Interested in this AI/ML Engineer role at Marvell Technology?
Apply Now →About This Role
About Marvell
Marvell’s semiconductor solutions are the essential building blocks of the data infrastructure that connects our world. Across enterprise, cloud and AI, and carrier architectures, our innovative technology is enabling new possibilities.
At Marvell, you can affect the arc of individual lives, lift the trajectory of entire industries, and fuel the transformative potential of tomorrow. For those looking to make their mark on purposeful and enduring innovation, above and beyond fleeting trends, Marvell is a place to thrive, learn, and lead.
Your Team, Your Impact
The Network and Compute Product Engineering organization at Marvell serves as a core technical leadership function during product development and NPI. The team operates across multiple business units and works closely with design engineering, operations, program management, quality, and leadership to guide technologies from early development into production ready silicon.
Product Engineering is responsible for ensuring technical rigor, manufacturability, and readiness for customer deployment. The function exists to connect architecture, silicon behavior, manufacturing reality, and business constraints, and to drive teams toward the technical decisions required for successful productization.
The organization plays a key role in enabling next\-generation compute and AI\-driven infrastructure, supporting products deployed at scale across hyperscale data centers and emerging AI workloads.What You Can Expect
As a Principal Product Engineer within the Networking and Compute organization, you are recognized as a technical leader and key stakeholder at the intersection of product engineering and next‑generation test strategy. The rapid evolution of AI infrastructure is driving XPU and hyperscale products into unprecedented performance, volume, complexity regimes, and the methodologies used to validate and qualify them must evolve in kind. In this role, you will help lead that evolution, shaping how Marvell approaches test and productization for its most strategically critical product lines. You are not simply assigned products to manage \- you own them; and with them, the technical judgment, influence, and leadership to drive meaningful change across the full end‑to‑end lifecycle.
In this role, you will...
- Drive technical leadership from development through production, ensuring decisions support robust, scalable productization
- Serve as a primary technical stakeholder during NPI, guiding teams through characterization, debug, qualification, and readiness discussions
- Align and influence cross‑functional engineering teams toward technically sound, full‑closure solutions during complex problem‑solving efforts
- Establish and defend test tier architecture across wafer sort, ATE, and SLT, making data‑driven tradeoff decisions that balance coverage, escape risk, quality, and cost‑of‑test
- Lead ATE‑to‑SLT correlation efforts and drive structured escape analysis, translating yield and quality signals across test tiers into actionable product and process improvements
- Evaluate, champion, and help scale emerging SLT technologies, including high‑throughput, parallelized test platforms to improve test economics and manufacturing scalability
- Align and influence cross‑functional engineering teams toward technically sound, full‑closure solutions during complex problem‑solving efforts
- Define, quantify, and defend product quality, reliability, performance, and cost targets based on data and system‑level impact
- Lead technical risk assessments and tradeoff discussions, driving clarity and direction across engineering, operations, and management
- Represent Product Engineering in customer interactions and internal leadership forums, clearly articulating status, risk, and recommended paths forward
What We're Looking For
- Expertise in System Level Test platforms and validation work is a must; hands‑on experience with SLT board design, DUT fixturing, and socket qualification is strongly preferred
- Deep understanding of test tier architecture across wafer sort, ATE, and SLT, with the ability to make principled tradeoff decisions on coverage, escape risk, and cost‑of‑test
- Demonstrated experience driving ATE‑to‑SLT correlation, yield learning, and escape containment across test tiers in a volume manufacturing environment
- Familiarity with emerging high‑throughput SLT methodologies — including parallelization strategies, handler and thermal system constraints, and scalable test content development
- Deep understanding of the end‑to‑end product lifecycle, with the ability to drive technical decisions that enable scalable productization
- Strong expertise in structured problem solving and Root Cause Analysis across silicon, test, and manufacturing environments
- Proven ability to translate complex technical data and analytics into clear, executive‑level narratives and recommendations
- Hands‑on experience using ATE for silicon characterization, analysis, and debug; experience with 93K is strongly preferred
- Solid foundation in applied statistics for product characterization, yield improvement, and quality analysis
- Working knowledge of data analytics and visualization tools such as JMP and SiliconDash or equivalent platforms
- Strong planning and prioritization skills across multiple parallel efforts; interest in process automation and workflow optimization is a plus
Education and Experience
- Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field with 10\+ years of relevant industry experience, preferably in semiconductors
OR
- Master’s degree and/or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field with 5\+ years of relevant industry experience, preferably in semiconductors
Expected Base Pay Range (USD)
136,880 \- 205,000, $ per annum
The successful candidate’s starting base pay will be determined based on job\-related skills, experience, qualifications, work location and market conditions. The expected base pay range for this role may be modified based on market conditions.
Additional Compensation and Benefit Elements
Marvell is committed to providing exceptional, comprehensive benefits that support our employees at every stage \- from internship to retirement and through life’s most important moments. Our offerings are built around four key pillars: financial well\-being, family support, mental and physical health, and recognition. Highlights include an employee stock purchase plan with a 2\-year look back, family support programs to help balance work and home life, robust mental health resources to prioritize emotional well\-being, and a recognition and service awards to celebrate contributions and milestones. We look forward to sharing more with you during the interview process.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status.
Any applicant who requires a reasonable accommodation during the selection process should contact Marvell HR Helpdesk at [email protected].
Interview Integrity
To support fair and authentic hiring practices, candidates are not permitted to use AI tools (such as transcription apps, real\-time answer generators like ChatGPT or Copilot, or automated note\-taking bots) during interviews.
These tools must not be used to record, assist with, or enhance responses in any way. Our interviews are designed to evaluate your individual experience, thought process, and communication skills in real time. Use of AI tools without prior instruction from the interviewer will result in disqualification from the hiring process.
This position may require access to technology and/or software subject to U.S. export control laws and regulations, including the Export Administration Regulations (EAR). As such, applicants must be eligible to access export\-controlled information as defined under applicable law. Marvell may be required to obtain export licensing approval from the U.S. Department of Commerce and/or the U.S. Department of State. Except for U.S. citizens, lawful permanent residents, or protected individuals as defined by 8 U.S.C. 1324b(a)(3\), all applicants may be subject to an export license review process prior to employment.
\#LI\-TT1
Salary Context
This $136K-$205K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Marvell Technology, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($170K) sits 22% below the category median. Disclosed range: $136K to $205K.
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.
Marvell Technology AI Hiring
Marvell Technology has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, US. Compensation range: $205K - $205K.
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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