Sandisk
Technology
SeniorEngineer,MachineLearning
Neural analysis suggests this role is
optimal for entry candidates.
“Senior Engineer, Machine Learning at Sandisk. Skills: Machine Learning, ML Systems, MLOps, Backend Engineering. Design end-to-end machine learning pipelines. Develop end-to-end machine learning pipelines”
Industry & Context.
Failure modes; Observability; Guardrails
What They're Looking For.
Must Have
Master's or PhD in Statistics, Data Science, Computer Science, or related quantitative field, 3–4+ years of experience in data science or machine learning pipeline, Expertise in statistical analysis and machine learning techniques, Proficiency in Python, Proficiency in pandas, Proficiency in numpy, Proficiency in scikit-learn, Proficiency in statsmodels, Proficiency in SQL, Proficiency in data visualization tools, Experience with large-scale operational datasets, Proficiency in Python, Proficiency in SQL, Proficiency in building RESTful APIs, Experience with asynchronous programming and workflows, Solid understanding of software engineering best practices, Experience with version control (bitbucket), Experience with unit and integration testing, Experience with code quality and maintainability, Build or integrate data ingestion pipelines, Experience in performing EDA, Understand the analysis, Proven experience managing the full ML lifecycle, Hands-on experience with MLOps practices and tools, Experience building scalable and reliable ML systems in production, Experience with distributed data processing systems, Understanding of workflow orchestration and scheduling for ML pipelines, Experience developing end-to-end applications, Experience building internal ML dashboards and tools using Streamlit, Ability to create intuitive interfaces
Nice to Have
PhD preferred, Experience working with Databricks, Experience working with AzureML, Familiarity with big data technologies (Spark, PySpark), Experience working with cloud platforms (AWS, Azure, or GCP), Knowledge of MLOps practices, Knowledge of model deployment frameworks
What You'll Do.
Design end-to-end machine learning pipelines
Develop end-to-end machine learning pipelines
Maintain end-to-end machine learning pipelines
Build production-grade ML services
Own production-grade ML services
Architect async workflows
Architect API-driven systems
Manage async workflows
Manage API-driven systems
Integrate ML solutions into production environments
Integrate ML solutions into distributed systems
Design robust systems
Develop internal analytical tools
Develop interactive internal ML tools
Develop dashboards using Streamlit
How You'll Work.
Team & Collaboration
Collaborate with data scientists; Collaborate with stakeholders
Full Job 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 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
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