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
Sales Operations is a key enabler of Marvell’s growth strategy. Connecting customers, Sales, Business Units, Finance, and Supply Chain, the team provides the analytical rigor, operational discipline, and execution excellence that power Marvell’s go-to-market engine.
By turning complex data into actionable insights, optimizing end-to-end sales processes, and strengthening cross-functional alignment, Sales Operations directly improves customer alignment, forecast predictability, and revenue performance. The team is instrumental in expanding Marvell’s presence among Cloud and AI customers—one of the company’s most strategic and fastest-growing markets.What You Can Expect
As the Customer Business Manager, you will serve as the central link between Marvell’s largest Cloud and AI customers and its internal execution teams. You will help achieve Marvell’s objectives by validating forecast integrity, improving demand-and-supply alignment, and driving disciplined execution across complex, multi-stakeholder environments.
You will challenge assumptions, validate customer demand signals against external market indicators—including capital expenditure, investment priorities, and market trends—as well as system-level ratios and attach rates. You will then translate these insights into more predictable revenue outcomes, improving operational responsiveness, decision quality, and business performance in one of Marvell’s most critical growth segments.
This role offers a unique opportunity to work at the intersection of Cloud and AI technology, customer strategy, and executive-level decision-making. You will gain visibility, influence, and ownership across Marvell’s most important customer relationships and growth initiatives while helping build the operational foundation of a company shaping the future of cloud and AI infrastructure.
At Marvell, your work does more than support the business—it drives it.
Strategic Analysis & Customer Intelligence
- Monitor public disclosures, announcements, and earnings calls for signals about the pace and direction of assigned customers’ infrastructure investments.
- Analyze disclosed and projected investments by segment, with emphasis on data center buildouts by region, scale, timeline, and estimated power availability.
- Develop and maintain rack-level models that document quantities, ratios, and share for all merchant and custom silicon content.
Forecast & Backlog Optimization
- Lead proactive, data-driven customer discussions to align demand, supply, product transitions, and long-range plans.
- Reconcile customer forecasts with internal forecasts and models to identify, resolve, and communicate gaps and divergent trends.
- Own the customer engagement cadence that drives backlog placement in line with customer and internal forecasts.
Alignment & Business Planning
- Coordinate closely across Cloud and AI customers, Sales, and key internal teams to strengthen revenue execution, forecast accuracy, and demand-supply alignment.
- Lead internal preparation for customer forecast reviews, demand-planning cycles, and strategic business reviews.
- Influence decisions across Sales, Business Units, Finance, and Supply Chain through data-backed insights and clear recommendations.
Revenue Growth & Market Penetration
- Identify opportunities to grow revenue, expand market share, and optimize ASPs across Cloud and AI customers.
- Apply deep knowledge of hyperscale trends, customer capital expenditure cycles, and competitive dynamics to strengthen Marvell’s position.
- Provide strategic recommendations that inform product strategy, pricing, and supply planning.
Accountability & Flawless Execution
- Hold internal and external stakeholders accountable for commitments related to strategic initiatives and customer agreements.
- Ensure reliable execution of contractual and operational commitments, including buffer strategies, share allocations, supply plans, and on-time delivery.
- Identify and escalate risks early, coordinating cross-functional mitigation plans to protect revenue and customer satisfaction.
What We're Looking For
- 8+ years of experience in sales operations, customer operations, or account management in the semiconductor industry.
- Customer-facing experience and the ability to navigate complex, data-driven business environments; experience with Cloud or AI customers is strongly preferred.
- Proven ownership of customer forecasting, demand planning, and revenue execution.
- Strong analytical skills and the ability to translate data into executive-ready insights.
- A strategic, solutions-oriented self-starter who thrives in fast-paced, high-accountability environments.
- Exceptional communication and influencing skills, with the ability to align diverse teams and drive timely, high-quality decisions.
- Demonstrated problem-solving ability, structured thinking, and a strong bias for action and accountability.
Expected Base Pay Range (USD)
150,140 - 224,900, $ 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.
This role is eligible to participate in Marvell's bonus and equity plans, including a new hire equity grant and annual equity refreshers, ensuring you are rewarded for your contributions and remain invested in the company's long-term success. 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.
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Salary Context
This $150K-$224K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($187K) sits 12% below the category median. Disclosed range: $150K to $224K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
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: $224K - $224K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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