Design, build, deploy, and operate ML systems that run in real environments and scale with the business at Palo Alto Office
Requirements
- 8+ years in software engineering with deep, hands-on ML experience.
- Proven experience building and running ML systems in production, not prototypes.
- Strong foundation in applied machine learning, statistics, and system design.
- Experience with ML frameworks such as PyTorch, TensorFlow, or equivalent.
- Experience with data pipelines, feature stores, model serving, and distributed training.
- Python, SQL
- PyTorch or TensorFlow
Responsibilities
- Design the ML system architecture across data ingestion, feature engineering, training, evaluation, deployment, and inference.
- Build and operate production ML pipelines with clear reliability, performance, and cost constraints.
- Own model lifecycle management including retraining, monitoring, drift detection, and rollback.
- Partner closely with product and engineering to translate business problems into ML systems that ship.
- Make architectural decisions around tooling, infrastructure, and tradeoffs with long-term ownership.
- Contribute code directly across Python, ML frameworks, and ML infrastructure components.
- Operate in startup or scale-up conditions with high autonomy and accountability.
Other
- Full-time employment
- Hybrid at Palo Alto Office
- Startup or early-stage experience where systems were built from scratch or scaled rapidly.
- Strong tenure signal with meaningful ownership at prior companies
- Not a research scientist role, not a pure data scientist role, not a people-management-first role, not a short-term advisory position
