---
title: 'Applied Machine Learning Research Scientist at Cerebras Systems'
canonical: 'https://feeny.ai/job/applied-machine-learning-research-scientist-cerebras-systems-united-states-and-3hja9635xha3'
type: 'job'
last_seen: '2026-09-08'
---

# Applied Machine Learning Research Scientist at Cerebras Systems

- **Company:** [Cerebras Systems](https://feeny.ai/companies/cerebras-systems)
- **Location:** United States and, Canada
- **Employment:** full-time
- **Posted:** 2026-03-05
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/cerebras/d654a704-2c58-4ba6-9109-2c92aecb2503/application

**Skills:** Python, PyTorch, Machine learning systems, Deep learning architectures, Transformers, ML paper implementation, Large language models, Reinforcement learning, Distributed training frameworks, FSDP, Megatron, Large-scale data pipelines, ML system optimization

> Join Cerebras Systems to translate modern machine learning techniques into scalable, high-performance systems. You will focus on LLM training, fine-tuning, and reinforcement learning workflows, bridging the gap between research algorithms and production-ready infrastructure on advanced AI hardware.

## Job description

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 https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

## About The Role

As an Applied Machine Learning Research Scientist at Cerebras, you will play a key role in turning modern machine learning techniques into scalable, high-performance systems. This role sits at the intersection of modeling and systems focused not on publishing new algorithms, but on understanding how they work and making them run effectively at scale. Your work will directly impact how large language models (LLMs) are trained, optimized, and deployed on one of the most advanced AI platforms in the world.

You will work closely with researchers and senior engineers to implement and improve workflows for LLM pretraining, fine-tuning, and reinforcement learning-based post-training. This includes building training pipelines, debugging complex system behaviors, improving model quality, and iterating on data and evaluation strategies. Your contributions will help translate cutting-edge ML ideas into reliable, production-ready systems that solve real-world problems.

This role is ideal for candidates who enjoy hands-on engineering, want to build deep intuition for ML systems, and are excited about working on LLMs and reinforcement learning in practice, not just in theory.

## Responsibilities

- Apply post-training techniques (e.g. RLVR, RLHF, GRPO etc.) techniques to improve model performance.
- Build and maintain evaluation pipelines to measure model performance across tasks and domains.
- Debug issues across the ML stack, including data pipelines, training jobs, model outputs and mixed or lower precision computation.
- Collaborate with researchers to translate ML ideas into efficient, scalable implementation.
- Design, implement, and scale ML pipelines across all stages of LLM development (pretraining, fine-tuning, alignment).
- Work with large datasets, including dataset generation, filtering, and synthetic data approaches.
- Optimize training and inference workflows for performance, efficiency, and reliability.
- Contribute high-quality, maintainable code to shared ML infrastructure.

## Skills & Qualifications

- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 4+ years of experience (including internships, research, or industry experience) working with machine learning systems; we are hiring multiple positions for various levels.
- Strong programming skills in Python.
- Experience with ML frameworks such as PyTorch.
- Solid understanding of machine learning fundamentals.
- Familiarity with deep learning architectures, particularly transformers.
- Ability to read and understand modern ML papers and implement key ideas.

## Preferred Skills & Qualifications

- Experience working with large language models (training, fine-tuning, and evaluation).
- Familiarity with reinforcement learning concepts.
- Experience with distributed training frameworks (e.g., FSDP, Megatron).
- Experience working with large-scale datasets and data pipelines.
- Experience debugging or optimizing ML systems for performance.
- Contributions to meaningful codebases, projects, or open-source systems

## 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 https://www.cerebras.ai/join-us!

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.

This website or its third-party tools process personal data. For more details, click here https://www.cerebras.net/privacy/ to review our CCPA disclosure notice.

## About Cerebras Systems

## Company Overview
- **One-liner**: Cerebras builds wafer-scale AI chips (the largest semiconductors ever made) and supercomputers that deliver up to 15x faster inference than GPUs, offered both on-premises and via cloud APIs.
- **Entity Type**: Public (NASDAQ: CBRS) — filed for IPO and now listed on the Nasdaq.
- **Headquarters**: Sunnyvale, California, United States
- **Founded**: 2015
- **Founders**: Andrew Feldman, Gary Lauterbach, Michael James, Sean Lie, Jean-Philippe Fricker

## Core Business
- **Primary industries**: Semiconductor manufacturing, AI computing hardware and software, cloud AI services.
- **Target customers**: B2B – enterprises, government research labs, cloud service providers, and AI developers.
- **Mission**: “Building impossible technologies so you can too” – accelerating AI through wafer-scale computing.

## Products & Services
- **Wafer-Scale Engine (WSE-3)**: The world’s largest AI chip (215 mm²), 58x larger than a typical GPU, built for ultra‑fast AI training and inference. Manufactured by TSMC.
- **CS-3 Supercomputer**: A single system powered by the WSE-3, delivering record-breaking AI performance for on-premises deployment.
- **Cerebras AI Inference Cloud & Training Cloud**: Pay-as-you‑go APIs that provide drop‑in OpenAI‑compatible access to Cerebras’ hardware for serving, fine‑tuning, and pre‑training models.
- **Condor Galaxy Network**: A series of supercomputers (e.g., CG‑1 with 4 exaFLOPs) built in partnership with G42 for high‑performance AI.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (recently public; market cap data not available in provided sources).
- **Key Metric**: Annual revenue of $78.7 M (latest); total funding of $2.7 B across 15 rounds.
- **Notable Investors/Partners**: G42, OpenAI (signed in 2026), Amazon Web Services (signed in 2026), Meta (Llama API), Perplexity, Mistral, Hugging Face, OpenRouter.
- **Growth Signals**: 
  - 34.3% YoY headcount growth (701 employees).
  - Rapid data center expansion across North America and Europe.
  - Won the HPCwire Readers’ & Editors’ Choice 2025 for Best AI Product/Technology.
  - Named to Forbes America’s Best Startup Employers 2026 and Fast Company Most Innovative Companies 2026 (AI).

## Competitive Advantages
- **Wafer‑scale architecture**: Eliminates interconnect bottlenecks, reducing latency and delivering up to 15x faster inference than GPU clusters.
- **Largest AI chip ever built**: 58x larger than any GPU, enabling massive on‑chip memory (SRAM) and compute density.
- **Full‑stack offering**: Hardware + cloud APIs + on‑prem systems, giving customers deployment flexibility.
- **Strong customer relationships**: Key contracts with OpenAI, AWS, G42, and Mayo Clinic (2024 Gordon Bell Prize work).

## Strategic Focus
- **Scale inference capacity**: Building out data centers to become the world’s #1 provider of high‑speed AI inference.
- **Deepen cloud partnerships**: Expanding pay‑as‑you‑go cloud access and enterprise deployments.
- **Advance wafer‑scale technology**: Continuous R&D on WSE‑3 successors and software stack (compiler, ML workflows).
- **Broaden model support**: Already serving Llama, Gemma, Qwen, Mistral, and more; integrating with Hugging Face and OpenRouter.

## Why Work Here
- **Culture**: “Extraordinary people, breakthrough innovation, global impact” – the company highlights collaborative, low‑overhead teams with little bureaucracy.
- **Work environment**: Hybrid/office with locations in Sunnyvale, San Diego, Toronto, and Bangalore. Inclusive and flexible policy.
- **Perks**: Premium medical/dental/vision, life insurance, generous vacation, 401(k) and Group RRSP retirement plans, daily catered meals, healthy snacks, family‑friendly events (including CEO’s famous BBQ).
- **Engineering focus**: Tackling fundamental challenges in chip design, system software, compiler technology, and ML workflows. Positions range from hardware (Design Verification, Manufacturing) to software (SRE, Compiler, Cloud).
- **Philanthrophy**: Supports local communities and hosts students from around the world for Q&A.

## Sources
1. [cerebras.ai/company](https://www.cerebras.ai/company)
2. [cerebras.ai](https://www.cerebras.ai/)
3. [cerebras.ai/join-us](https://www.cerebras.ai/join-us)
4. [linkedin.com/company/cerebras-systems](https://www.linkedin.com/company/cerebras-systems)

## Other roles at Cerebras Systems

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- [Distributed Software Engineer](https://feeny.ai/job/distributed-software-engineer-cerebras-systems-sunnyvale-2y0byqqxk57d) — Sunnyvale, CA
- [AI Inference Core - Infrastructure SW Engineer](https://feeny.ai/job/ai-inference-core-infrastructure-sw-engineer-cerebras-systems-sunnyvale-nbqee273jncn) — Sunnyvale, CA
- [AI Inference Core - Senior SW Engineer for Platform & DevOps](https://feeny.ai/job/ai-inference-core-senior-sw-engineer-for-platform-devops-cerebras-systems-06p3hxeg5qb7) — Sunnyvale, CA
- [AI Inference Core - SDET Technical Lead, Release Integration Testing](https://feeny.ai/job/ai-inference-core-sdet-technical-lead-release-integration-testing-cerebras-fvqxmj75wav1) — Sunnyvale, CA
- [ML Systems Integration Engineer](https://feeny.ai/job/ml-systems-integration-engineer-cerebras-systems-sunnyvale-edvsd91tqp93) — Sunnyvale, CA
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- [PCB Layout Engineering Lead](https://feeny.ai/job/pcb-layout-engineering-lead-cerebras-systems-united-states-em00ewnccr6z) — United States
- [Network Security Engineer (Remote)](https://feeny.ai/job/network-security-engineer-remote-cerebras-systems-united-states-yfrjb08d4wh9) — United States