Member of Technical Staff, ML Performance

Odyssey

. Optimize models used in real time by hundreds of thousands of users .

Posted 10/2/2026full-timePalo Alto • California • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in optimizing machine learning models for performance and efficiency, with a strong focus on distributed training strategies and GPU optimization. Proven ability to lead projects from conception to execution while collaborating with cross-functional teams to enhance model architectures.

Highest-signal resume keywords

Machine Learning Performance OptimizationDistributed Training StrategiesProficiency in PyTorchNVIDIA GPU EcosystemsEnd-to-End Project Ownership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning ArchitecturesPerformance Bottleneck IdentificationModel DevelopmentInference InfrastructureTechnical Decision-MakingMetric-Based Analysis

Soft Skills

Problem-Solving MindsetAbility to Acquire New Skills

Tools & Technologies

PyTorchTensorFlowJAXTritonNVIDIA GPU Optimization Stacks

Industry Keywords

Real-Time Model OptimizationLarge GPU ClustersPerformance Improvement Frameworks

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

Optimize models used in real time by hundreds of thousands of users
Design and implement distributed training strategies to reduce training time and resource consumption on large GPU clusters
Partner with ML researchers and engineers to ensure model architectures are highly performant from conception
Develop tools to identify performance bottlenecks and stability issues in training and serving environments
Pioneer approaches, frameworks, and system designs that improve performance across model development and inference infrastructure
Make autonomous technical decisions
Use the latest-generation GPUs

Requirements

What you’ll need

8+ years of software engineering experience, with significant work in ML performance
Deep insight into modern machine learning architectures, particularly distributed training and inference
Track record of owning projects end to end
Problem-solving mindset with the ability to acquire new skills as needed
Proficiency with PyTorch (or TF/JAX) and Triton
Proficiency with NVIDIA GPU ecosystems and optimization stacks
Highly metric-based