Scientist 3, Data Science (Machine Learning Engineer) job opportunity at SanDisk.



Date2026-05-06T13:09:08.828Z bot
SanDisk Scientist 3, Data Science (Machine Learning Engineer)
Experience: General
Pattern: Full-time
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Data Science (Machine Learning Engineer)

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degreeGeneral
loacation Bengaluru, Karnataka, India
loacation Bengaluru, Kar..........India
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Job DescriptionRole OverviewWe 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 ResponsibilitiesDesign, 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 & QualificationsCore Engineering SkillsStrong proficiency in Python, SQL, and building RESTful APIsExperience with asynchronous programming and workflowsSolid understanding of software engineering best practices: Version control (bitbucket), Unit and integration testing, Code quality and maintainabilityMachine Learning & MLOpsBuild 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 trackingModel versioningAutomated training and deployment pipelinesCI/CD for ML systemsSystems, Infrastructure & OrchestrationExperience building scalable and reliable ML systems in productionFamiliarity 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 pipelinesFull Stack DevelopmentExperience developing end-to-end applications, including:Backend pipelines and servicesFrontend/UI componentsHands-on experience building internal ML dashboards and tools using StreamlitAbility to create intuitive interfaces for monitoring models, exploring data, and enabling stakeholder interaction

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