Senior Machine Learning Engineer

Strativ Group; Palo Alto (CA); On-site; USD 600,000 - 1,000,000; Full time

Job summary

Strativ Group in Palo Alto is building a seed-stage AI infrastructure company focused on high-performance inference on TPUs and GPUs, with a longer-term goal of an agent cloud where models and agents can improve themselves. Engineers will work directly with founders to shape architecture and direction from day one.

The role covers ML systems, distributed systems and AI infrastructure with potential work on low-level serving or infrastructure for autonomous, self-improving workloads.

Qualifications

• Strong background in at least one area: ML systems/AI infrastructure, model inference/serving, distributed training/inference, GPU/TPU systems, scheduling/runtimes or compiler-level optimization.

Responsibilities

• Build and optimize production infrastructure for large-scale AI workloads.
• Work on ML systems, distributed systems and AI infrastructure.
• Own significant pieces of the system from day one.
• Focus on low-level serving and runtime performance or infrastructure for autonomous agent workloads.
• Improve latency, throughput and infrastructure costs.
• Enhance reliability, observability and deployment of AI workloads.

Job 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

What They Are Looking For

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

Compensation

• Total compensation: up to $1M+, combining cash and founding equity