Senior Engineer, Machine Learning

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

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.

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.

Requirements

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; 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; 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; 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; 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.

Nice to have

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.