Staff ML Systems Engineer – Reliable Inference & Serving

Human Intuition Inc.; New York (NY); On-site; USD 140,000 - 195,000; Full time

Job summary

Human Intuition Inc. is building the autonomous company and seeking an experienced engineer to ensure dependable, efficient model serving behind agents and training rollouts. You will work on serving, routing, and evaluating performance metrics that matter for task completion.

The role emphasizes reliability, scalability, and cost-aware design, with collaboration across research and infrastructure teams to support evaluation and post-training workloads.

Qualifications

• Experience building and operating machine learning services or performance-sensitive distributed systems.
• Strong Python and understanding of model serving, accelerator memory, and concurrency.
• Hands-on experience with an inference engine or substantial production model-serving workloads.
• Ability to investigate performance bottlenecks and distinguish measured improvements from assumptions.
• Ownership of reliability, debugging, and clear operational documentation.

Responsibilities

• Build inference services and model routing with explicit reliability, latency, and cost targets.
• Profile representative agent workloads, including long contexts, tool calls, streaming, and concurrent requests.
• Improve throughput and resource use through scheduling, batching, caching, and informed deployment choices.
• Develop reproducible benchmarks that connect serving changes to task quality as well as speed and cost.
• Implement versioned rollouts, observability, capacity planning, and practical failure recovery.

Skills

ML services

Distributed systems

Python

Performance optimization

Reliability ownership

Education

Bachelor's degree in CS/Math/ML

Tools

Inference engines

Profiling tools

Observability

Job description

Human Intuition Inc. is building the autonomous company and seeking an experienced engineer to ensure dependable, efficient model serving behind agents and training rollouts. You will work on serving, routing, and evaluating performance metrics that matter for task completion.

The role emphasizes reliability, scalability, and cost-aware design, with collaboration across research and infrastructure teams to support evaluation and post-training workloads.