ML Infrastructure Engineer — On-Site SF, GPU Pipelines

Objective Partners; San Francisco (CA); On-site; USD 180,000 - 250,000; Full time

Benefits offered by this job

Full medical, dental, vision coverage

Flexible PTO

Daily catered lunches

Commuter reimbursement

Job summary

Objective Partners in San Francisco is seeking a Machine Learning Infrastructure Engineer to architect and scale compute, training, and inference pipelines powering next-gen AI models.

You will own distributed systems across large GPU clusters, collaborate with core platform teams, and drive low-latency serving with high reliability in a fast-paced startup environment.

The role is full-time and on-site, with comprehensive health benefits, flexible PTO, catered lunches, and commuter reimbursement.

Qualifications

• 3+ years of production experience in software development, backend systems, or ML platforms.
• Advanced proficiency in Python and strong background in low-level systems design.
• Hands-on experience with container orchestration (Kubernetes) and distributed compute frameworks.
• Ability to own projects from planning to deployment.
• Comfort in a fast-paced, high-rigor environment with rapid iteration.

Responsibilities

• Architect and sustain high-throughput pipelines for rapid experimentation and ultra-low latency serving.
• Establish benchmarking suites to identify bottlenecks and optimize resources.
• Evaluate model optimization developments and integrate them into production.
• Build multi-node environments that support parallelized training.
• Maximize hardware efficiency across large GPU clusters.
• Develop tooling and telemetry to streamline research-to-production transitions.

Skills

Python

Distributed systems

Backend development

First principles thinking

Fast-paced environments

Tools

Kubernetes

Docker

GPU clusters

Telemetry tooling

Job description

Objective Partners in San Francisco is seeking a Machine Learning Infrastructure Engineer to architect and scale compute, training, and inference pipelines powering next-gen AI models.

You will own distributed systems across large GPU clusters, collaborate with core platform teams, and drive low-latency serving with high reliability in a fast-paced startup environment.

The role is full-time and on-site, with comprehensive health benefits, flexible PTO, catered lunches, and commuter reimbursement.