Founding Machine Learning Engineer

Strativ Group; Palo Alto (CA); On-site; USD 420,000 - 500,000; Full time

Benefits offered by this job

In-office Palo Alto

Equity: 1-2% founding equity

Significant ownership from day one

Job summary

Strativ Group in Palo Alto is building AI infrastructure for the next generation of agentic AI. You will design and optimize production infrastructure for large-scale AI workloads, focusing on TPU and GPU inference, distributed serving, and runtime performance.

As part of a small founding team, you will work directly with the founders, own significant parts of the infrastructure, and help shape the architecture and direction toward autonomous, self-improving models with minimal human supervision.

Qualifications

• Strong background in ML systems or AI infrastructure.
• Experience with model inference and serving.
• Experience with distributed training or inference.
• GPU and TPU systems experience.
• High-performance or low-latency distributed systems.

Responsibilities

• Build and optimize production infrastructure for large-scale AI workloads.
• Focus on TPU/GPU inference, distributed model serving and scheduling.
• Optimize runtime, latency, throughput, and infrastructure cost.
• Ensure reliability, observability and smooth deployment.
• Create infrastructure for autonomous and agentic workloads.

Skills

ML systems

Model inference

Distributed training

GPU/TPU systems

Low-latency distributed systems

Scheduling & runtimes

Job description

We are working with an early-stage AI infrastructure company in Palo Alto building the systems that will power the next generation of agentic AI.

The company is currently focused on high-performance TPU and GPU inference serving, with a broader vision to build an agent cloud for self-improving models and agents.

This is a genuinely early founding-team opportunity. You will work directly with the founders and have significant influence over both the technical architecture and the direction of the company.

The Role

You will build and optimize production infrastructure for large-scale AI workloads, with a focus on:

• High-performance TPU and GPU inference
• Distributed model serving and scheduling
• Runtime and systems optimization
• Latency, throughput and infrastructure cost
• Reliability, observability and deployment
• Infrastructure for increasingly autonomous and agentic workloads

The work sits at the intersection of ML systems, distributed systems and AI infrastructure. You could be working anywhere from low-level serving and runtime performance through to the infrastructure required for long-running, self-improving agent systems.

What They Are Looking For

You do not need experience across the entire stack, but you should have a strong background in one or more of:

• ML systems or AI infrastructure
• Model inference and serving
• Distributed training or inference
• GPU or TPU systems
• High-performance or low-latency distributed systems
• Scheduling, runtimes or compiler-level optimization

TPU experience is particularly valuable, although candidates with strong GPU infrastructure or inference backgrounds are equally relevant.

Why Join?

The company is still only a small founding team, so this is an opportunity to join before the engineering organization scales significantly.

You will have unusually high ownership, work directly alongside the founders and help define the infrastructure behind a much bigger ambition than simply building another inference platform.

The longer-term goal is infrastructure where models, agents and the systems they run on can continuously improve together with minimal human supervision.

Package

• Location: Palo Alto, in office
• Compensation: Exceptional packages, up to $500,000 cash
• Equity: Meaningful founding equity, typically around 1-2%
• Significant technical ownership from day one