Scientist 3, Data Science (Machine Learning Engineer) at Sandisk

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 maintainabilityMachine 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 systemsSystems, 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 pipelinesFull 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