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### What MatX Is Building
MatX is on a mission to be the compute platform for AGI. We are developing vertically integrated full\-stack solutions from silicon to systems including hardware and software to train and run the largest ML workloads for AGI. MatX is seeking silicon micro\-architects and design engineers to join our team as we create best\-in\-class silicon for high\-performance and sustainable GenAI. Successful candidates for these roles will be responsible for delivering performant and functionally accurate silicon for MatX products across compute, memory management. High\-speed connectivity and other key technologies.
### About the Role
MatX is building next\-generation AI infrastructure systems for high\-performance datacenter workloads. We are looking for a Senior Platform TPM, Rack\-Scale AI Systems to drive design\-phase execution for our rack\-scale platform. This role will coordinate cross\-functional execution across rack architecture, accelerator board design, power delivery, thermal and liquid cooling, high\-speed interconnect, firmware, diagnostics, test vehicles, system validation, and external CM/JDM partner engagement. You will work closely with Systems Engineering, hardware engineering, firmware, validation, supply chain, and external CM/JDM partners.
### What You'll Do Here
- Drive the integrated execution plan for rack and board design from architecture through design release.
- Own and maintain the design\-phase master schedule across rack, board, power, thermal, interconnect, mechanical, firmware, diagnostics, validation, and supply\-chain dependencies.
- Coordinate execution across key platform workstreams, including board electrical/SI/PI readiness, rack power, liquid cooling, cable/optical interconnect, BMC/telemetry, diagnostics, test vehicles, PLM/BOM/ECO readiness, and external CM/JDM design reviews.
- Partner with Systems Engineering to define design gates, risks, dependencies, decision points, and exit criteria.
- Track critical path items, schedule risks, owners, closure dates, and escalation paths.
- Coordinate design\-readiness reviews, DFX reviews, and implementation\-readiness reviews with internal teams and external CM/JDM partners.
- Drive readiness for system validation using test vehicles, including power, thermal, connectivity, diagnostics, instrumentation, telemetry, and validation planning.
- Maintain a live risk register covering design readiness, system validation readiness, power/thermal risks, interconnect risks, long\-lead dependencies, and supply\-chain risks.
- Ensure long\-lead design dependencies are visible to supply chain, materials, and sourcing teams.
- Prepare executive\-level updates covering milestone health, top risks, decisions needed, critical path, and resource gaps.
- Ensure validation learnings are captured, tracked, and incorporated into the platform execution plan.
### Who you are
- 8\+ years of technical program management, hardware engineering program management, systems engineering, or hardware platform development experience.
- Experience managing complex hardware platforms through architecture, design, validation, and design release.
- Experience working across electrical, mechanical, thermal, firmware, validation, manufacturing, supply\-chain, and external partner teams.
- Ability to drive schedules, dependency management, issue tracking, risk management, milestone governance, and executive\-level communication.
- Experience coordinating design reviews and driving closure across cross\-functional engineering teams.
- Working familiarity with several of the following areas: board design, power delivery, SI/PI, thermal design, liquid cooling, high\-speed interconnect, firmware, diagnostics, rack integration, or system validation.
- Experience working with ODMs, JDMs, contract manufacturers, or external engineering/manufacturing partners.
- Strong written and verbal communication skills, including the ability to summarize technical risks, tradeoffs, decisions, and schedule impact.
- Proficiency with program\-management and engineering collaboration tools such as Jira, Confluence, Smartsheet, MS Project, Google Workspace, or equivalent.
### Bonus Points If You Have
- Experience with AI infrastructure, GPU/accelerator systems, datacenter racks, networking systems, storage systems, or high\-power server platforms.
- Experience coordinating programs involving high\-speed interconnects, liquid\-cooled systems, telemetry, diagnostics, or rack\-scale validation.
- Prior hands\-on engineering experience in hardware, firmware, systems, manufacturing, validation, or a related technical field.
Compensation
The US base salary for this full\-time position is determined based on a variety of factors including role, experience, location, job related skills, and relevant education and training. Career length is only a guideline for compensation.
- Early Career \- $120,000 \- $275,000 \+ equity
- Mid Career \- $175,000 \- $450,000 \+ equity
- Senior Career \- $275,000 \- $600,000 \+ equity
What We Offer
- A Stake in our success A cash/equity mix that fits your needs and option to do early exercise
- Health \& Wellness Company subsidized Health, Dental, Vision, and Life insurance; Pre\-tax Health Savings Accounts with generous company contribution (even if you don’t)
- Time To Recharge 4 weeks paid time off (accrued), 12 company holidays, and 3 weeks remote/flexible work per year
- Support to Parents Up to 12 weeks of paid parental leave, regardless of your path to parenthood
- Learning \& Development $1,500 yearly towards your professional development e.g. conferences, courses, and other learning opportunities
- Team Connection Team Lunches, quarterly off\-sites, and regular town halls
- Financial Wellbeing 401K and/or Roth IRA, with 5% company contribution, even if you don’t!
- Flexible Spending Accounts Pre\-tax spend accounts for medical, dental/vision, dependent care, parking, and transit expenses
- Commute On Us For those commuting up to 1 hour, put your rideshare cost on our company card and reclaim the drive\-time to get work done!
- MatX E\[x]tras $50 per month to use on the perks you care about most
- Remote Perks We work remotely Monday \& Friday, supported by home\-tech setup, and remote wifi expense reimbursement
*As part of our dedication to the diversity of our team and our focus on creating an inviting and inclusive work experience, MatX is committed to a policy of Equal Employment Opportunity and will not discriminate against an applicant or employee on the basis of race, color, religion, creed, national origin or ancestry, sex, gender, gender identity, gender expression, sexual orientation, age, physical or mental disability, medical condition, marital/domestic partner status, military and veteran status, genetic information or any other legally recognized protected basis under federal, state or local laws, regulations or ordinances.*
*All candidates must be authorized to work in the United States and work from our offices in Mountain View Tuesdays\-Thursdays.*
*This position requires access to information that is subject to U.S. export controls. This offer of employment is contingent upon the applicants capacity to perform job functions in compliance with U.S. export control laws without obtaining a license from U.S. export control authorities.*
*MatX does not accept unsolicited resumes from individual recruiters or third\-party recruiting agencies in response to job postings. No fee will be paid to third parties who submit unsolicited candidates directly to our hiring managers or People team and any resumes submitted are deemed to be the property of MatX.*
Salary Context
This $120K-$600K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1956 roles with salary data).
View full AI/ML Engineer salary data →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 3,739 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At MATX, 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 in Demand for This Role
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 $179,000 based on 11,901 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($360K) sits 101% above the category median. Disclosed range: $120K to $600K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
MATX AI Hiring
MATX has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Mountain View, CA, US. Compensation range: $600K - $600K.
Location Context
Across all AI roles, 16% (597 positions) offer remote work, while 3,119 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,739 open positions tracked in our dataset. By seniority: 115 entry-level, 1,764 mid-level, 1,444 senior, and 416 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (597 positions). The remaining 3,119 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,739 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,650), Data Scientist (271), AI Software Engineer (252). 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 (115) are outnumbered by mid-level (1,764) and senior (1,444) 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 416 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (597 positions), with 3,119 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,929 postings), Aws (1,185 postings), Azure (869 postings), Rag (866 postings), Gcp (726 postings), Prompt Engineering (578 postings), Pytorch (575 postings), Claude (547 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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