Machine Learning Systems Engineer

Strativ Group; Palo Alto (CA); On-site; USD 450,000 - 550,000; Full time

Benefits offered by this job

Founding equity

Direct exposure to founders

Competitive compensation

Job summary

Strativ Group in Palo Alto is building production infrastructure for large-scale AI workloads, focusing on high-performance TPU and GPU inference, distributed model serving, and autonomous agent systems.

You will own critical components from day one, work directly with the founders, and influence the architectural direction and long-term strategy while optimizing latency, throughput, and cost. This is a genuinely early founding-team opportunity with meaningful equity.

Qualifications

• Background in ML systems or AI infrastructure required or highly desirable.
• Experience with model inference or serving.
• Background in distributed training or inference.
• Experience with GPU/TPU systems and low-latency distributed systems.
• Familiarity with scheduling, runtimes, or compiler-level optimization.

Responsibilities

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

Skills

ML systems

AI infrastructure

Model inference

Distributed training

GPU systems

TPU systems

Low-latency systems

Scheduling

Runtime optimization

Compiler optimization

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.

About the Role

You will build and optimize production infrastructure for large-scale AI 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.

Responsibilities

• 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 optimizationLatency, 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.

Required Skills

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
• 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.