AI Infrastructure Engineer

Jobtailor; San Francisco (CA); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Jobtailor is seeking a highly capable ML Infrastructure Engineer to build and operate systems for large-scale model training and evaluation in our SF office. You will enhance reliability, throughput, and resource efficiency, develop shared inference platforms, optimize GPU scheduling, and collaborate with researchers and engineers to drive end-to-end performance.

Candidates should bring strong software fundamentals and experience with distributed systems; hybrid work from the US office is

Qualifications

• Strong software engineering fundamentals.
• Experience building or operating large-scale distributed systems.
• Experience in ML infrastructure, inference systems, GPU performance, or infrastructure tooling.
• Highly self-motivated and comfortable taking ownership of open-ended problems.
• Enjoy debugging across system boundaries.
• Use measurements to guide improvements in performance and reliability.
• Ability to work from the US office three days per week.
• Authorization to work in the country where the job is located.
• Must disclose whether employment visa sponsorship is required.

Responsibilities

• Build and operate infrastructure for large-scale training and evaluation
• Improve reliability, throughput, and resource efficiency
• Develop shared inference and grading platforms with automated capacity management, health monitoring, and performance visibility
• Improve compute scheduling and resource allocation to reduce idle GPU time
• Help workloads recover quickly from failures
• Diagnose bottlenecks across training, inference, and orchestration
• Work across teams to improve end-to-end performance
• Build self-service tools, automated validation, and observability
• Help researchers launch experiments, diagnose issues, and compare results with less manual intervention
• Own projects from identifying bottlenecks and designing solutions through deployment and operation
• Collaborate closely with researchers and engineering teams

Skills

Distributed Systems

ML Infrastructure

GPU Performance

Performance Measurement

Debugging Across Boundaries

Ownership

Self-Motivated

Collaboration

Compute Scheduling

Resource Allocation

Observability

Automated Validation

Capacity Management

Health Monitoring

Job description

• Build and operate infrastructure for large-scale training and evaluation
• Improve reliability, throughput, and resource efficiency
• Develop shared inference and grading platforms with automated capacity management, health monitoring, and performance visibility
• Improve compute scheduling and resource allocation to reduce idle GPU time
• Help workloads recover quickly from failures
• Diagnose bottlenecks across training, inference, and orchestration
• Work across teams to improve end-to-end performance
• Build self-service tools, automated validation, and observability
• Help researchers launch experiments, diagnose issues, and compare results with less manual intervention
• Own projects from identifying bottlenecks and designing solutions through deployment and operation
• Collaborate closely with researchers and engineering teams

Requirements

• Strong software engineering fundamentals
• Experience building or operating large-scale distributed systems
• Experience in ML infrastructure, inference systems, GPU performance, or infrastructure tooling
• Highly self-motivated and comfortable taking ownership of open-ended problems
• Enjoy debugging across system boundaries
• Use measurements to guide improvements in performance and reliability
• Ability to work from the US office three days per week
• Authorization to work in the country where the job is located
• Must disclose whether employment visa sponsorship is required

Core Competencies

Demonstrates expertise in building and operating large-scale distributed systems, with a focus on ML infrastructure and GPU performance optimization. Capable of diagnosing bottlenecks and improving system reliability and throughput through effective collaboration and ownership of projects.

Highest-signal resume keywords

• Large-Scale Distributed Systems
• ML Infrastructure
• GPU Performance Optimization
• Performance Measurement and Improvement
• Debugging Across System Boundaries

Hard Skills

• Software Engineering Fundamentals
• Infrastructure Tooling
• Capacity Management
• Health Monitoring
• Performance Visibility
• Compute Scheduling
• Resource Allocation
• Automated Validation
• Observability
• Experiment Launching

Soft Skills

• Self-Motivated
• Ownership of Problems
• Collaboration
• Problem-Solving

Industry Keywords

• Training and Evaluation Infrastructure
• Resource Efficiency
• Idle GPU Time Reduction
• Workload Recovery
• End-to-End Performance