Senior ML Engineer (Data Scientist)

About the Role We're a Series A MLOps and enterprise AI platform company helping organizations deploy, manage, and monitor machine learning models at scale. Our Kubernetes-native infrastructure and model governance tooling are trusted by enterprise customers, and we're now investing heavily in predictive product simulations, agentic AI capabilities, and next-generation data science infrastructure. As a Senior ML Engineer (Data Scientist) , you'll help build and scale the data science foundation that powers these simulations. Working at the intersection of engineering and research, you'll improve data ingestion, experimentation frameworks, and model deployment pipelines — directly enabling data-driven product decisions. This is a hybrid role based in San Francisco, CA . What You'll Do Build and optimize data pipelines (ETL/ELT) across SQL/NoSQL systems, ensuring reliability and quality of large-scale event and log data. Apply statistical modeling, causal inference, and ML to analyze user behavior, design experiments, and generate actionable insights. Develop predictive, generative, and clustering models — including embeddings, anomaly detection, and time-series — to power simulations and personalization features. Collaborate with a multidisciplinary team of GenAI experts, behavioral scientists, and ML engineers to create synthetic personas and deliver customer-ready reports and presentations. Deploy and scale models in cloud environments (AWS, GCP, and/or Azure) using containerized workflows with Docker and Kubernetes. Design and maintain monitoring and evaluation pipelines to track model performance, detect drift, and ensure fairness and reproducibility. Scale data science infrastructure end-to-end — from ingestion pipelines through to experimentation frameworks. What We're Looking For Must-haves: 3+ years of experience as a Data Scientist or Machine Learning Engineer. Hands-on experience building and deploying ML models with PyTorch and TensorFlow ; strong proficiency in Python . Strong ML/DS fundamentals with the ability to translate research insights into product decisions. Required skills: Experience with distributed data processing frameworks such as Spark, Dask, or Ray . Proficiency with containerization and orchestration tools — Docker and Kubernetes . Proven experience deploying data science and ML workloads on cloud platforms ( AWS, GCP, and/or Azure ). Nice to have: Experience designing and implementing scalable data pipelines and experimentation frameworks. Background in causal inference, synthetic data generation, or behavioral modeling. Familiarity with model monitoring, drift detection, and explainability tooling. Location & Work Arrangement Location: San Francisco, CA (hybrid) Visa sponsorship: Not available Compensation & Benefits Compensation details were not provided for this role. We're happy to discuss salary expectations during the interview process.