AI/ML Data Engineer

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

Embedded within the AI/ML team, this role owns the data engineering layer that powers both Gen AI applications and ML model development. Responsible for building production-grade pipelines, curating AI-ready datasets for LLMs and ML models, and contributing to front-end interfaces when required — ensuring the team can deliver complete, data-driven AI products without external dependency.

What You Can Expect

Key Responsibilities

• Architect and deliver production-grade ELT/ETL pipelines across Databricks and Snowflake for ML training, validation, and inference workflows

• Build and maintain AI-ready datasets optimized for both ML model consumption and Gen AI use cases — clean, versioned, and reproducible

• Curate and structure high-quality datasets for RAG pipelines and embedding generation; design document chunking strategies, metadata schemas, and grounding data layers that directly improve retrieval accuracy and Gen AI application performance

• Implement data quality frameworks and data contracts at pipeline boundaries to protect model and application integrity

• Build and manage vector-ready data assets, integrating with vector stores and embedding infrastructure for Gen AI applications

• Establish DataOps best practices — CI/CD for pipelines, data lineage, versioning, and cost observability across platforms

• Develop Streamlit applications and React-based UIs to surface model outputs, data products, and internal AI tooling

• Partner with ML Engineers, Data Scientists, and AI Engineers to translate modeling and application requirements into reliable data products

• Contribute to lakehouse architecture decisions, storage optimization, and compute efficiency across the AI/ML data platform

What We're Looking For

Required Skills

• Databricks — Spark, Delta Lake, Databricks Workflows, Unity Catalog; production-grade experience required
• Snowflake — advanced SQL, data modeling, performance tuning, cost management
• Python — strong engineering fundamentals; PySpark, pandas, pipeline frameworks (dbt, Airflow, or equivalent)
• SQL — expert level; complex transformations, query optimization, schema design
• Front-End Development — React, JavaScript/TypeScript, REST API integration, and Streamlit for rapid AI/ML application prototyping and internal tooling
• Solid understanding of ML lifecycle — feature stores, training pipelines, inference data patterns
• Cloud-native experience on AWS, Azure, or GCP
• Data quality and observability tooling

Nice to Have

• Hands-on experience with MLflow, Feast, LangChain, or LlamaIndex
• Exposure to graph databases (Neo4j, Neptune, or equivalent)
• Exposure to vector databases (Pinecone, Weaviate, pgvector, or equivalent)
• Experience with streaming pipelines (Kafka, Kinesis, Spark Structured Streaming)
• Familiarity with LLM evaluation frameworks and dataset benchmarking

Expected Base Pay Range (USD)

105,200 - 157,600, $ 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.