Gpu Training Optimization Engineer Job at Randstad (Shanghai)

Ai/ml Performance Engineer - Gpu Optimization

As an AI Performance Engineers you will focus on pushing machine learning worklo...

Location

Finland , Helsinki

Salary:

Not provided

AMD

Expiration Date

Until further notice

Requirements

Experience with profiling, debugging, benchmarking, and optimization tools
Familiarity with ML frameworks (e.g., PyTorch, JAX, TF) and inference serving frameworks (e.g., vLLM, SGLang)
Strong C++ and/or Python skills, along the basics: unix, git, terminal, debugging, testing, thinking
Experience with Docker, container orchestration (Kubernetes), and job schedulers (Slurm)
Ability to work independently and collaboratively in a multi-cultural team
Excellent communication skills in a fast-moving environment
BSc, MSc, PhD or equivalent experience in Computer Science, Electrical Engineering or a related field

Job Responsibility

Explore and benchmark ML models and workloads (including diffusion models, LLMs, and multimodal systems) to identify bottlenecks across compute, memory, and networking layers
Optimize performance for inference and training on AMD GPUs, including parallelization strategies, quantization techniques, serving orchestration, network communication and distributed execution
Perform deep profiling to uncover inefficiencies in ML frameworks, data pipelines, compiler tools, and key tensor operations such GEMMs, Convs and Attention, to name a few
Support AMD top-tier customers to improve model throughput, reduce latency, and optimize resource utilization across multi-GPU and cluster environments
Work closely with hardware, compiler, and software teams to drive improvements across the full ROCm stack
Communicate performance bottlenecks, solutions, and optimization strategies to stakeholders
Work with international teams located across Europe, US and Asia

Senior GPU Software Performance Engineer — Post‑Training

Drive the performance of post‑training workloads on AMD Instinct™ GPUs. You’ll w...

Location

United States , San Jose

Salary:

204000.00 - 306000.00 USD / Year

AMD

Expiration Date

Until further notice

Requirements

Proven GPU performance engineering for deep learning (ROCm/HIP, Triton, or similar)
Hands-on with SFT. LoRA and RL-based training at scale
Strong PyTorch experience (torch.distributed, FSDP/ZeRO or equivalent)
Proficient in Python and C++
comfortable reading/writing kernels when needed
Experience with distributed systems and collective communication libraries
Track record of turning profiles into fixes, upstreaming changes, and documenting results

Job Responsibility

Lead performance for finetuning and RL training solutions on AMD GPUs
Improve throughput, memory efficiency, and stability across data, model, and optimizer steps
Optimize multi-GPU/multi-node training and communication patterns
Contribute efficient kernels/ops and targeted graph-level optimizations
Profile, diagnose, and resolve bottlenecks using standard tooling
prevent regressions in CI
Ship reproducible pipelines and documentation adopted by internal teams and external developers
Collaborate with framework, compiler, and model teams to land durable improvements

Fulltime

Principal ML Engineer - Large Scale Training Performance Optimization

We are looking for a Principal Machine Learning Engineer to join our Models and ...

Location

United States , San Jose; Bellevue

Salary:

226400.00 - 339600.00 USD / Year

AMD

Expiration Date

Until further notice

Requirements

Experience with ML/DL frameworks such as PyTorch, JAX, or TensorFlow
Experience with distributed training and distributed training frameworks, such as Megatron-LM, MaxText, TorchTitan
Experience with LLMs or computer vision, especially large models
Experience with GPU kernel optimization
Excellent Python or C++ programming skills, including debugging, profiling, and performance analysis at scale
Experience with ML infra at kernel, framework, or system level
Strong communication and problem-solving skills
A master's degree or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field

Job Responsibility

Train large models to convergence on AMD GPUs at scale
Improve the end-to-end training pipeline performance
Optimize the distributed training pipeline and algorithm to scale out
Contribute your changes to open source
Stay up-to-date with the latest training algorithms
Influence the direction of AMD AI platform
Collaborate across teams with various groups and stakeholders

Fulltime

Senior AI Infrastructure Engineer - Training Platform

As a Software Engineer on the Machine Learning Infrastructure team, you will bui...

Location

United States , San Francisco; Seattle; New York

Salary:

216000.00 - 270000.00 USD / Year

Scale

Expiration Date

Until further notice

Requirements

5+ years of experience in backend or infrastructure engineering, with at least 2 years focused on orchestrating ML workloads at scale (100+ GPU nodes)
Strong programming skills in one or more languages (e.g. Python, Go, Rust, C++)
Experience with complex compute management systems that cover queueing, quotas, preemption, and gang scheduling
Experience with distributed training infrastructure, such as EFA, Infiniband, and topology-aware scheduling
Experience with distributed storage systems (e.g. Lustre, S3) as they relate to training throughput
Expert-level knowledge of Kubernetes internals (Custom Resources, Operators, Admission Controllers) and how they interact with device plugins for specialized hardware
Familiarity with cloud infrastructure (AWS, GCP) and infrastructure as code (e.g., Terraform)
Proven ability to solve complex problems and work independently in fast-moving environments

Job Responsibility

Architect and scale a multi-tenant orchestration layer that abstracts away the complexity of GPU clusters, ensuring high utilization and seamless job recovery
Design and implement scheduling primitives to optimize the lifecycle of training jobs
Develop deep observability and automated health-checking into the training stack to proactively identify and isolate hardware failures
Evaluate and integrate emerging technologies in the CNCF and AI ecosystem (e.g. Ray, Kueue), making data-driven build vs. buy decisions that balance velocity with long-term maintainability
Work closely with Finance and Procurement teams to drive our capacity planning process
Participate in our team's on call process to ensure the availability of our services
Own projects end-to-end, from requirements, scoping, design, to implementation, in a highly collaborative and cross-functional environment

What we offer

Comprehensive health, dental and vision coverage
retirement benefits
a learning and development stipend
generous PTO
commuter stipend (may be eligible)

Fulltime

Member of Technical Staff - Distributed Training Engineer

Our Training Infrastructure team is building the distributed systems that power ...

Location

United States , San Francisco; Boston

Salary:

Not provided

Liquid AI

Expiration Date

Until further notice

Requirements

Hands-on experience building distributed training infrastructure (PyTorch Distributed DDP/FSDP, DeepSpeed ZeRO, Megatron-LM TP/PP)
Experience diagnosing performance bottlenecks and failure modes (profiling, NCCL/collectives issues, hangs, OOMs, stragglers)
Understanding of hardware accelerators and networking topologies
Experience optimizing data pipelines for ML workloads

Job Responsibility

Design and build core systems that make large training runs fast and reliable
Build scalable distributed training infrastructure for GPU clusters
Implement and tune parallelism/sharding strategies for evolving architectures
Optimize distributed efficiency (topology-aware collectives, comm/compute overlap, straggler mitigation)
Build data loading systems that eliminate I/O bottlenecks for multimodal datasets
Develop checkpointing mechanisms balancing memory constraints with recovery needs
Create monitoring, profiling, and debugging tools for training stability and performance

What we offer

Competitive base salary with equity in a unicorn-stage company
We pay 100% of medical, dental, and vision premiums for employees and dependents
401(k) matching up to 4% of base pay
Unlimited PTO plus company-wide Refill Days throughout the year

Fulltime

Staff Software Engineer, GPU Infrastructure (HPC)

The internal infrastructure team is responsible for building world-class infrast...

Location

Salary:

Not provided

Cohere

Expiration Date

Until further notice

Requirements

Deep expertise in ML/HPC infrastructure: Experience with GPU/TPU clusters, distributed training frameworks (JAX, PyTorch, TensorFlow), and high-performance computing (HPC) environments
Kubernetes at scale: Proven ability to deploy, manage, and troubleshoot cloud-native Kubernetes clusters for AI workloads
Strong programming skills: Proficiency in Python (for ML tooling) and Go (for systems engineering), with a preference for open-source contributions over reinventing solutions
Low-level systems knowledge: Familiarity with Linux internals, RDMA networking, and performance optimization for ML workloads
Research collaboration experience: A track record of working closely with AI researchers or ML engineers to solve infrastructure challenges
Self-directed problem-solving: The ability to identify bottlenecks, propose solutions, and drive impact in a fast-paced environment

Job Responsibility

Build and scale ML-optimized HPC infrastructure: Deploy and manage Kubernetes-based GPU/TPU superclusters across multiple clouds, ensuring high throughput and low-latency performance for AI workloads
Optimize for AI/ML training: Collaborate with cloud providers to fine-tune infrastructure for cost efficiency, reliability, and performance, leveraging technologies like RDMA, NCCL, and high-speed interconnects
Troubleshoot and resolve complex issues: Proactively identify and resolve infrastructure bottlenecks, performance degradation, and system failures to ensure minimal disruption to AI/ML workflows
Enable researchers with self-service tools: Design intuitive interfaces and workflows that allow researchers to monitor, debug, and optimize their training jobs independently
Drive innovation in ML infrastructure: Work closely with AI researchers to understand emerging needs (e.g., JAX, PyTorch, distributed training) and translate them into robust, scalable infrastructure solutions
Champion best practices: Advocate for observability, automation, and infrastructure-as-code (IaC) across the organization, ensuring systems are maintainable and resilient
Mentorship and collaboration: Share expertise through code reviews, documentation, and cross-team collaboration, fostering a culture of knowledge transfer and engineering excellence

What we offer

An open and inclusive culture and work environment
Work closely with a team on the cutting edge of AI research
Weekly lunch stipend, in-office lunches & snacks
Full health and dental benefits, including a separate budget to take care of your mental health
100% Parental Leave top-up for up to 6 months
Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement
Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend
6 weeks of vacation (30 working days!)

Fulltime

Research Scientist / Engineer – Training Infrastructure

Luma’s mission is to build multimodal AI to expand human imagination and capabil...

Location

United States , Palo Alto

Salary:

187500.00 - 395000.00 USD / Year

Luma AI

Expiration Date

Until further notice

Requirements

Extensive experience with distributed PyTorch training and parallelisms in foundation model training
Deep understanding of GPU clusters, networking, and storage systems
Familiarity with communication libraries (NCCL, MPI) and distributed system optimization

Job Responsibility

Design, implement, and optimize efficient distributed training systems for models with thousands of GPUs
Research and implement advanced parallelization techniques (FSDP, Tensor Parallel, Pipeline Parallel, Expert Parallel)
Build monitoring, visualization, and debugging tools for large-scale training runs
Optimize training stability, convergence, and resource utilization across massive clusters

Fulltime

New

Staff Machine Learning Engineer - ML Training Infrastructure

The Role:   We are seeking an experienced, technically strong, impact-driven ex...

Location

United States , Austin; Mountain View

Salary:

185000.00 - 335300.00 USD / Year

General Motors

Expiration Date

Until further notice

Requirements

Bachelor's degree or higher in Computer Science or a related field, or equivalent practical experience
8+ years of professional software engineering experience
5+ years of specialized experience in AI/ML infrastructure, such as enabling distributed training for large-scale ML models
Strong programming skills in Python, with deep proficiency in frameworks such as PyTorch (preferred), TensorFlow, or similar ML systems
Proven experience designing and operating distributed systems for ML training, including distributed computing, GPU computing, and cloud environments (AWS, GCP, Azure)
Demonstrated track record of leading technically ambiguous, cross-team infrastructure initiatives and driving them to measurable impact
Strong architectural judgment and ability to make sound technical tradeoffs across performance, reliability, usability, and cost
Willingness to travel to Sunnyvale, CA as needed
Comfortable operating in highly ambiguous and dynamic environments

Job Responsibility

Define and drive the architecture, design, and development of scalable, reliable, and high-performance ML frameworks and platform capabilities to support model training at scale
Lead model training performance analysis and optimization efforts across distributed training workflows, improving scalability, efficiency, and cost across heterogeneous hardware environments
Raise the bar on system observability, debuggability, operational excellence, and developer experience across the ML training stack
Own large, ambiguous, cross-functional technical initiatives from strategy through execution, including technical roadmap definition, tradeoff analysis, and delivery
Influence platform direction by identifying long-term infrastructure investments, setting engineering standards, and driving adoption of best practices across teams
Collaborate across organizational boundaries to align requirements, resolve technical disagreements, and integrate new capabilities into the platform ecosystem
Mentor engineers through design reviews, technical guidance, and hands-on partnership, while elevating engineering quality across the team

What we offer

medical
dental
vision
Health Savings Account
Flexible Spending Accounts
retirement savings plan
sickness and accident benefits
life insurance
paid vacation & holidays
tuition assistance programs

Fulltime

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Gpu Training Optimization Engineer

Job Description

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