Senior Engineer, Machine Learning

Role Overview: As a Machine Learning Engineer at SanDisk, you will be responsible for designing, building, and owning end-to-end ML systems in production. Your role will require a strong blend of machine learning expertise, backend engineering, and full-stack development. You will 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. - Collaborate with data scientists and stakeholders to deliver impactful solutions. - Experience with cloud platforms (AWS, GCP, Azure). Qualifications: - Masters or PhD in Statistics, Data Science, Computer Science, or a related quantitative field. - 34+ 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, and data visualization tools. - Experience working with large-scale operational datasets. Additional Information: All your information will be kept confidential according to EEO guidelines. Role Overview: As a Machine Learning Engineer at SanDisk, you will be responsible for designing, building, and owning end-to-end ML systems in production. Your role will require a strong blend of machine learning expertise, backend engineering, and full-stack development. You will 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. - Collaborate with data scientists and stakeholders to deliver impactful solutions. - Experience with cloud platforms (AWS, GCP, Azure). Qualifications: - Masters or PhD in Statistics, Data Science, Computer Science, or a related quantitative field. - 34+ 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, and data visualization tools. - Experience working with large-scale operational datasets. Additional Information: All your information will be kept confidential according to EEO guidelines.