Company Description

SanDisk is a leading global provider of flash memory and solid‑state storage solutions, designing and manufacturing products such as SSDs, memory cards, and USB flash drives for consumer, mobile, and enterprise applications. Founded in 1988, the company has been a pioneer in flash technology, including the creation of the first flash‑based SSD in 1991.

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

Job Description

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 maintain end-to-end machine learning pipelines, including data ingestion, training, evaluation, deployment, monitoring, and retraining.
Build and own production-grade ML services that are reliable, scalable, and fault-tolerant.
Architect and manage async workflows and API-driven systems for ML and data services.
Integrate ML solutions into complex production environments and distributed systems.
Design robust systems with a strong focus on failure modes, observability, and guardrails to ensure reliability.
Develop internal analytical tools used by leadership and cross-functional teams for decision-making.
Develop interactive 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 in Python, SQL, and building RESTful APIs
Experience with asynchronous programming and workflows
Solid understanding of software engineering best practices: Version control (bitbucket), Unit and integration testing, Code quality and maintainability

Machine Learning & MLOps

Build or integrate data ingestion pipelines (batch or streaming)
Experience in performing EDA and understand the analysis.
Proven experience managing the full ML lifecycle.
Hands-on experience with MLOps practices and tools:

Experiment tracking
Model versioning
Automated training and deployment pipelines
CI/CD for ML systems

Systems, Infrastructure & Orchestration

Experience building scalable and reliable ML systems in production
Familiarity with:

Containerization (Docker)
Orchestration platforms (e.g., Kubernetes, Airflow, Prefect, Dagster)
Infrastructure as Code (IaC)

Experience with distributed data processing systems (e.g., Spark)
Understanding of workflow orchestration and scheduling for ML pipelines

Full Stack Development

Experience developing end-to-end applications, including:

Backend pipelines and services
Frontend/UI components

Hands-on experience building internal ML dashboards and tools using Streamlit
Ability to create intuitive interfaces for monitoring models, exploring data, and enabling stakeholder interaction

Qualifications

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.

Additional Information

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

Scientist 3, Data Science (Machine Learning Engineer)