Gpu Computing

Gpu Computing Engineer Jobs

9 jobs found

6d ago

Software Engineer

Robotics Technologies Irving, TX

Irving, TX

6d ago

Full time Ai Engineer Applied Ai Engineer Artificial Intelligence

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Job Description

Robotics Technologies is seeking a Lead Software Engineer to drive performance and scalability across simulation foundation platforms, GPU computing, and agentic AI. This onsite role in Irving, TX focuses on optimizing workloads from NVIDIA Omniverse and Isaac Sim to distributed multi-GPU and containerized GPU execution.

Focus Areas

Simulation performance optimization across NVIDIA Omniverse and Isaac Sim
GPU computing performance engineering in distributed multi-GPU environments
Agentic AI systems for automated code analysis and runtime performance optimization
Operational scaling for containerized GPU workloads and GPU cloud infrastructure

Key Responsibilities

Analyze and optimize workloads across CPU, GPU, memory, I/O, storage, and networking using NVIDIA Nsight tools
Refactor and optimize simulation codebases using CUDA, C++, and Python to improve hardware utilization and execution performance
Design and implement agentic AI systems for automated code analysis and runtime performance optimization
Optimize and scale simulation workloads using NVIDIA Omniverse and Isaac Sim across multi-GPU and distributed computing environments
Manage and optimize containerized GPU workloads using Docker, Kubernetes, and Slurm
Collaborate on GPU cloud infrastructure work, including CoreWeave, AWS, and Azure
Develop automated regression testing, observability, and performance benchmarking solutions
Partner with robotics, perception, and AI engineering teams to improve simulation performance and scalability

Required Qualifications

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline
Minimum 7+ years of relevant software engineering experience
Strong programming proficiency in C++ and Python
Deep expertise in CUDA programming, GPU performance optimization, and NVIDIA GPU architecture
Strong experience with agentic AI and autonomous agents for automated code and runtime optimization
Hands-on experience with NVIDIA Omniverse, Isaac Sim, or equivalent simulation engines
Practical experience with NVIDIA Nsight tools, including Nsight Systems, Nsight Compute, and Nsight Graphics
Strong understanding of multi-GPU systems, parallel processing, and distributed computing
Experience with Linux, Docker, Kubernetes, and Slurm
Proven ability to benchmark, debug, and optimize compute-intensive workloads
Experience optimizing RTX and real-time rendering workloads
Background in robotics, autonomous systems, digital twins, or simulation platforms
Knowledge of High-Performance Computing (HPC) environments and large-scale GPU clusters
Experience with CI/CD pipelines and automated performance benchmarking frameworks
Familiarity with machine learning infrastructure, AI training, and synthetic data generation

Technology & Tools

Programming and acceleration: CUDA, C++, Python, NVIDIA GPU architecture
Simulation platforms: NVIDIA Omniverse, Isaac Sim
Profiling and analysis: NVIDIA Nsight, Nsight Systems, Nsight Compute, Nsight Graphics
Deployment and orchestration: Docker, Kubernetes, Slurm
Cloud and infrastructure: CoreWeave, AWS, Azure
Operating environment: Linux
Delivery and ML tooling: CI/CD, machine learning infrastructure, AI training, synthetic data generation
Rendering: RTX, real-time rendering workloads

Work Environment

CUDA, C++, Python, Agentic AI
NVIDIA Omniverse, NVIDIA Isaac Sim, NVIDIA Nsight
GPU Computing, Docker, Kubernetes, Slurm
Linux, CoreWeave, AWS, Azure

Location

Irving, TX (onsite)

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11d ago

GPU/FPGA Software Engineer

Everwatch Annapolis Junction, MD

Annapolis Junction, MD

11d ago

$60 - $105/hr Full time Data Processing Developer Engineering

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Job Description

Join Everwatch in Annapolis Junction, MD onsite and build end-to-end software that turns vision concepts into production-ready systems. This role combines acceleration pipeline engineering with modern tooling and an agile delivery mindset, helping teams design efficient, effective solutions for high-throughput, low-latency workloads.

What you will do

Develop complete software solutions from initial vision through production in an agile environment
Design and implement high-throughput, low-latency acceleration pipelines using FPGAs or GPUs
Apply current architectural approaches along with open-source frameworks and tools to deliver end-to-end systems to clients
Analyze the target environment to ensure solutions align with current architecture and operating constraints while supporting future enhancements
Identify and implement system improvements as designs evolve
Collaborate across system development efforts to drive progress

What you bring

Experience architecting and implementing high-throughput, low latency acceleration pipelines using FPGAs (RLT/HLS) or GPUs (CUDA/HIP)
Proficiency with C++, Python, and Docker
Strong understanding of GPU microarchitecture, including warp/wavefront scheduling, shared memory utilization, and occupancy optimization
FPGA experience with SystemVerilog, Verilog, or VHDL, plus C/C++ for HLS
Experience with AMD/Xilinx Vivado and Vitis, or Intel Quartus Prime
Experience integrating high-speed IPs, including PCIe Gen 4/5, DDR4/5, HBM, and 100G Ethernet
TS/SCI clearance with a polygraph
Bachelor’s degree and 20+ years of software engineering experience, or 24+ years of software engineering experience in lieu of a degree

Nice to have

Experience with Agentic AI
Experience with Machine Learning
Experience with Kubernetes or Docker Swarm
Experience with Agile

Security and clearance

Selected applicants will be subject to a security investigation and may need to meet eligibility requirements for access to classified information. TS/SCI clearance with polygraph is required.

Salary: USD 60 to 105 per hour.
Location: Annapolis Junction, MD onsite.

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18d ago

Generative AI Engineer - Graphics

Advanced Micro Devices, Inc Austin, TX

Austin, TX

18d ago

Full time Ai Image Generation Artificial Intelligence Deep Learning

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Job Description

Advanced Micro Devices, Inc is seeking an experienced AI/ML Engineer to help build and improve Generative AI capabilities for image generation and visual content. This role sits at the intersection of machine learning and high-performance GPU computing, with a focus on developing scalable training and inference pipelines and translating new research into AMD technologies.

Working in an on-site environment, you will contribute to diffusion-based learning and multimodal applications, while evaluating model quality, performance, and efficiency using modern benchmarks and techniques.

Responsibilities

Develop, train, fine-tune, and optimize Generative AI models for image generation and visual content creation.
Design and implement diffusion models, multimodal architectures, and advanced machine learning algorithms.
Build scalable training and inference pipelines that leverage high-performance GPU computing.
Evaluate model quality, performance, and efficiency using state-of-the-art techniques and benchmarks.
Collaborate with research, software, and hardware teams to integrate AI innovations into AMD technologies.
Optimize AI workloads for AMD GPU architectures and support performance analysis and improvements.
Stay current with advancements in Generative AI, image synthesis, diffusion models, and large-scale machine learning.

Requirements

Strong software development experience in Python and C++.
Deep understanding of machine learning, deep learning, and neural network architectures.
Experience developing and training diffusion models, image generation models, GANs, transformers, or multimodal AI systems.
Hands-on experience with PyTorch, TensorFlow, JAX, or similar machine learning frameworks.
Experience fine-tuning and deploying large-scale AI models.
Familiarity with distributed training, model optimization, and GPU acceleration techniques.
Experience building AI training and inference pipelines in production environments.
Knowledge of computer vision, image synthesis, and multimodal learning techniques.
Experience with MLOps, CI/CD workflows, Git, and modern software engineering practices.
Experience leveraging AI coding assistants, agentic AI systems, or automated development workflows.
Strong communication, collaboration, and problem-solving skills.

Education

Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Engineering, or a related field.

On-Site Locations

Austin, TX
Boston, MA
Orlando, FL
San Jose, California
Markham, ON, Canada

Technologies

Python, C++, PyTorch, TensorFlow, JAX
GANs, transformers
Git, MLOps, CI/CD
Distributed training, GPU acceleration, GPU computing

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21d ago

GPU Graphics Driver Software Engineer

Experis San Jose, CA

San Jose, CA

21d ago

$80 - $85/hr Full time Computer Graphics Device Driver Development Device Drivers

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Job Description

Experis is seeking a GPU Graphics Driver Software Engineer to help strengthen and maintain production-level graphics driver software. In this onsite role, you will collaborate with engineering teams on driver features, validate GPU functionality for next-generation hardware platforms, and improve performance through careful analysis and troubleshooting.

This position supports work across modern GPU and graphics technologies, with an emphasis on low-level systems programming and maintainable, production-ready implementation.

Role focus

Enhance and maintain sophisticated production-level graphics drivers
Collaborate with engineering teams to implement GPU driver features and resolve technical issues
Validate GPU functionality for next-generation hardware platforms
Analyze, troubleshoot, and optimize software performance
Develop high-quality, maintainable, and production-ready software solutions

Requirements

Bachelor’s degree or higher in Computer Engineering, Electrical Engineering, Computer Science, or a related discipline
5–7 years of software engineering experience with strong C/C++ programming skills
Experience in device driver development, embedded software, kernel development, or other low-level systems programming
Experience with OpenCL, CUDA, OpenGL, DirectX, and/or D3D technologies, along with GPU hardware programming
Knowledge of CPU/GPU architectures, data structures, algorithms, and scripting using Python, Perl, or Shell

Technologies

C/C++
OpenCL
CUDA
OpenGL
DirectX
D3D
Python
Perl
Shell

What’s in it for me

Opportunity to work on cutting-edge GPU and graphics technologies
Exposure to next-generation hardware platforms and innovations
Collaborative engineering environment with highly skilled technical teams
Challenging work involving performance optimization and low-level software development
Career growth within a technology-focused organization

Location: San Jose, CA (onsite)

Salary: USD 80–85 per hour

Compensation: $80–$85/hr on W2

Education: Bachelor’s degree or higher

Experience: 5 years minimum

Note: Responsibilities and validation work are described for next-generation hardware platforms in Austin, Tx.

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30d ago

Staff Machine Learning Engineer

Integral Ad Science, Inc. New York, NY

New York, NY

30d ago Closed

$135k - $232k/yr Full time Application Security Artificial Intelligence Automation

This position is no longer accepting applications (closed Nov 9, 2026).

Job Description

Integral Ad Science is seeking a Staff Machine Learning Engineer to join the Data Science Team in New York, NY. In this onsite role, you will provide technical leadership and operational ownership for large-scale machine learning systems and infrastructure supporting core AI/ML services.

Role Responsibilities

Lead the architectural vision and system design for core AI/ML-based services.
Serve as technical lead for complex, multi-quarter initiatives from inception through global deployment.
Design and build large-scale deep learning infrastructure and distributed training platforms focused on low latency and high availability at enterprise scale.
Collaborate with Product Management, Core Engineering, and executive stakeholders to align ML capabilities with business strategy.
Convert abstract product requirements into concrete technical designs.
Mentor senior and mid-level data scientists and engineers.
Define standard methodologies, set code quality expectations, and lead architecture design reviews.
Work with large-scale AI training infrastructure components including accelerators, network fabrics, CUDA, NCCL, and RDMA, along with big data ecosystems such as Databricks, Spark, Kubernetes, Kafka, and Prometheus.
Design, develop, and support robust CI/CD pipelines for AI/ML services to ensure smooth transitions from research to production endpoints.

Required Qualifications

PhD/Master’s degree in a technical field such as computer science, mathematics, statistics, or equivalent years of experience.
7+ years of machine learning experience in industry with a demonstrated record of operating at a Lead, Principal, or Staff level.
Deep expertise designing complex ML systems, including ranking infrastructures and data pipelines capable of handling massive throughput.
Production-hardened experience with frameworks including PyTorch, JAX, or TensorFlow.
Strong production-grade programming skills in Python, Go, Java, or C++, with a strong understanding of software design principles and algorithms.
Proven ability to operate in ambiguity and independently drive large, unstructured problems to technical resolution.
A track record of collaborating with, elevating, and mentoring engineering talent.

Technologies

PyTorch, JAX, TensorFlow
Python, Go, Java, C++
CUDA, NCCL, RDMA
Databricks, Spark, Kubernetes, Kafka, Prometheus

Compensation and Location

Location: New York, NY (onsite)

Salary: USD 135,100 - 231,600 per year

Benefits

Paid time off
Health insurance (medical, dental, vision)
PPO, HSA and FSA options
401k with employer matching contributions
Competitive compensation
Eligible for an annual bonus and/or other incentive plans

IAS Pay Transparency

Primary Location: US - New York, NY
Primary Location Base Pay Range: $135,100.00 - $231,600.00 Annual
Additional Locations: USA - Remote
Additional Locations Pay Range: 119,000.00 - 204,000.00 USD Annual

Agency / Third-Party Recruiters

IAS does not accept unsolicited resumes or candidate profiles.
If you are interested in becoming an IAS recruiting partner, send an email introducing your company to recruitingagencies@integralads.com.

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1mo ago

Systems Software Engineer

Micron Richardson, TX

Richardson, TX

1mo ago Closed

Full time C/c++ Cuda Gpu Computing

This position is no longer accepting applications (closed Nov 3, 2026).

Job Description

Micron’s Systems Software Engineering team is hiring a Principal Systems Software Engineer within the ITP Pathfinding organization’s Storage and Memory Systems (SMS) group. In this role, you will architect, design, and build software that helps validate and communicate the impact of future memory and storage technologies across AI applications, from hardware/software interfaces to inference workflows.

Role Summary

As a Principal Systems Software Engineer, you will enable proof-of-concept hardware capabilities by designing and evolving AI inference software stacks. You will also develop methods to quantify system feature value, lead architecture and development efforts, and contribute to research that identifies memory and storage system challenges and opportunities for AI workloads.

Responsibilities

Design and modify AI inference software stacks to enable proof-of-concept hardware capabilities.
Develop software to quantify and demonstrate the value of proposed system features.
Lead software architecture and development efforts independently and as a technical lead for small teams.
Research emerging AI software topics to identify memory and storage system challenges and opportunities.
Develop and modify Linux applications and libraries using C, C++, Python, Rust, CUDA, and/or SYCL for advanced heterogeneous computing systems.
Develop and use emulators to predict system-level performance and functional impacts before hardware availability.
Debug system-level issues across applications, libraries, operating systems, kernels, system configuration, networks, and hardware/software interfaces.
Use emerging AI tools to improve productivity.
Collaborate across Micron organizations and with external partners.
Mentor interns and earlier-career engineers.
Present technical findings, insights, and innovations to internal or external customers.

Required Qualifications

Bachelor’s or Master’s degree with 10+ years of experience, or a PhD with 7+ years of experience, in Computer Science, Software Engineering, Computer Engineering, or a related field.
Working knowledge of end-to-end AI inference software stack architectures, with hands-on development experience across layers such as frameworks, inference engines, ML compilers, compiler infrastructure, hardware runtimes, or hardware abstraction layers.
Experience debugging issues spanning applications, libraries, operating systems, kernels, networks, and hardware/software interfaces.
Proficient development skills in C, C++, and Python, with Linux-specific programming experience including multithreading, IPC, ioctl, GDB, and interaction with /proc and /sys filesystems.
Experience programming GPUs or other accelerators using CUDA, SYCL, OpenCL, or a comparable programming model.
Demonstrated ability to lead technical initiatives without supervisory authority.
Familiarity with LLM serving internals, including KV-cache management, prefill and decode phases, KV-cache offloading, and disaggregated serving.
Familiarity with inference engines and distributed serving frameworks such as SGLang, vLLM, NVIDIA Dynamo, or TensorRT-LLM.
Strong understanding of heterogeneous compute architecture concepts, including memory hierarchies, NUMA, caching, accelerator architectures, and interconnects such as PCIe, CXL, NVLink, or UALink.

Technologies

C, C++, Python, Rust
CUDA, SYCL, OpenCL
Linux, GDB
/proc, /sys
Multithreading, IPC, ioctl
KV-cache management, prefill and decode phases, KV-cache offloading, disaggregated serving
SGLang, vLLM, NVIDIA Dynamo, TensorRT-LLM
NUMA, PCIe, CXL, NVLink, UALink
Emulators, ML compilers

Preferred Qualifications

Technical leadership experience in a systems-focused pathfinding or research organization.
Technical knowledge of HBM, DRAM, and emerging memory technologies.
Technical knowledge of NVMe SSDs, NAND, and next-generation storage systems.

Location and Experience

Richardson, TX (onsite). Minimum experience: 7 years.

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1mo ago

Founding Developer Relations Engineer

Goaly Palo Alto, CA

Palo Alto, CA

1mo ago Closed

Full time Ai Developer Tooling Deep Learning Developer Relations

This position is no longer accepting applications (closed Nov 3, 2026).

Job Description

Goaly is coming out of stealth, and the company needs a technical voice who can make post-training performance tangible for the people running it day to day. As the first Developer Relations hire, you will build the benchmarks, tooling, and documentation that practitioners rely on, then distribute those artifacts across the channels where ML engineers actually learn and share.

What you will do

Build the signature technical artifacts, including a public post-training efficiency benchmark, reproducible before/after case studies (including doomed-run kills, silent throughput regressions, and dollars per eval point), and the open-source tools released around them.
Write conversion-focused docs such as quickstarts, reference documentation, and example integrations for the stacks users run, including PyTorch distributed, Megatron, FSDP, TorchTitan, Ray, Slurm, Kubernetes on GPUs, Weights & Biases (W&B), and MLflow. The goal is to help an engineer generate a read-only Retroactive Kill Report on their own cluster without needing Goaly’s involvement.
Create practitioner-ready technical content, including deep-dive engineering posts, benchmark write-ups, failure-mode teardowns (for example, “seven ways an RL run dies silently”), short demo videos, and live walkthroughs.
Own distribution by running Goaly’s technical presence across X, LinkedIn, GitHub, Hacker News, and the ML-infra Discord and Slack communities, plus deliver talks at venues users attend such as PyTorch Conference, MLSys, Ray Summit, GTC, and NeurIPS and ICML workshops, along with Bay Area AI infra meetups.
Act as a feedback loop: sit with design partners, reproduce their problems on Goaly’s stack, and deliver precise, prioritized engineering feedback. Convert repeated questions into docs, tools, or product changes.
Build community around the problem, not the product, through practitioner conversations, office hours, and a newsletter or series focused on learning about post-training efficiency.
Amplify the founders by turning the founding team’s Llama post-training infrastructure experience into content and talks, coordinating with the GTM team to strengthen both the design-partner pipeline and candidate pipeline.
Measure and improve by tracking reach, repo adoption, the docs funnel, inbound design-partner requests, and hires sourced. Double down on what works and stop what does not.

How the work is split

About half of the week is building: running real post-training workloads on Goaly’s stack, creating benchmark harnesses, building example integrations, and writing docs that take someone from zero to a first result in about thirty minutes.
About half of the week is distribution and relationship building: engineering posts, talks, open-source releases, and one-on-one conversations that lead to design partners and hires.

Who you are

3+ years as a software or ML engineer, with the expectation that you still write code every week.
A strong engineering background: you can stand up a distributed training job, read a profiler trace, and debug a CUDA OOM without needing help.
Hands-on experience with LLM post-training and/or the infrastructure underneath it, including SFT, RL, LoRA, DPO, or the stack beneath them such as PyTorch distributed, Megatron, DeepSpeed, FSDP, Ray, Slurm, and GPU Kubernetes.
A track record of explaining difficult topics clearly through blog posts, docs, conference talks, OSS READMEs, tutorials, or videos (links are expected).
Distribution you can point to, such as an audience on X, LinkedIn, YouTube, a newsletter, or GitHub, plus talks given, communities where people know your name, or evidence of building reach from zero in ML infrastructure and post-training.
Two-way communication: write content practitioners trust, and also listen to a frustrated infra engineer for an hour and return with the three engineering fixes that matter most.
A taste for substance: you would rather publish one reproducible benchmark than multiple hot takes.
Comfort being the public face of something early, even without a playbook, with messaging that shifts as the product evolves.

Technical stack you will work with

PyTorch distributed, Megatron, FSDP, TorchTitan, Ray, Slurm, Kubernetes on GPUs, Weights & Biases (W&B), MLflow
SFT, RL, LoRA, DPO, DeepSpeed
CUDA OOM, W&B, Nsight, PyTorch Profiler

Location and logistics

Palo Alto, CA (hybrid): 4+ days/week in office, with roughly 20% travel for conferences and customer visits.
Visa sponsorship: H-1B and OPT/CPT support available, with immigration counsel.
Perks: complimentary lunch, dinner, snacks, and drinks.

About the role, in practice

Goaly’s users are ML infra engineers and researchers who operate SFT, RL, and fine-tuning jobs on GPU clusters. They do not read marketing; they read benchmarks, repos, and documentation, and they rely on the engineer behind the artifacts. This role reports to the founders, works daily with engineering and the go-to-market team, and is explicitly founding-stage: you help shape the playbook while keeping output credible to people who train models.

How Goaly works

Mission first: choose work for impact and take responsibility for outcomes.
High agency: identify what is missing, form a plan, and move without waiting for perfect clarity.
Speed with rigor: ship, measure, and iterate while protecting correctness, safety, and reliability.
Flexible scope: cross team boundaries when it is the fastest path to the real problem.
Low ego, high standards: give direct feedback and adjust when evidence changes.
Continuous learning: the stack changes quickly, and you are willing to learn unfamiliar systems and domains.

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1mo ago

Data Scientist / Machine Learning Engineer

CorTech Auburn Hills, MI

Auburn Hills, MI

1mo ago Closed

$135k - $170k/yr Full time Artificial Intelligence Data Pipeline Data Science

1mo ago

AI Engineer 3

Capital One San Jose, CA

San Jose, CA

1mo ago Closed

$177k - $201k/yr Full time Agents Ai Engineer Artificial Intelligence

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