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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 existing and upcoming megatrends of cloud services, video streaming, 5G wireless and AI/ML among others, are driving the relentless demand for higher bandwidth, lower power and smaller footprint. Marvell offers a field proven solution for high\-speed optical interconnects and transceivers that are utilized for a wide array of enterprise, carrier, small medium business, industrial and cloud data center applications.What You Can Expect
Key Responsibilities
Build Data Pipelines:
Design and develop scalable data pipelines to ingest, process, and store large volumes of DSP validation and test data
Data Analysis \& Modeling:
Apply statistical analysis and machine learning techniques to identify patterns, detect anomalies, and support root\-cause analysis
Visualization \& Dashboarding:
Develop intuitive dashboards and visualizations to enable AE/FAE and validation engineers to quickly interpret test results and debug issues
Cloud\-Based Analytics:
Leverage cloud technologies to process and analyze large\-scale datasets efficiently, enabling near real\-time insights
Collaboration with Engineering Teams:
Work closely with hardware, firmware, and validation engineers to understand data, define metrics, and translate complex data into actionable insights Automation \& Efficiency:
Build tools and workflows that reduce manual debugging effort and accelerate validation cycles
What Makes This Role Exciting
- Work on cutting\-edge high\-speed connectivity systems (DSP/PHY)
- Apply AI/ML to real\-world hardware validation challenges
- Build end\-to\-end data platforms (from ingestion analytics visualization)
- Direct impact on product quality and time\-to\-market
- Opportunity to contribute to next\-generation AI\-driven debugging platforms
What We're Looking For
We are seeking a highly motivated Data Scientist / Data Analyst to support data analysis and data mining for high\-speed DSP (Digital Signal Processing) validation and interoperability testing. This role focuses on building scalable data pipelines, developing intelligent analytics, and delivering actionable insights to accelerate debug and validation cycles.
You will work at the intersection of hardware systems, large\-scale data, and AI\-driven analytics, enabling engineers to quickly identify issues, optimize system performance, and improve product quality.
Minimum Qualifications
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- Bachelor’s degree in Computer Science, Electrical Engineering, or related field with 3–5 years of industry experience, or Master’s / PhD with 1\-2 years of experience
- Strong foundation in data analysis, statistical modeling, and machine learning
- Proficiency in Python (pandas, numpy, matplotlib/seaborn, scikit\-learn or similar)
- Experience with data visualization tools such as Tableau or equivalent (e.g., Power BI, Superset)
- Experience working with large datasets and performing data cleaning, transformation, and feature engineering
Preferred Qualifications
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- Experience with cloud platforms (e.g., Amazon Web Services, Snowflake, Databricks)
- Familiarity with data pipeline development (ETL, streaming, batch processing)
- Experience with time\-series data analysis or signal/data from hardware systems
- Exposure to DSP systems, networking, or semiconductor validation workflows
- Experience with SQL and database systems (e.g., Snowflake, PostgreSQL)
- Knowledge of machine learning for anomaly detection, prediction, or optimization
- Familiarity with dashboard design for engineering workflows
\#LI\-TM1
Expected Base Pay Range (USD)
108,220 \- 162,100, $ 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\-SA1
Salary Context
This $108K-$162K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($135K) sits 27% below the category median. Disclosed range: $108K to $162K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; 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: $162K - $162K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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