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About This Role
Location
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Headquarters/Sunnyvale Office
Employment Type
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Full time
Location Type
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On\-site
Department
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Product Management Departments
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.
Own the Future of AI Inference
Cerebras powers the world's fastest AI inference. As the Product Manager for AI Models, you'll lead the strategic model portfolio that defines our product — deciding which models ship, how they perform, and how the world discovers them.
You'll partner directly with leading AI labs, drive launches that shape the industry, and ensure every model on our platform delivers exceptional quality at unprecedented speed.
What You'll Own
Strategic Model Portfolio
- Own the models roadmap: decide which frontier and open\-source models we support based on market demand, research trends, and strategic fit
- Establish partnerships with top model labs, for day0 launches
- Build relationships with open\-source maintainers to accelerate community model adoption
Product Quality \& Customer Success
- Define and enforce quality standards across our model catalog through systematic evaluation frameworks
- Design benchmarks and evaluations that prove our models deliver production\-grade performance
- Own the feedback loop: gather customer insights, identify model weaknesses, and drive improvements with engineering
- Enable strategic customers to integrate our inference into their products—removing blockers and optimizing for their specific use cases
Go\-to\-Market Excellence
- Lead high\-impact model launches that generate buzz and adoption
- Create compelling product marketing: demos, benchmarks, tutorials, and documentation that showcase what's possible on Cerebras
- Craft technical content that resonates with developers and decision\-makers alike
Technical Decision\-Making
- Select and prioritize performance optimizations (quantization, speculative decoding, etc.) based on customer needs and hardware capabilities
- Collaborate with optimization engineers to implement techniques that maximize our speed advantage
- Balance tradeoffs between quality, latency, throughput, and cost
Cross\-Functional Leadership
- Orchestrate launches across model enablement, optimization engineering, deployment, sales, and marketing
- Drive alignment in a fast\-moving environment where priorities shift based on model releases and customer needs
- Be the voice of the customer to engineering and the voice of product to customers
Skills \& Qualifications
What we need to see:
- 5\+ years of experience as a product manager, currently at or above the level of Senior PM.
- 5\+ years of total technical work experience (e.g. SWE, ML researcher, solution engineer).
- Ability to thrive in a fast\-paced, dynamic environment. With an entrepreneurial sense of ownership and ability to lead projects.
- Knowledge and passion for the worlds of open\-source models and generative AI research.
- Knowledge of the community model ecosystem, including: PyTorch, Hugging Face, vLLM, and SGLang.
- Highly motivated, independent, organized, and an effective communicator.
- Comfortable using Python with the chat completions API, for basic model testing.
Preferred requirements
How to stand out:
- Product manager experience at a model training lab or a company that implements open\-source models.
- Experience working with customers in a solution engineering role.
- Experience writing technical marketing assets and social media, with a growing portfolio.
- Experience working in a cross\-functional organization, and leading projects across multiple teams.
- Experience writing model quality evaluations and system prompt harnesses.
- Experience writing application code in use cases such as code generation or deep research search application.
- Expertise on agentic flows and current LLM model family architectures.
- Understanding of model compilers and optimization.
- Contributor to communities like vLLM, SGLang, PyTorch, or Hugging Face transformers.
- Experience with model optimization or compression methods like quantization.
Location
- Hybrid at our Sunnyvale, California or Toronto, Canada office.
- Remote possible for candidates willing to travel 1\-2x per quarter.
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:
- Build a breakthrough AI platform beyond the constraints of the GPU.
- Publish and open source their cutting\-edge AI research.
- Work on one of the fastest AI supercomputers in the world.
- Enjoy job stability with startup vitality.
- Our simple, non\-corporate work culture that respects individual beliefs.
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.*
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Cerebras Systems, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Cerebras Systems AI Hiring
Cerebras Systems has 9 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span US, Sunnyvale, CA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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