exa.

AI in this role

pytorchcursor
fine-tuning

Exa is an applied AI lab building a search engine unlike the world has ever seen. We build massive-scale infra to crawl the entire web, train state-of-the-art embedding models to process it, and design super high performant vector databases to retrieve over it. We now power search for Cursor, Cognition, HubSpot, and over 400,000 developers and have raised $350m from Lightspeed, Benchmark, and a16z.

 

Our ultimate goal is to build perfect search over all the world's information, far beyond Google. If you want to build massive-scale ML systems that will define the way the new AI world consumes information, this is the place for you.

 

Research at Exa

 

The ML organization sits at the heart of our mission. We train foundational models for search. Our goal is to build systems that can instantly filter the world's knowledge to exactly what you want, no matter how complex your query. Basically, put the web into an extremely powerful database.

 

We're looking for an ML Research Engineer to train embedding models for perfect search over the web. The role involves dreaming up novel transformer-based search architectures, creating datasets, creating evals, beating our internal SoTA, and repeat.

Desired Experience

  • You have graduate-level ML experience (Master's degree or PhD with at least 2 years of relevant experience, or are an exceptionally strong undergrad)

  • You can code up a transformer from scratch in PyTorch

  • You like creating large-scale datasets and diving deeply into the data

  • You care about the problem of finding high quality knowledge and recognize how important this is for the world

Example Projects

  • Pre-training: Train a hundred billion parameter model

  • Fine-tuning: Build an RLAIF pipeline for search

  • Dream up a novel architecture for search in the shower, then code it up and beat our best model's top score

  • Build an eval system that answers: How do we know we're advancing our search quality? (this is an incredibly difficult question to answer)

Logistics

  • Location: This is an in-person opportunity in San Francisco.

  • Visas: We're happy to sponsor international candidates (e.g., STEM OPT, OPT, H1B, O1, E3). While we cannot guarantee your visa, we have historically been successful in sponsoring candidates from all over the world. If you receive an offer, our team will work hard to get you a visa.

  • Benefits: We offer premium healthcare benefits (medical, dental, vision), fertility benefits, 16 weeks of fully paid parental leave for all new parents, and a monthly wellness stipend to all of our employees.

Exa is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, age, veteran status, marital status, pregnancy or related conditions, criminal histories consistent with applicable law, or any other basis protected by applicable law.

How we rate this

Research, ML at Exa rates 94 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningPyTorchCursor

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. What are the limits of Cursor that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Fine-tuning, PyTorch, and Cursor. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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