Senior ML Performance Engineer

Amadeus Search; San Francisco (CA); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive salary

Equity and bonus opportunities

Medical, dental, and vision coverage

Retirement savings plan

Additional wellness benefits

Job summary

A leading AI infrastructure company is seeking a Senior ML Performance Engineer to design a comprehensive performance testing platform for large language models. This role requires a minimum of 7 years in performance engineering and strong experience with GPU programming and ML inference workloads. Candidates should have expertise in Python and C/C++. The position offers competitive compensation, equity, and wellness benefits in a hybrid work environment.

Qualifications

• 7+ years in performance engineering or benchmarking roles.
• Strong knowledge of ML inference workloads.
• Experience building performance testing infrastructure from scratch.

Responsibilities

• Design and implement a performance testing platform for LLM inference workloads.
• Define benchmarking methodologies and metrics.
• Collaborate with engineers to integrate performance testing.

Skills

Performance engineering

Benchmarking

ML inference

Python

C/C++

GPU optimization

Analytical skills

Tools

CUDA

ROCm

PyTorch

TensorFlow

ONNX Runtime

Job description

Position: Senior ML Performance Engineer
Location: SF Bay Area (US) or Toronto (Canada) – Hybrid
Employment Type: Full-Time
Industry: AI Infrastructure / Compiler Systems

Overview

A venture-backed AI infrastructure company is building a high-performance, portable compiler designed to let developers “build once, deploy anywhere.” This includes cloud, edge, and hybrid environments — all optimized for resource efficiency, scalability, and sustainable AI development.

The team is looking for a Senior ML Performance Engineer to architect and lead a Performance Testing Platform from the ground up, measuring and optimizing the performance of large language models (LLMs) before and after compiler optimization on modern GPU architectures.

This role sits at the intersection of ML systems, GPU architecture, and performance engineering, with high visibility into product quality and customer impact.

Key Responsibilities

• Design and implement a comprehensive performance testing platform for LLM inference workloads across GPU clusters

• Define benchmarking methodologies, metrics, and test suites (latency, throughput, memory utilization, power consumption, and model accuracy)

• Establish baseline performance for unoptimized models and validate post-optimization improvements

• Build automated pipelines for continuous performance validation across compiler releases and model updates

• Investigate performance bottlenecks using GPU profilers and system-level monitoring

• Collaborate with compiler engineers, ML engineers, and DevOps to integrate performance testing into development workflows

• Create dashboards and reporting to track performance trends, regressions, and wins

• Document best practices for GPU-based ML performance testing

Required Qualifications

• 7+ years in performance engineering, benchmarking, or systems engineering roles

• Strong knowledge of ML inference workloads, particularly transformer-based LLMs

• Hands-on GPU programming and optimization experience (CUDA, ROCm, or similar)

• Strong programming skills in Python and C/C++

• Proven experience building performance testing infrastructure or benchmarking platforms from scratch

• Experience with ML frameworks: PyTorch, TensorFlow, ONNX Runtime, vLLM, TensorRT-LLM

• Proficiency with profiling and debugging GPU workloads

• Experience with CI/CD systems and test automation frameworks

• Strong analytical skills with the ability to design experiments, analyze results, and communicate findings clearly

Nice to Have

• AMD GPU experience (Mi200/Mi300) and ROCm ecosystem

• Compiler optimization knowledge

• Distributed inference and multi-GPU workloads

• ML model quantization, pruning, and optimization techniques

• High-performance computing or systems-level optimization

• Infrastructure-as-code experience: Kubernetes, Docker, Terraform

• Contributions to open-source ML or systems projects

Personal Attributes

• Detail-oriented — able to spot subtle regressions

• Self-driven and accountable

• Collaborative and team-oriented

• Passionate about sustainable AI

• Clear and effective communicator

Compensation & Benefits

• Competitive salary, dependent on experience and location

• Equity and bonus opportunities

• Medical, dental, and vision coverage

• Retirement savings plan

• Additional wellness benefits

Why This Role Is Unique

• Build the infrastructure that validates high-performance ML models

• Influence core product quality and customer outcomes

• Work in a highly technical, high-impact environment at the forefront of AI systems

• Collaborate across a globally distributed team