Software Engineer in GPU Networking & Distributed Systems

At Sciforium, we are at the forefront of AI infrastructure, innovating next-generation multimodal AI models and a proprietary high-efficiency serving platform. With substantial funding and direct collaboration from AMD, supported by their engineers, our team is rapidly expanding to develop the complete stack that powers cutting-edge AI models and real-time applications.About the RoleWe are on the lookout for a talented GPU Kernel Engineer who is eager to explore and maximize performance on modern accelerators. In this role, you will be responsible for designing and optimizing custom GPU kernels that drive our advanced large-scale AI systems. You will navigate the hardware-software stack, engaging in low-level kernel development and integrating optimized operations into high-level machine learning frameworks for large-scale training and inference.This position is perfect for someone who excels at the intersection of GPU programming, systems engineering, and state-of-the-art AI workloads, and aims to contribute significantly to the efficiency and scalability of our machine learning platform.Key ResponsibilitiesDevelop, implement, and enhance custom GPU kernels utilizing C++, PTX, CUDA, ROCm, Triton, and/or JAX Pallas.Profile and fine-tune the end-to-end performance of machine learning operations, particularly for large-scale LLM training and inference.Integrate low-level GPU kernels into frameworks such as PyTorch, JAX, and our proprietary internal runtimes.Create performance models, pinpoint bottlenecks, and deliver kernel-level enhancements that significantly boost AI workloads.Collaborate with machine learning researchers, distributed systems engineers, and model-serving teams to optimize computational performance across the entire stack.Engage closely with hardware vendors (NVIDIA/AMD) and stay updated on the latest GPU architecture and compiler/toolchain advancements.Contribute to the development of tools, documentation, benchmarking suites, and testing frameworks ensuring correctness and performance reproducibility.Must-Haves5+ years of industry or research experience in GPU kernel development or high-performance computing.Bachelor’s, Master’s, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, or a related discipline.Strong programming proficiency in C++, Python, and familiarity with machine learning frameworks.

ABOUT BASETENAt Baseten, we empower the world's leading AI firms—such as Cursor, Notion, and OpenEvidence—by delivering mission-critical inference solutions. Our unique blend of applied AI research, robust infrastructure, and user-friendly developer tools enables AI pioneers to effectively deploy groundbreaking models. With our recent achievement of a $300M Series E funding round supported by esteemed investors like BOND and IVP, we're on an exciting growth trajectory. Join our dynamic team and contribute to the platform that drives the next generation of AI products.THE ROLEWe are looking for an experienced Senior GPU Kernel Engineer to join our innovative team at the forefront of AI acceleration. In this role, your programming expertise will directly enhance the performance of cutting-edge machine learning models. You'll be responsible for developing highly efficient GPU kernels that optimize computational processes, allowing for transformative AI applications.You'll thrive in a fast-paced, intellectually challenging environment where your technical skills are pivotal. Your contributions will directly affect production systems that serve millions of users across various platforms. This position offers exceptional opportunities for career advancement for engineers enthusiastic about low-level optimization and impactful systems engineering.EXAMPLE INITIATIVESAs part of our Model Performance team, you will engage in projects like:Baseten Embeddings Inference: The quickest embeddings solution availableThe Baseten Inference StackEnhancing model performance optimizationRESPONSIBILITIESCore Engineering ResponsibilitiesDesign and develop high-performance GPU kernels for essential machine learning operations, including matrix multiplications and attention mechanisms.Collaborate with cross-functional teams to drive performance improvements and implement optimizations.Debug and refine kernel code to achieve maximal efficiency and reliability.Stay abreast of the latest advancements in GPU technology and machine learning frameworks.

At Gimlet Labs, we are pioneering the first heterogeneous neocloud tailored for AI workloads. As the demand for AI systems grows, traditional infrastructure faces significant limitations in terms of power, capacity, and cost. Our innovative platform addresses these challenges by decoupling AI workloads from the hardware, intelligently partitioning tasks, and directing each component to the most suitable hardware for optimal performance and efficiency. This method allows for the creation of heterogeneous systems that span multiple vendors and generations of hardware, including the latest cutting-edge accelerators, achieving substantial improvements in performance and cost-effectiveness.Building upon this robust foundation, Gimlet is developing a production-grade neocloud designed for agentic workloads. Our customers can effortlessly deploy and manage their workloads with stable, production-ready APIs, eliminating the complexities of hardware selection, placement, or low-level performance optimization.We collaborate with foundational labs, hyperscalers, and AI-native companies to drive real production workloads capable of scaling to gigawatt-class AI data centers.We are currently seeking a dedicated Member of Technical Staff specializing in kernels and GPU performance. In this role, you will work closely with accelerators and execution hardware to extract maximum performance from AI workloads across diverse and rapidly evolving platforms. You will analyze low-level execution behaviors, design and optimize kernels, and ensure consistent performance across both established and emerging hardware.This position is perfect for engineers who thrive on deep performance analysis, enjoy exploring hardware trade-offs, and are passionate about transforming theoretical peak performance into tangible real-world outcomes.

At Magic, our goal is to develop safe AGI that propels humanity forward by addressing some of the most pressing challenges we face. We are committed to harnessing the power of automated research and code generation to enhance models and improve alignment in ways that surpass human capabilities. Our innovative methodology integrates cutting-edge pre-training, domain-specific reinforcement learning, ultra-long context, and advanced inference-time computing.Role OverviewAs a Kernel Engineer, you will be responsible for the design, implementation, and maintenance of high-performance kernels, aiming to optimize throughput and minimize latency during both training and inference processes.Magic's extended context windows present unique kernel optimization challenges, particularly regarding memory efficiency, data movement, and sustained throughput.Key ResponsibilitiesDesign and develop kernels that facilitate high-performance long-context functionality.Take ownership of kernel design, implementation, deployment, and ensure production reliability.Emphasize robustness, thorough testing, and functional accuracy while striving for optimal performance.Assess the feasibility of porting Magic’s compute kernels to various hardware platforms.Collaborate with the training, inference, and reinforcement learning teams to co-design kernels.Explore our work through the Magic-Attention, presented at GTC 2026.QualificationsExperience in low-level programming for AI accelerators, including NVIDIA Blackwell or Google TPUs.Proficient in developing and optimizing GPU kernels using frameworks such as NCCL, MSCCLPP, CUTLASS, CuTeDSL, Triton, Quack, and Flash Attention.

At Inferact, we are on a mission to establish vLLM as the premier AI inference engine, significantly enhancing the speed and reducing the cost of AI inference. Our founders, the visionaries behind vLLM, have spent years bridging the gap between advanced models and cutting-edge hardware.About the RoleWe are seeking a skilled performance engineer dedicated to maximizing the computational efficiency of modern accelerators. In this role, you'll develop kernels and implement low-level optimizations that position vLLM as the fastest inference engine globally. Your contributions will be pivotal as your code will execute across a broad spectrum of hardware accelerators, from NVIDIA GPUs to the latest silicon innovations. You'll collaborate closely with hardware vendors to ensure we fully leverage the capabilities of each new generation of chips.

P-1285 About This Role Join our dynamic team at Databricks as a Staff Software Engineer specializing in GenAI Performance and Kernel. In this pivotal role, you will take charge of designing, implementing, and optimizing high-performance GPU kernels that drive our GenAI inference stack. Your expertise will lead the development of finely-tuned, low-level compute paths, balancing hardware efficiency with versatility, while mentoring fellow engineers in the intricacies of kernel-level performance engineering. Collaborating closely with machine learning researchers, systems engineers, and product teams, you will elevate the forefront of inference performance at scale. What You Will Do Lead the design, implementation, benchmarking, and maintenance of essential compute kernels (such as attention, MLP, softmax, layernorm, memory management) tailored for diverse hardware backends (GPU, accelerators). Steer the performance roadmap for kernel-level enhancements, focusing on areas like vectorization, tensorization, tiling, fusion, mixed precision, sparsity, quantization, memory reuse, scheduling, and auto-tuning. Integrate kernel optimizations seamlessly with higher-level machine learning systems. Develop and uphold profiling, instrumentation, and verification tools to identify correctness, performance regressions, numerical discrepancies, and hardware utilization inefficiencies. Conduct performance investigations and root-cause analyses to address inference bottlenecks, such as memory bandwidth, cache contention, kernel launch overhead, and tensor fragmentation. Create coding patterns, abstractions, and frameworks to modularize kernels for reuse, cross-backend compatibility, and maintainability. Influence architectural decisions to enhance kernel efficiency (including memory layout, dataflow scheduling, and kernel fusion boundaries). Guide and mentor fellow engineers focused on lower-level performance, conducting code reviews and establishing best practices. Collaborate with infrastructure, tooling, and machine learning teams to implement kernel-level optimizations in production and assess their impacts.

At Genmo, we are at the forefront of advancing artificial intelligence through innovative research in video generation. Our mission is to construct open, cutting-edge models that will ultimately contribute to the realization of Artificial General Intelligence (AGI). As part of our dynamic team, you will play a pivotal role in redefining the future of AI and expanding the horizons of video creation.We are looking for a skilled GPU Performance Engineer who can extract maximum performance from our H100 infrastructure and fine-tune our model serving stack to achieve unparalleled efficiency. If you are passionate about optimizing performance, particularly at the microsecond level, and thrive on pushing hardware to its limits, this is the perfect opportunity for you.Key ResponsibilitiesUtilize advanced profiling tools such as Nsight Systems and nvprof to analyze and enhance GPU workloads.Develop high-performance CUDA and Triton kernels to optimize essential model functions.Reduce cold start latency from seconds to mere milliseconds in our serving infrastructure.Optimize memory access patterns, implement kernel fusion, and maximize GPU utilization.Collaborate closely with machine learning engineers to optimize model implementations.Diagnose and resolve performance issues throughout the application and hardware stack.Implement custom memory pooling and allocation strategies to enhance performance.Promote performance optimization techniques and foster a culture of excellence across teams.

About Our TeamJoin the Fleet team at OpenAI, where we empower groundbreaking research and product innovation through our advanced computing infrastructure. We manage extensive systems across data centers, GPUs, and networking, ensuring optimal performance, high availability, and efficiency. Our work is crucial in enabling OpenAI’s models to function seamlessly at scale, supporting both our internal research endeavors and external products like ChatGPT. We are committed to prioritizing safety, reliability, and the ethical deployment of AI technology.About the RoleAs a Software Engineer on the Fleet High Performance Computing (HPC) team, you will play a vital role in ensuring the reliability and uptime of OpenAI’s compute fleet. Minimizing hardware failures is essential for smooth research training progress and uninterrupted services, as even minor hardware issues can lead to significant setbacks. With the rise of large supercomputers, the stakes in maintaining efficiency and stability have never been higher.At the cutting edge of technology, we often lead the charge in troubleshooting complex, state-of-the-art systems at scale. This is a unique opportunity for you to engage with groundbreaking technologies and create innovative solutions that enhance the health and efficiency of our supercomputing infrastructure.Our team fosters a culture of autonomy and ownership, enabling skilled engineers to drive meaningful change. In this role, you will focus on comprehensive system investigations and develop automated solutions to enhance our operations. We seek individuals who dive deep into challenges, conduct thorough investigations, and create scalable automation for detection and remediation.Key Responsibilities:Develop and maintain automation systems for provisioning and managing server fleets.Create tools to monitor server health, performance metrics, and lifecycle events.Collaborate effectively with teams across clusters, networking, and infrastructure.Work closely with external operators to maintain a high level of service quality.Identify and resolve performance bottlenecks and inefficiencies in the system.Continuously enhance automation processes to minimize manual intervention.You Will Excel in This Role if You Have:Experience in managing large-scale server environments.A blend of technical skills in systems programming and infrastructure management.Strong problem-solving abilities and a methodical approach to troubleshooting.Familiarity with high-performance computing technologies and tools.

Join Our Team at KernelAt Kernel, we are revolutionizing the way developers interact with the digital world through our innovative platform, offering Lightning-Fast Browsers-as-a-Service for seamless browser automation and advanced web agents. Our cutting-edge API and MCP server empower developers to effortlessly launch browsers in the cloud, eliminating the complexities of infrastructure management.Our serverless browser platform takes the hassle out of autoscaling, reliability, and observability, allowing developers to concentrate on their agents' functionality rather than the underlying processes. Kernel transforms AI into a practical and impactful tool, enabling developers to deploy agents that can genuinely engage with online environments.Trusted by industry leaders such as Cash App and Rye for applications ranging from comprehensive research to QA automation and real-time web analysis, we have successfully raised $22M from prominent investors including Accel, YCombinator, and others.With just one line of code, any web agent can be deployed to our cloud—what happens next is up to you. If you are passionate about creating essential infrastructure for the future of AI applications, we would love to connect.

Join Prima Mente: A Leader in Biology AIAt Prima Mente, we are redefining the frontier of biology through artificial intelligence. Our mission is to generate unique datasets, develop versatile biological foundation models, and translate groundbreaking discoveries into impactful research and clinical outcomes. With a commitment to understanding the complexities of the brain, we aim to shield it from neurological diseases while enhancing overall health. Our diverse team of AI researchers, experimentalists, clinicians, and operational experts operates across London, San Francisco, and Dubai.Role Overview: GPU/CPU-Accelerated BioinformaticsWe are seeking a skilled Bioinformatics Software Engineer to architect and implement scalable production pipelines for processing multi-omics data. The successful candidate will enable rapid transitions from hypothesis to patent-ready solutions in a matter of months.Key Responsibilities:Design and implement bioinformatics pipelines optimized for GPU/CPU utilization utilizing tools like Flyte and Nextflow, capable of processing over 1,000 samples at scale.Optimize performance and cost efficiency by leveraging GPU/CPU acceleration where it provides the greatest benefit.Collaborate with experimental and machine learning teams to validate computational results and align processing with model requirements.Foster and manage collaborations with academic and industrial research partners.Growth Expectations1 Month: You will be deploying your workflows on GPU/CPU-accelerated cloud infrastructure to process multi-omic experiments, while building relationships with AI/ML and wet lab teams to understand their requirements.3 Months: Your optimized pipelines will be processing thousands of samples with substantial speed enhancements and reduced costs, yielding publication and patent-ready outcomes.6 Months: Your automated pipelines will support daily AI model training, and you will co-design experiments alongside AI/ML engineers, leading technical execution on external collaborations.Your ProfileYou are passionate about pushing the boundaries of AI and biology. As an engineer rather than an analyst, you thrive on enhancing performance and efficiency while architecting robust systems. You are comfortable making rapid technical decisions and iterate quickly.Desired QualificationsExperience in bioinformatics, computational biology, or a related field.Proficiency in software engineering, particularly in developing scalable data processing pipelines.Strong understanding of multi-omics data and methods.Familiarity with GPU/CPU acceleration techniques.Excellent communication and collaboration skills.

At Sciforium, we are at the forefront of AI infrastructure, pioneering advanced multimodal AI models and an innovative, high-efficiency serving platform. With substantial backing from AMD and a dedicated team of engineers, we are rapidly expanding our capabilities to support the next generation of frontier AI models and real-time applications.About the RoleWe are looking for a highly skilled Senior HPC & GPU Infrastructure Engineer who will be responsible for ensuring the health, reliability, and performance of our GPU compute cluster. As the primary custodian of our high-density accelerator environment, you will serve as the crucial link between hardware operations, distributed systems, and machine learning workflows. This position encompasses a range of responsibilities, from hands-on Linux systems engineering and GPU driver setup to maintaining the ML software stack (CUDA/ROCm, PyTorch, JAX, vLLM). If you are passionate about optimizing hardware performance, enjoy troubleshooting GPUs at scale, and aspire to create world-class AI infrastructure, we would love to hear from you.Your Responsibilities1. System Health & Reliability (SRE)On-Call Response: Be the primary responder for system outages, GPU failures, node crashes, and other cluster-wide incidents, ensuring rapid issue resolution to minimize downtime.Cluster Monitoring: Develop and maintain monitoring protocols for GPU health, thermal behavior, PCIe/NVLink topology issues, memory errors, and general system load.Vendor Liaison: Collaborate with data center personnel, hardware vendors, and on-site technicians for repairs, RMA processing, and physical maintenance of the cluster.2. Linux & Network AdministrationOS Management: Oversee the installation, patching, and maintenance of Linux distributions (Ubuntu / CentOS / RHEL), ensuring consistent configuration, kernel tuning, and automation for large node fleets.Security & Access Controls: Set up VPNs, iptables/firewalls, SSH hardening, and network routing to secure our computing infrastructure.Identity & Storage Management: Manage LDAP/FreeIPA/AD for user identity and administer distributed file systems like NFS, GPFS, or Lustre.3. GPU & ML Stack EngineeringDeployment & Bring-Up: Spearhead the deployment of new GPU nodes, including BIOS configuration and software integration to ensure optimal performance.

About KernelKernel is an innovative developer platform that delivers Lightning-Fast Browsers-as-a-Service for browser automation and web agent deployment. Our API and MCP server empower developers to effortlessly launch cloud-based browsers without the hassle of infrastructure management.Our serverless browser solution takes care of the complexities: autoscaling, dependable browser infrastructure, observability, and intricate web interactions, allowing developers to concentrate on their agents' functionality rather than the underlying technology. Kernel brings AI to life, enabling developers to create agents that genuinely engage with the digital landscape.Our platform is trusted by teams at Cash App, Rye, and many others for various tasks including in-depth research, QA automation, and real-time web analysis. We recently secured $22M in funding from notable investors such as Accel, YCombinator, Vercel, Paul Graham, Solomon Hykes (Docker), David Cramer (Sentry), and Charlie Marsh (Astral).With just a single line of code, you can deploy any web agent to our cloud infrastructure. If you are passionate about developing essential infrastructure for the future of AI applications, we would love to connect with you.

About KernelKernel is a cutting-edge developer platform that offers Lightning-Fast Browsers-as-a-Service tailored for browser automation and web agent creation. Our API and MCP server enable developers to seamlessly launch browsers in the cloud without the hassle of infrastructure management.Our serverless browser platform takes care of the complex tasks: autoscaling reliable browser infrastructure, ensuring observability, and managing the intricate details of web interactions, allowing developers to concentrate on their agent functionalities rather than the underlying processes. Kernel brings AI to life, making it practical and powerful, empowering developers to deploy agents that can effectively engage with the digital landscape.We are trusted by teams at Cash App, Rye, and numerous others for diverse applications like in-depth research, QA automation, and real-time web analysis. We have successfully secured $22M in funding from notable investors including Accel, YCombinator, Vercel, Paul Graham, Solomon Hykes (Docker), David Cramer (Sentry), Charlie Marsh (Astral), among others.With just one line of code, you can deploy any web agent to our cloud. The rest is in your hands. If you're passionate about developing critical infrastructure for the next generation of AI applications, we would love to connect.

About KernelKernel is a cutting-edge developer platform that offers Lightning-Fast Browsers-as-a-Service for browser automations and web agents. Our API and MCP server empower developers to effortlessly launch browsers in the cloud without the hassle of managing infrastructure.Our serverless browser platform takes care of the complex aspects: autoscaling reliable browser infrastructure, observability, and intricate web interactions, enabling developers to concentrate on the functionality of their agents rather than the underlying details. Kernel transforms AI into a tangible, practical, and powerful tool, allowing developers to deploy agents capable of genuine interaction with the digital landscape.We pride ourselves on being trusted by teams at Cash App, Rye, and numerous others for deep research, QA automation, and real-time web analysis. We have successfully secured $22M in funding from top investors including Accel, YCombinator, Vercel, Paul Graham, Solomon Hykes (Docker), David Cramer (Sentry), Charlie Marsh (Astral), and more.With just one line of code, you can deploy any web agent to our cloud. The rest is in your hands. If you are passionate about building essential infrastructure for the next wave of AI applications, we would love to hear from you.About the RoleAs a Product Engineer at Kernel, you will be a full-stack engineer who values product development as much as coding. You possess the ability to translate your strong product instincts into code, ranging from pixel-perfect UI decisions to backend API architecture. You proactively contribute to the specification process rather than waiting for one to be provided.You will collaborate closely with our co-founders to define product direction, deliver full-stack features from end to end, and ensure that Kernel maintains its polished yet powerful appearance.Your ResponsibilitiesLead the full-stack implementation of user-facing product surfaces: dashboard, onboarding, website, and core product functionalities.Influence the product roadmap by integrating customer feedback, analyzing usage patterns, and leveraging your own insights into developer needs.Enhance developer experience across our SDK, documentation, CLI, and API, delivering the kind of seamless experience that makes developers exclaim, 'this just works.'Rapidly prototype and iterate, bringing features from concept to production with minimal oversight.Help shape the standards for building a superior developer product at Kernel.Your QualificationsYou are comfortable taking ownership of features from frontend to backend, demonstrating a holistic understanding of product development.A strong passion for creating seamless user experiences and an ability to translate product vision into functional code.Experience working in a fast-paced environment with a focus on agile methodologies.

About Our TeamThe Inference team at OpenAI is dedicated to translating our cutting-edge research into accessible, transformative technology for consumers, enterprises, and developers. By leveraging our advanced AI models, we enable users to achieve unprecedented levels of innovation and productivity. Our primary focus lies in enhancing model inference efficiency and accelerating progress in research through optimized inference capabilities.About the RoleWe are seeking talented engineers to expand and optimize OpenAI's inference infrastructure, specifically targeting emerging GPU platforms. This role encompasses a wide range of responsibilities from low-level kernel optimization to high-level distributed execution. You will collaborate closely with our research, infrastructure, and performance teams to ensure seamless operation of our largest models on cutting-edge hardware.This position offers a unique opportunity to influence and advance OpenAI’s multi-platform inference capabilities, with a strong emphasis on optimizing performance for AMD accelerators.Your Responsibilities Include:Overseeing the deployment, accuracy, and performance of the OpenAI inference stack on AMD hardware.Integrating our internal model-serving infrastructure (e.g., vLLM, Triton) into diverse GPU-backed systems.Debugging and optimizing distributed inference workloads across memory, network, and compute layers.Validating the correctness, performance, and scalability of model execution on extensive GPU clusters.Collaborating with partner teams to design and optimize high-performance GPU kernels for accelerators utilizing HIP, Triton, or other performance-centric frameworks.Working with partner teams to develop, integrate, and fine-tune collective communication libraries (e.g., RCCL) to parallelize model execution across multiple GPUs.Ideal Candidates Will:Possess experience in writing or porting GPU kernels using HIP, CUDA, or Triton, with a strong focus on low-level performance.Be familiar with communication libraries like NCCL/RCCL, understanding their importance in high-throughput model serving.Have experience with distributed inference systems and be adept at scaling models across multiple accelerators.Enjoy tackling end-to-end performance challenges across hardware, system libraries, and orchestration layers.Be eager to join a dynamic, agile team focused on building innovative infrastructure from the ground up.

Join Zyphra as a Research Engineer specializing in AI Performance and Kernel Optimization. In this role, you will work at the forefront of AI technologies, developing and optimizing kernel solutions that enhance the performance of our systems. You will collaborate with cross-functional teams, leveraging your expertise to drive innovation and efficiency.

Join our dynamic team at Reka as a GPU Performance Engineer, where you will leverage your expertise in Python and large-scale model training to enhance our training infrastructure. You will play a pivotal role in optimizing model performance, contributing to critical technical decisions, and improving our post-training processes, including reinforcement learning and fine-tuning. Your contributions will also focus on enhancing the efficiency and scalability of our model serving infrastructure.

Baseten develops infrastructure and tools that help AI companies deploy and scale inference. Teams at organizations like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer rely on Baseten to bring advanced machine learning models into production. The company recently secured a $300M Series E from investors including BOND, IVP, Spark Capital, Greylock, and Conviction. Role overview This Software Engineer - GPU Inference position joins the founding team for Baseten Voice AI in San Francisco. The team focuses on building production-ready Voice AI systems, bringing open-source voice models into real-world use for clients in productivity, customer service, healthcare conversations, and education. The work shapes how people interact with technology through voice, creating broad impact across industries. In this role, the engineer leads the internal inference stack that powers Voice AI models. Responsibilities include guiding the product roadmap and driving engineering execution. Collaboration is a key part of the job, working closely with Forward Deployed Engineers, Model Performance Engineers, and other technical groups to advance Voice AI capabilities. Sample projects and initiatives The world's fastest Whisper, with streaming and diarization Canopy Labs selects Baseten for Orpheus TTS inference Partnering with the Core Product team to build an orchestration framework for a multi-model voice agent Working with the Training Platform team to support continuous training of voice models Designing a developer-friendly API and SDK for self-service adoption of Baseten Voice AI products

At Thinking Machines Lab, our ambition is to enhance human potential by advancing collaborative general intelligence. We envision a future where individuals have the tools and knowledge to harness AI for their distinct requirements and aspirations.Our team comprises dedicated scientists, engineers, and innovators who have contributed to some of the most renowned AI products, including ChatGPT and Character.ai, along with open-weight models like Mistral, and influential open-source projects such as PyTorch, OpenAI Gym, Fairseq, and Segment Anything.About the RoleWe are seeking an Infrastructure Research Engineer to architect, optimize, and sustain the computational frameworks that facilitate large-scale language model training. You will create high-performance machine learning kernels (e.g., CUDA, CuTe, Triton), enable effective low-precision arithmetic operations, and enhance the distributed computing infrastructure essential for training expansive models.This position is ideal for an engineer who thrives in close collaboration with hardware and research disciplines. You will partner with researchers and systems architects to merge algorithmic design with hardware efficiency. Your responsibilities will include prototyping new kernel implementations, evaluating performance across various hardware generations, and helping to establish the numerical and parallelism strategies crucial for scaling next-generation AI systems.Note: This is an evergreen role that remains open continuously for expressions of interest. We receive numerous applications, and there may not always be an immediate opportunity that aligns with your qualifications. However, we encourage you to apply, as we regularly assess applications and will reach out as new positions become available. You are also welcome to reapply after gaining additional experience, but please refrain from applying more than once every six months. Additionally, you may notice postings for specific roles catering to particular projects or team needs. In such cases, you are encouraged to apply directly alongside this evergreen listing.What You’ll DoDesign and develop custom ML kernels (e.g., CUDA, CuTe, Triton) for key LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for contemporary GPU and accelerator architectures.Conceptualize compute primitives aimed at alleviating memory bandwidth bottlenecks and enhancing kernel compute efficiency.Collaborate with research teams to synchronize kernel-level optimizations with model architecture and algorithmic objectives.Create and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.Contribute to the stability and scalability of our infrastructure, ensuring it meets the growing demands of AI development.