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# Senior Machine Learning Engineer

- **Company:** Strativ Group
- **Location:** Palo Alto, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Distributed Computing Environment, Distributed Systems, Machine Learning, AI Infrastructure, Graphics Processing Unit (GPU), Low Latency, Machine Learning Operations, Model Inference
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_51cde12aa81e1f1e432df4223f6a6559

## About the Role

You do not need experience across the whole stack. You should have a strong background in at least one of these areas:

 * ML systems or AI infrastructure
 * Model inference and serving
 * Distributed training or inference
 * GPU or TPU systems
 * Scheduling, runtimes or compiler-level optimization

## Description

We are working with a seed-stage AI infrastructure company in Palo Alto that is building optimization tools to change how models are served. Their current focus is high-performance inference on TPUs and GPUs, and the longer-term goal is an agent cloud where models and agents can improve themselves. The founding team is small, and the engineers who join now will work directly with the founders and shape both the architecture and the direction of the company.

About the Role

You will build and optimize the production infrastructure that runs large-scale AI workloads. The role covers ML systems, distributed systems and AI infrastructure, and the scope is wide. Depending on your interests, you might spend your time on low-level serving and runtime performance, or on the infrastructure that long-running, self-improving agents need to operate. You will own significant pieces of the system from your first day.

What You Will Work On

 * High-performance inference on TPUs and GPUs
 * Distributed model serving and scheduling
 * Runtime and systems optimization
 * Latency, throughput and infrastructure cost
 * Reliability, observability and deployment
 * Infrastructure for increasingly autonomous, agentic workloads