Ai/ml Performance Engineer - Gpu Optimization Job at AMD (Helsinki)

Senior System Performance Engineer

Role As a Senior System Performance Engineer on GM's AV System Performance Tea...

Location

United States , Austin;Mountain View

Salary:

128700.00 - 261300.00 USD / Year

General Motors

Expiration Date

Until further notice

Requirements

Minimum 3+ years of relevant industry experience
Hands-on programming experience with C++ and Python
Strong understanding of computer architecture and system-level software fundamentals
Proven experience with performance profiling, analysis, tuning, and optimization
Experience developing or optimizing high-performance software, ideally for heterogeneous compute environments (e.g., GPUs, DSPs, or accelerators)
Familiarity with industry benchmarks and workloads (e.g., MLPerf)
Strong communication skills with the ability to influence technical decisions within a team or product area
Ability to lead projects through ambiguity and deliver results end to end
BS, MS in Computer Science or a related technical field (or equivalent practical experience)

Job Responsibility

Collaborate with performance leads and partner engineering teams to align on performance requirements, development practices, and improvement opportunities
Lead performance-focused engineering initiatives with moderate ambiguity and cross-team collaboration
Contribute to the roadmap for performance tooling, frameworks, and methodologies that support efficient and scalable AV software development
Evaluate and prototype new tools, techniques, and technologies to improve runtime performance and developer workflows
Design, implement, and maintain tools and automated systems that support performance analysis, debugging, and continuous monitoring
Apply and help improve performance engineering standards, processes, and best practices at the team level
Analyze software behavior, identify performance bottlenecks, and collaborate with product teams to propose and implement optimizations
Mentor junior engineers on performance profiling, optimization strategies, and engineering best practices

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

Principal Software Consultant - AI/ML Engineer

As an ML Team Lead, you will be responsible for leading the technical direction ...

Location

Pakistan , Lahore, Karachi, Islamabad

Salary:

Not provided

10Pearls

Expiration Date

Until further notice

Requirements

Bachelor's or Master's degree in computer science, Artificial Intelligence, Data Science, Software Engineering, or a related field
7+ years of professional software engineering experience with at least 5 years of hands-on experience building and deploying ML systems into production
Prior experience as a Tech Lead, Staff Engineer, or hands-on lead for AI/ML engineering teams
Strong expertise in classical machine learning domains such as forecasting, ranking, classification, and optimization
Hands-on experience building modern LLM and agentic AI systems including RAG pipelines, tool-using agents, multi-step workflows, and evaluation systems
Strong proficiency in Python and backend system development
Experience with ML frameworks such as PyTorch or TensorFlow
Strong understanding of scalable distributed systems, APIs, system integration, architecture design, and production engineering practices
Experience operating ML services at scale, including SLO management, monitoring, on-call practices, and incident response
Experience working with Kubernetes-based deployments, CI/CD pipelines, and modern cloud-native engineering practices

Job Responsibility

Lead the technical direction for the team’s ML and LLM systems, including architecture patterns, platform choices, evaluation frameworks, and engineering standards
Stay hands-on by designing and implementing complex ML and agentic AI systems, writing production-grade code, and leading through technical execution
Design, develop, and deploy scalable ML and LLM-powered applications and services in production environments
Build and optimize AI-powered solutions such as RAG systems, multi-step agents, AI assistants, chatbots, forecasting systems, ranking models, classification models, and optimization systems
Drive architecture and design reviews to ensure scalability, reliability, security, and maintainability of AI/ML systems
Own the technical roadmap for ML/LLM initiatives and translate business objectives into execution plans and scalable solutions
Collaborate closely with Product Managers, Engineers, Data Engineers, MLOps Engineers, QA Engineers, and cross-functional stakeholders to deliver business-aligned AI solutions
Establish engineering best practices for prompt engineering, model evaluation, regression testing, observability, and production readiness
Define and implement quality standards, evaluation suites, acceptance metrics, and regression plans for all AI/ML features
Ensure high availability, scalability, and resilience of tier-1 ML services through SLOs, monitoring, incident response, failover strategies, circuit breakers, and multi-zone deployments

Fulltime

AI Product Performance Engineer

WHAT YOU DO AT AMD CHANGES EVERYTHING. At AMD, our mission is to build great pro...

Location

China , Shenzhen

Salary:

Not provided

AMD

Expiration Date

Until further notice

Requirements

deep knowledge of Data Center AI workloads such as LLM, Generative AI, Recommendation, NLP, Video Analytics, and/or transformer
hands-on experiences with various AI models, end-to-end pipeline, industry framework / SDKs and solutions
GPU Architecture Mastery
Kernel Programming Expertise: Strong proficiency in C++ and parallel computing, with extensive hands-on experience in NVIDIA CUDA or AMD HIP kernel programming
Performance Engineering: Demonstrated ability to debug and profile complex GPU workloads
Systems Knowledge: Familiarity with asynchronous execution, stream management, and host-device memory transfers
Python DSLs & Triton: Experience implementing kernels using OpenAI Triton or other Python-based DSLs
Inference Engine Experience: Hands-on experience integrating custom kernels into large-scale inference frameworks such as vLLM, SGLang, or TensorRT-LLM
Deep Learning Frameworks: Familiarity with writing custom extensions or operators for PyTorch (C++/CUDA extensions)
Hardware Agnosticism: Experience porting kernels between NVIDIA and AMD architectures or working with cross-platform HPC libraries

Job Responsibility

High-Performance Kernel Development: Design, implement, and optimize high-performance GPU kernels for AI/ML workloads to maximize hardware utilization
Performance Optimization: Analyze and optimize kernel execution for latency and throughput, addressing bottlenecks in memory bandwidth, instruction latency, and thread divergence
Workload Analysis: Evaluate the end-to-end performance impact of individual kernels on full-stack AI models, ensuring that micro-optimizations translate to application-level speedups
Profiling & Tuning: Utilize advanced GPU profiling tools (e.g., ROCm Profiler, Pytorch Profiler) to identify performance cliffs, stall pipelines, and memory hierarchy inefficiencies
Architecture Adaptation: Tailor implementation strategies to leverage specific features of modern GPU architectures (e.g., Matrix Cores, HBM characteristics)
Framework Integration: Collaborate with software stack teams to expose optimized kernels within high-level frameworks and inference engines

What we offer

AMD benefits at a glance

Fulltime

Staff Software Engineer - AI/ML Infra

GEICO AI platform and Infrastructure team is seeking an exceptional Senior ML Pl...

Location

United States , Chevy Chase; New York City; Palo Alto

Salary:

115000.00 USD / Year

Geico

Expiration Date

Until further notice

Requirements

Bachelor’s degree in computer science, Engineering, or related technical field (or equivalent experience)
8+ years of software engineering experience with focus on infrastructure, platform engineering, or MLOps
3+ years of hands-on experience with machine learning infrastructure and deployment at scale
2+ years of experience working with Large Language Models and transformer architectures
Proficient in Python
strong skills in Go, Rust, or Java preferred
Proven experience working with open source LLMs (Llama 2/3, Qwen, Mistral, Gemma, Code Llama, etc.)
Proficient in Kubernetes including custom operators, helm charts, and GPU scheduling
Deep expertise in Azure services (AKS, Azure ML, Container Registry, Storage, Networking)
Experience implementing and operating feature stores (Chronon, Feast, Tecton, Azure ML Feature Store, or custom solutions)

Job Responsibility

Design and implement scalable infrastructure for training, fine-tuning, and serving open source LLMs (Llama, Mistral, Gemma, etc.)
Architect and manage Kubernetes clusters for ML workloads, including GPU scheduling, autoscaling, and resource optimization
Design, implement, and maintain feature stores for ML model training and inference pipelines
Build and optimize LLM inference systems using frameworks like vLLM, TensorRT-LLM, and custom serving solutions
Ensure 99.9%+ uptime for ML platforms through robust monitoring, alerting, and incident response procedures
Design and implement ML platforms using DataRobot, Azure Machine Learning, Azure Kubernetes Service (AKS), and Azure Container Instances
Develop and maintain infrastructure using Terraform, ARM templates, and Azure DevOps
Implement cost-effective solutions for GPU compute, storage, and networking across Azure regions
Ensure ML platforms meet enterprise security standards and regulatory compliance requirements
Evaluate and potentially implement hybrid cloud solutions with AWS/GCP as backup or specialized use cases

What we offer

Comprehensive Total Rewards program that offers personalized coverage tailor-made for you and your family’s overall well-being
Financial benefits including market-competitive compensation
a 401K savings plan vested from day one that offers a 6% match
performance and recognition-based incentives
and tuition assistance
Access to additional benefits like mental healthcare as well as fertility and adoption assistance
Supports flexibility- We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year

Fulltime

Staff Software Engineer - AI/ML Platform

GEICO AI platform and Infrastructure team is seeking an exceptional Senior ML Pl...

Location

United States , Chevy Chase; New York City; Palo Alto

Salary:

115000.00 USD / Year

Geico

Expiration Date

Until further notice

Requirements

Bachelor’s degree in computer science, Engineering, or related technical field (or equivalent experience)
8+ years of software engineering experience with focus on infrastructure, platform engineering, or MLOps
3+ years of hands-on experience with machine learning infrastructure and deployment at scale
2+ years of experience working with Large Language Models and transformer architectures
Proficient in Python
strong skills in Go, Rust, or Java preferred
Proven experience working with open source LLMs (Llama 2/3, Qwen, Mistral, Gemma, Code Llama, etc.)
Proficient in Kubernetes including custom operators, helm charts, and GPU scheduling
Deep expertise in Azure services (AKS, Azure ML, Container Registry, Storage, Networking)
Experience implementing and operating feature stores (Chronon, Feast, Tecton, Azure ML Feature Store, or custom solutions)

Job Responsibility

Design and implement scalable infrastructure for training, fine-tuning, and serving open source LLMs (Llama, Mistral, Gemma, etc.)
Architect and manage Kubernetes clusters for ML workloads, including GPU scheduling, autoscaling, and resource optimization
Design, implement, and maintain feature stores for ML model training and inference pipelines
Build and optimize LLM inference systems using frameworks like vLLM, TensorRT-LLM, and custom serving solutions
Ensure 99.9%+ uptime for ML platforms through robust monitoring, alerting, and incident response procedures
Design and implement ML platforms using DataRobot, Azure Machine Learning, Azure Kubernetes Service (AKS), and Azure Container Instances
Develop and maintain infrastructure using Terraform, ARM templates, and Azure DevOps
Implement cost-effective solutions for GPU compute, storage, and networking across Azure regions
Ensure ML platforms meet enterprise security standards and regulatory compliance requirements
Evaluate and potentially implement hybrid cloud solutions with AWS/GCP as backup or specialized use cases

What we offer

Comprehensive Total Rewards program that offers personalized coverage tailor-made for you and your family’s overall well-being
Financial benefits including market-competitive compensation
a 401K savings plan vested from day one that offers a 6% match
performance and recognition-based incentives
and tuition assistance
Access to additional benefits like mental healthcare as well as fertility and adoption assistance
Supports flexibility- We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year

Fulltime

Staff Software Engineer - AI/ML Infra

GEICO AI platform and Infrastructure team is seeking an exceptional Senior ML Pl...

Location

United States , Palo Alto

Salary:

90000.00 USD / Year

Geico

Expiration Date

Until further notice

Requirements

Bachelor’s degree in computer science, Engineering, or related technical field (or equivalent experience)
8+ years of software engineering experience with focus on infrastructure, platform engineering, or MLOps
3+ years of hands-on experience with machine learning infrastructure and deployment at scale
2+ years of experience working with Large Language Models and transformer architectures
Proficient in Python
strong skills in Go, Rust, or Java preferred
Proven experience working with open source LLMs (Llama 2/3, Qwen, Mistral, Gemma, Code Llama, etc.)
Proficient in Kubernetes including custom operators, helm charts, and GPU scheduling
Deep expertise in Azure services (AKS, Azure ML, Container Registry, Storage, Networking)
Experience implementing and operating feature stores (Chronon, Feast, Tecton, Azure ML Feature Store, or custom solutions)

Job Responsibility

Design and implement scalable infrastructure for training, fine-tuning, and serving open source LLMs (Llama, Mistral, Gemma, etc.)
Architect and manage Kubernetes clusters for ML workloads, including GPU scheduling, autoscaling, and resource optimization
Design, implement, and maintain feature stores for ML model training and inference pipelines
Build and optimize LLM inference systems using frameworks like vLLM, TensorRT-LLM, and custom serving solutions
Ensure 99.9%+ uptime for ML platforms through robust monitoring, alerting, and incident response procedures
Design and implement ML platforms using DataRobot, Azure Machine Learning, Azure Kubernetes Service (AKS), and Azure Container Instances
Develop and maintain infrastructure using Terraform, ARM templates, and Azure DevOps
Implement cost-effective solutions for GPU compute, storage, and networking across Azure regions
Ensure ML platforms meet enterprise security standards and regulatory compliance requirements
Evaluate and potentially implement hybrid cloud solutions with AWS/GCP as backup or specialized use cases

What we offer

Comprehensive Total Rewards program that offers personalized coverage tailor-made for you and your family’s overall well-being
Financial benefits including market-competitive compensation
a 401K savings plan vested from day one that offers a 6% match
performance and recognition-based incentives
and tuition assistance
Access to additional benefits like mental healthcare as well as fertility and adoption assistance
Supports flexibility- We provide workplace flexibility as well as our GEICO Flex program, which offers the ability to work from anywhere in the US for up to four weeks per year

Fulltime

Lead AI/ML Engineer

Location

India , Hyderabad & Pune

Salary:

Not provided

Fission Labs

Expiration Date

Until further notice

Requirements

Experience with cloud-based platforms (AWS, Azure), API integrations, and data models
Exposure to AI/ML-enabled platforms or decision-intelligence systems
Certifications: CBAP / PMI-PBA / Agile BA / SAFe Product Owner / Scrum Master
Experience in stakeholder training, change management, or workshop facilitation
At least 4-5 years in AI/ML infrastructure and large-scale training environments
Expert in AWS cloud services (EC2, S3, EKS, SageMaker, Batch, FSx, etc.) and familiar with Azure, GCP, and hybrid/multi-cloud setups
Strong knowledge of AI/ML training frameworks (PyTorch, TensorFlow, Hugging Face, DeepSpeed, Megatron, Ray, etc.)
Proven experience with cluster orchestration tools (Kubernetes, Slurm, Ray, SageMaker, Kubeflow)
Deep understanding of hardware architectures for AI workloads (NVIDIA, AMD, Intel Habana, TPU)
Expert knowledge of inference optimization techniques including speculative decoding, KV cache optimization (MQA/GQA/PagedAttention), and dynamic batching

Job Responsibility

Design, implement, and optimize end-to-end ML training workflows including infrastructure setup, orchestration, fine-tuning, deployment, and monitoring
Evaluate and integrate multi-cloud and single-cloud training options across AWS and other major platforms
Lead cluster configuration, orchestration design, environment customization, and scaling strategies
Compare and recommend hardware options (GPUs, TPUs, accelerators) based on performance, cost, and availability

What we offer

Opportunities for continuous learning and certification support
Collaborative and growth-oriented work culture
Competitive compensation and comprehensive benefits
Exposure to modern cloud and integration technologies

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

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Ai/ml Performance Engineer - Gpu Optimization

Job Description

Job Responsibility

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