Senior ML Engineer (Data Scientist)

About this role

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.

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