Senior Engineer, Machine Learning

SanDisk is a leading global provider offlash memory and solid‑state storage solutions, designing and manufacturing products such asSSDs, memory cards, and USB flash drives for consumer, mobile, and enterprise applications. Founded in1988, the company has been a pioneer in flash technology, including the creation of thefirst flash‑based SSD in 1991.

Formerly part of Western Digital (2016–2025), SanDisk re‑emerged as anindependent publicly traded company in 2025, strengthening its focus on next‑generation storage technologies. It remains one of theworld’s largest suppliers of NAND flash memory

Role Overview

We are looking for a highly skilled Machine Learning Engineer who can design, build, and own end-to-end ML systems in production. This role requires a strong blend of machine learning expertise, backend engineering, and full-stack development, with a focus on building reliable, scalable platforms used by leadership and critical business functions.

Key Responsibilities

• Design, develop, and maintainend-to-end machine learning pipelines, including data ingestion, training, evaluation, deployment, monitoring, and retraining.
• Build and ownproduction-grade ML services that are reliable, scalable, and fault-tolerant.
• Architect and manageasync workflows and API-driven systems for ML and data services.
• Integrate ML solutions intocomplex production environments and distributed systems.
• Design robust systems with a strong focus onfailure modes, observability, and guardrails to ensure reliability.
• Develop internal analytical tools used byleadership and cross-functional teams for decision-making.
• Developinteractive internal ML tools and dashboards using Streamlit for model insights, monitoring, and experimentation.
• Experience with cloud platforms (AWS, GCP, Azure).
• Collaborate with data scientists and stakeholders to deliver impactful solutions.

Required Skills & Qualifications

Core Engineering Skills

• Strong proficiency inPython,SQL, and buildingRESTful APIs
• Experience withasynchronous programming and workflows
• Solid understanding ofsoftware engineering best practices: Version control (bitbucket), Unit and integration testing, Code quality and maintainability

Machine Learning & MLOps

• Build or integratedata ingestion pipelines (batch or streaming)
• Experience in performing EDA and understand the analysis.
• Proven experience managing thefull ML lifecycle.
• Hands-on experience withMLOps practices and tools:
  • Experiment tracking
  • Model versioning
  • Automated training and deployment pipelines
  • CI/CD for ML systems

Systems, Infrastructure & Orchestration

• Experience buildingscalable and reliable ML systems in production
• Familiarity with:
  • Containerization (Docker)
  • Orchestration platforms (e.g., Kubernetes, Airflow, Prefect, Dagster)
  • Infrastructure as Code (IaC)
• Experience withdistributed data processing systems (e.g., Spark)
• Understanding ofworkflow orchestration and scheduling for ML pipelines

Full Stack Development

• Experience developingend-to-end applications, including:
  • Backend pipelines and services
  • Frontend/UI components
• Hands-on experience buildinginternal ML dashboards and tools using Streamlit
• Ability to createintuitive interfaces for monitoring models, exploring data, and enabling stakeholder interaction

Required Qualifications

• Master’s or PhD in Statistics, Data Science, Computer Science, or a related quantitative field.
• 3–4+ years of experience in data science or machine learning pipeline.
• Strong expertise in statistical analysis and machine learning techniques.
• Proficiency in:
  • Python (pandas, numpy, scikit-learn, statsmodels)
  • SQL
  • Data visualization tools
• Experience working with large-scale operational datasets.

Preferred Qualifications

• Experience working with Databricks or AzureML.
• Familiarity with big data technologies (Spark, PySpark).
• Experience working with cloud platforms (AWS, Azure, or GCP).
• Knowledge of MLOps practices and model deployment frameworks.

All your information will be kept confidential according to EEO guidelines.