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Principal ML Investigator

Sunnyvale, CA · Sunnyvale, California, United States

Actively hiring, first seen by us today
Pay
Pay not listedSimilar roles state $166K–$240K
Work setting
On-site — work setting source
Location type: On-site
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
Software Engineering › Cluster — team source
Department: Software Engineering › Cluster
From the employer’s posting

What you’ll work on

Full posting

Cerebras is adding an ML team that can focus on a new ML effort that can align with existing teams.

Post-training and reinforcement learning: Techniques used to improve model deployment quality through further training, tuning, RL, and focus on particular downstream tasks;

  • Build up a team capable of industry research and advanced development

  • Organize advanced development topics into a cohesive research agenda

  • Adapt novel algorithms and model architectures to run on the Cerebras platform

  • Train, tune, and evaluate models to guide production scenarios

  • Collaborate with external customers and academic partners

From the employer’s posting
Cerebras is adding an ML team that can focus on a new ML effort that can align with existing teams. We are seeking a principal investigator who will partner with our ML leaders to formulate the new effort and to build up the new team and capabilities. This new team would coordinate with our current ML teams: Field ML, which works directly with customers, Applied ML, which builds new ML capabilities and applications for customers, and Core ML, which adapts ML algorithms to find unique capabilities of Cerebras hardware. The new team could take up the same or complementary responsibilities.
Post-training and reinforcement learning: Techniques used to improve model deployment quality through further training, tuning, RL, and focus on particular downstream tasks;
Build up a team capable of industry research and advanced development.
Organize various advanced development topics into cohesive agenda.
Adapt novel algorithms and model architectures to run on the Cerebras platform.
Systematically train, tune, and evaluate models to guide/advise production scenarios.
Collaborate with external partners (customers, academic) to drive insight and credibility.

What you’ll bring

All qualifications

Core experience

  • PhD in Computer Science or a related field
  • Strong grasp of ML theory in one or more focus areas
  • Proven experience engineering ML systems for scale or production
  • Experience leading a team of researchers or engineers
Qualification wording
PhD in Computer Science or related field.
Strong grasp of ML theory in one or more of the above areas.
Proven experience engineering ML systems for scale or production deployment.
Experience leading a team of researchers or engineers.
Education & alternatives
Skills & Qualifications - PhD in Computer Science or related field. - Strong grasp of ML theory in one or more of the above areas.
Apply at Cerebras Systems

Posting record

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Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Nov 8, 2026today
Recorded sightings
1
Employer says posted
Dec 12, 2025

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$166K–$240K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 1,319 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 0

Early. Similar jobs usually come down between day 7 and day 72.

From 4,655 similar postings that came down. Coming down is not always a hire.

Cerebras Systems is hiring

13 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring record

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Demand for ML Engineer roles

New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.

Weekly new ML Engineer postings at the same employers, from the week of August 17 to the week of October 26. The fewest in a week was 619, the most 1,091, and the latest week had 863.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.

This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About The Role

Cerebras is adding an ML team that can focus on a new ML effort that can align with existing teams. We are seeking a principal investigator who will partner with our ML leaders to formulate the new effort and to build up the new team and capabilities. This new team would coordinate with our current ML teams: Field ML, which works directly with customers, Applied ML, which builds new ML capabilities and applications for customers, and Core ML, which adapts ML algorithms to find unique capabilities of Cerebras hardware. The new team could take up the same or complementary responsibilities.

We would like the new team to work on some of the following areas:

  • Post-training and reinforcement learning: Techniques used to improve model deployment quality through further training, tuning, RL, and focus on particular downstream tasks;

  • Dataset curation and optimization: Techniques to collect and select high-quality data, which can help models to train or tune more quickly or to higher quality;

  • LLM Pretraining: Techniques to ensure stability and compute-efficiency while pretraining high quality models. May include training dynamics, parameterizations, numerics, or others;

  • Sparsity: Techniques to sparsify models or data that improve training time-to-quality, or optimize inference speed or throughput;

  • Domains: Coding agents, reasoning agents, generative language, image, video.

Principal Investigator Responsibilities

  • Build up a team capable of industry research and advanced development.

  • Organize various advanced development topics into cohesive agenda.

  • Adapt novel algorithms and model architectures to run on the Cerebras platform.

  • Systematically train, tune, and evaluate models to guide/advise production scenarios.

  • Collaborate with other teams to co-design next-generation hardware and software architectures.

  • Collaborate with external partners (customers, academic) to drive insight and credibility.

Skills & Qualifications

  • PhD in Computer Science or related field.

  • Strong grasp of ML theory in one or more of the above areas.

  • Proven experience engineering ML systems for scale or production deployment.

  • Experience leading a team of researchers or engineers.

Preferred Skills & Qualifications

  • Track record of patents or publications in top-tier conferences or journals.

  • Experience with large language models (e.g., GPT family, Llama).

  • Experience with distributed training concepts and frameworks.

  • Experience in training speed optimizations, such as model architecture transformations to target hardware, or low-level kernel development (e.g., Triton).

  • Ability to analytically model or optimize system performance.

Why Join Cerebras

People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

  1. Build a breakthrough AI platform beyond the constraints of the GPU.

  2. Publish and open source their cutting-edge AI research.

  3. Work on one of the fastest AI supercomputers in the world.

  4. Enjoy job stability with startup vitality.

  5. Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it's like to work at Cerebras here!

Apply today and become part of the forefront of groundbreaking advancements in AI!

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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