Software Engineer, Inference - Performance Optimization

Role overview This Software Engineer position at OpenAI focuses on inference and performance optimization. Based in San Francisco, the role centers on increasing the speed and efficiency of advanced AI systems. Collaboration with experienced engineers is a key part of the work, with an emphasis on refining AI performance. What you will do Work on optimizing the performance of AI inference systems Collaborate with other engineers to improve efficiency and speed Contribute to solutions that enhance AI system capabilities Location This role is based in San Francisco.

Join DigitalOcean as a Senior Engineer focused on Inference Optimizations, where you will play a pivotal role in enhancing our AI and machine learning capabilities. Collaborate with a talented team to develop cutting-edge solutions that optimize inference processes across various applications.

At ClickUp, we're not just developing software; we're shaping the future of work! In an era dominated by work sprawl, we identified a more efficient way. This led us to create the first truly integrated AI workspace, consolidating tasks, documents, chat, calendar, and enterprise search, all enhanced by context-driven AI. Our mission is to empower millions of teams to escape silos, reclaim their time, and reach unprecedented levels of productivity. At ClickUp, you'll have the chance to learn, innovate, and leverage AI in transformative ways that will not only influence our product but also the broader landscape of work itself. Join a daring, pioneering team that's challenging the limits of what's possible! We are on the lookout for a technical leader in SaaS client performance who is passionate about enhancing the customer experience through top-tier performance solutions. As a Senior Performance Engineer, you will spearhead comprehensive strategies to optimize application speed, memory utilization, and reliability across our entire platform. You will be empowered to analyze, diagnose, and address performance bottlenecks wherever they arise—be it front-end, back-end, or infrastructure—ensuring ClickUp remains the fastest and most reliable productivity platform available.The ideal candidate is a hands-on authority in browser and NodeJS performance, with a thorough understanding of how code influences rendering, memory management, and overall user experience. You excel in solving intricate challenges, collaborating across teams, and establishing new benchmarks for performance excellence. If you're driven to make a significant impact for millions of users, this is your chance to lead at scale.Your Responsibilities:Conduct root cause analysis on client performance issues and perform post-mortems.Profile application code to identify inefficient algorithms, memory leaks, and other issues; propose and implement effective solutions.Establish performance monitoring, alerting, and dashboards to proactively detect and resolve client performance challenges.Examine client traffic patterns, load testing outcomes, and other metrics to set benchmarks and drive enhancements.Champion performance best practices and set performance standards across the engineering organization.Identify infrastructure upgrades (caching, CDNs, database optimization) to elevate the client experience.Collaborate with development teams to incorporate performance as a core requirement in the development of new features.

About Our TeamAt OpenAI, our Foundations team is dedicated to examining how model behavior evolves as we scale up models, data, and computing resources. We meticulously analyze the relationships between model architecture, optimization strategies, and training datasets to inform the design and training of next-generation models.About the PositionAs a Team Lead in Research Inference, you will be instrumental in constructing systems that empower advanced AI models to operate efficiently at scale. Your role lies at the crossroads of model research and systems engineering, where you will translate innovative architectural concepts into high-performance inference systems, clearly illustrating the trade-offs in performance, memory usage, and scalability.Your contributions will significantly shape model design, evaluation, and iteration processes across our research organization. By developing and refining high-performance inference infrastructures, you will provide researchers with the tools necessary to explore new ideas while understanding their computational and systems implications.This position does not involve serving products; instead, it supports research through a focus on performance, accuracy, and realism, ensuring that our AI research is firmly rooted in scalable solutions.ResponsibilitiesDesign and develop optimized inference runtimes for large-scale AI models, emphasizing efficiency, reliability, and scalability.Take ownership of optimizing core execution processes, including model execution, memory management, batching, and scheduling.Enhance and expand distributed inference across multiple GPUs, focusing on parallelism, communication patterns, and runtime coordination.Implement and refine critical inference operators and kernels based on real-world workloads.Collaborate closely with research teams to ensure accurate and efficient support for new model architectures within inference systems.Identify and resolve performance bottlenecks through comprehensive profiling, benchmarking, and low-level debugging.Contribute to the observability, correctness, and reliability of large-scale AI systems.Ideal Candidate ProfileExperience in developing production-level inference systems, beyond just training and executing models.Proficient in GPU-centric performance engineering, including managing memory behavior and understanding latency/throughput trade-offs.Strong analytical skills and familiarity with performance profiling tools.

OverviewAt Pulse, we are revolutionizing the way data infrastructure operates by addressing the critical challenge of accurately extracting structured information from intricate documents on a large scale. Our innovative document understanding technique merges intelligent schema mapping with advanced extraction models, outperforming traditional OCR and parsing methods.Located in the heart of San Francisco, we are a dynamic team of engineers dedicated to empowering Fortune 100 enterprises, YC startups, public investment firms, and growth-stage companies. Backed by top-tier investors, we are rapidly expanding our footprint in the industry.What sets our technology apart is our sophisticated multi-stage architecture, which includes:Specialized models for layout understanding and component detectionLow-latency OCR models designed for precise extractionAdvanced algorithms for reading-order in complex document structuresProprietary methods for table structure recognition and parsingFine-tuned vision-language models for interpreting charts, tables, and figuresIf you possess a strong passion for the convergence of computer vision, natural language processing, and data infrastructure, your contributions at Pulse will significantly impact our clients and help shape the future of document intelligence.

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.

About Our TeamJoin the Inference team at OpenAI, where we leverage cutting-edge research and technology to deliver exceptional AI products to consumers, enterprises, and developers. Our mission is to empower users to harness the full potential of our advanced AI models, enabling unprecedented capabilities. We prioritize efficient and high-performance model inference while accelerating research advancements.About the RoleWe are seeking a passionate Software Engineer to optimize some of the world's largest and most sophisticated AI models for deployment in high-volume, low-latency, and highly available production and research environments.Key ResponsibilitiesCollaborate with machine learning researchers, engineers, and product managers to transition our latest technologies into production.Work closely with researchers to enable advanced research initiatives through innovative engineering solutions.Implement new techniques, tools, and architectures that enhance the performance, latency, throughput, and effectiveness of our model inference stack.Develop tools to identify bottlenecks and instability sources, designing and implementing solutions for priority issues.Optimize our code and Azure VM fleet to maximize every FLOP and GB of GPU RAM available.You Will Excel in This Role If You:Possess a solid understanding of modern machine learning architectures and an intuitive grasp of performance optimization strategies, especially for inference.Take ownership of problems end-to-end, demonstrating a willingness to acquire any necessary knowledge to achieve results.Bring at least 5 years of professional software engineering experience.Have or can quickly develop expertise in PyTorch, NVidia GPUs, and relevant optimization software stacks (such as NCCL, CUDA), along with HPC technologies like InfiniBand, MPI, and NVLink.Have experience in architecting, building, monitoring, and debugging production distributed systems, with bonus points for working on performance-critical systems.Have successfully rebuilt or significantly refactored production systems multiple times to accommodate rapid scaling.Are self-driven, enjoying the challenge of identifying and addressing the most critical problems.

About Our TeamJoin OpenAI’s dynamic Inference team, where we empower the deployment of cutting-edge AI models, including our renowned GPT models, advanced Image Generation capabilities, and Whisper, across diverse platforms. Our mission is to ensure these models are not only high-performing and scalable but also available for real-world applications. Collaborating closely with our Research team, we’re committed to bringing the next generation of AI innovations to fruition. As a compact, agile team, we prioritize delivering an exceptional developer experience while continuously pushing the frontiers of artificial intelligence.As we expand our focus into multimodal inference, we are building the necessary infrastructure to support models that process images, audio, and other non-text modalities. This work involves tackling diverse model sizes and interactions, managing complex input/output formats, and ensuring seamless collaboration between product and research teams.About The RoleWe are seeking a passionate Software Engineer to aid in the large-scale deployment of OpenAI’s multimodal models. You will join a small yet impactful team dedicated to creating robust, high-performance infrastructure for real-time audio, image, and various multimodal workloads in production environments.This position is inherently collaborative; you will work directly with researchers who develop these models and with product teams to define novel interaction modalities. Your contributions will enable users to generate speech, interpret images, and engage with models in innovative ways that extend beyond traditional text-based interactions.Key Responsibilities:Design and implement advanced inference infrastructure for large-scale multimodal models.Optimize systems for high-throughput and low-latency processing of image and audio inputs and outputs.Facilitate the transition of experimental research workflows into dependable production services.Engage closely with researchers, infrastructure teams, and product engineers to deploy state-of-the-art capabilities.Contribute to systemic enhancements, including GPU utilization, tensor parallelism, and hardware abstraction layers.You May Excel In This Role If You:Have a proven track record of building and scaling inference systems for large language models or multimodal architectures.Possess experience with GPU-based machine learning workloads and a solid understanding of the performance dynamics associated with large models, particularly with intricate data types like images or audio.Thrive in a fast-paced, experimental environment and enjoy collaborating with cross-functional teams to drive impactful results.

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 fal as we revolutionize the generative-media infrastructure landscape. Our mission is to enhance model inference performance, enabling creative experiences on an unprecedented scale. We are seeking a Staff Technical Lead for Inference & ML Performance, an individual who possesses a unique blend of deep technical knowledge and strategic foresight. In this pivotal role, you will lead a talented team dedicated to building and optimizing cutting-edge inference systems. If you're ready to influence the future of inference performance in a fast-paced and rapidly growing environment, we want to hear from you.Why This Role MattersIn this role, you will play a crucial part in shaping the future of fal’s inference engine, ensuring that our generative models consistently deliver outstanding performance. Your contributions will directly affect our capacity to swiftly provide innovative creative solutions to a diverse clientele, from individual creators to global brands.Your ResponsibilitiesDefine and steer the technical direction, guiding your team across various domains including kernels, applied performance, ML compilers, and distributed inference to develop high-performance solutions.

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 our innovative team at Anthropic as a Software Engineer specializing in Cloud Inference Safeguards. In this role, you will play a crucial part in developing and enhancing the systems that ensure the robustness and security of our cloud-based inference services. You will collaborate with cross-functional teams to design, implement, and maintain scalable solutions that meet our high standards for reliability and performance.

P-1285 About This Role Join Databricks as a Staff Software Engineer specializing in GenAI inference, where you will spearhead the architecture, development, and optimization of the inference engine that powers the Databricks Foundation Model API. Your role will be crucial in bridging cutting-edge research with real-world production requirements, ensuring exceptional throughput, minimal latency, and scalable solutions. You will work across the entire GenAI inference stack, including kernels, runtimes, orchestration, memory management, and integration with various frameworks and orchestration systems. What You Will Do Take full ownership of the architecture, design, and implementation of the inference engine, collaborating on a model-serving stack optimized for large-scale LLM inference. Work closely with researchers to integrate new model architectures or features, such as sparsity, activation compression, and mixture-of-experts into the engine. Lead comprehensive optimization efforts focused on latency, throughput, memory efficiency, and hardware utilization across GPUs and other accelerators. Establish and uphold standards for building and maintaining instrumentation, profiling, and tracing tools to identify performance bottlenecks and drive optimizations. Design scalable solutions for routing, batching, scheduling, memory management, and dynamic loading tailored to inference workloads. Guarantee reliability, reproducibility, and fault tolerance in inference pipelines, including capabilities for A/B testing, rollbacks, and model versioning. Collaborate cross-functionally to integrate with federated and distributed inference infrastructure, ensuring effective orchestration across nodes, load balancing, and minimizing communication overhead. Foster collaboration with cross-functional teams, including platform engineers, cloud infrastructure, and security/compliance professionals. Represent the team externally through benchmarks, whitepapers, and contributions to open-source projects. What We Look For A BS/MS/PhD in Computer Science or a related discipline. A solid software engineering background with 6+ years of experience in performance-critical systems. A proven ability to own complex system components and influence architectural decisions from conception to execution. A deep understanding of ML inference internals, including attention mechanisms, MLPs, recurrent modules, quantization, and sparse operations. Hands-on experience with CUDA, GPU programming, and essential libraries (cuBLAS, cuDNN, NCCL, etc.). A strong foundation in distributed systems design, including RPC frameworks, queuing, RPC batching, sharding, and memory partitioning. Demonstrated proficiency in diagnosing and resolving performance bottlenecks across multiple layers (kernel, memory, networking, scheduler).

Join fal in our pursuit to maintain a leading edge in model performance for generative media models. You'll be instrumental in designing and implementing innovative solutions for model serving architecture, built on our proprietary inference engine. Your focus will be on maximizing throughput while minimizing latency and resource consumption. In addition, you will create performance monitoring and profiling tools to identify bottlenecks and optimization opportunities. Collaborate closely with our Applied ML team and clients in the media sector to ensure their workloads leverage our accelerator effectively.

Join the Fluidstack TeamAt Fluidstack, we’re pioneering the infrastructure for advanced intelligence. We collaborate with leading AI laboratories, governmental entities, and major corporations—including Mistral, Poolside, and Meta—to deliver computing solutions at unprecedented speeds.Our mission is to transform the vision of Artificial General Intelligence (AGI) into a reality. Driven by our purpose, our dedicated team is committed to building state-of-the-art infrastructure that prioritizes our customers' success. If you share our passion for excellence and are eager to contribute to the future of intelligence, we invite you to be part of our journey.Role OverviewThe Inference Platform team at Fluidstack is at the forefront of addressing the cost and latency challenges associated with frontier AI. You will play a crucial role in managing the serving layer that connects our global accelerator supply with the production workloads of our clients, which include LLM serving frameworks, KV cache infrastructure, and Kubernetes orchestration across multiple data centers.This hands-on individual contributor role combines elements of distributed systems, model optimization, and serving infrastructure. You will oversee the entire lifecycle of inference deployments for leading AI labs, striving for enhancements in throughput, cost-efficiency, and response times, while also influencing the architectural decisions that guide Fluidstack’s deployment strategies.

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

ABOUT BASETENAt Baseten, we empower the leading AI companies of today, including Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer, by providing essential inference capabilities. Our unique blend of applied AI research, adaptable infrastructure, and intuitive developer tools enables innovators at the cutting edge of AI to seamlessly transition advanced models into production. With our recent success in securing a $300M Series E funding round, backed by notable investors such as BOND, IVP, Spark Capital, Greylock, and Conviction, we're on an exciting growth trajectory. Join our team and contribute to the platform that engineers rely on to launch AI-driven products.THE ROLEAs an Applied AI Inference Engineer at Baseten, you'll collaborate closely with clients to design, develop, and implement high-performance AI applications using our platform. You will guide customers through the entire process, from initial concept to deployment, transforming vague business objectives into dependable, observable solutions that meet defined quality, latency, and cost metrics.This position is ideal for innovative engineers eager to gain insight into how modern organizations scale AI adoption. You will thrive if you enjoy a multifaceted role that intersects product development, software engineering, performance optimization, and direct customer engagement.It’s essential to note that this position requires hands-on coding and software development, while also encompassing elements of product management, technical customer success, and pre-sales engineering.EXAMPLE INITIATIVESExplore insights from our Forward Deployed Engineering team through these blog posts: Forward Deployed Engineering on the frontier of AIThe fastest, most accurate Whisper transcriptionDeploy production-ready model servers from Docker imagesDeploy custom ComfyUI workflows as APIs...

Join Anthropic as a Software Engineer on our Launch Engineering team, where your focus will be on designing and building cutting-edge deployment infrastructure for inference code. You will ensure our AI models, at scale, are continuously and seamlessly deployed to production. This role is pivotal in optimizing resource management while maximizing deployment efficiency. Your expertise will be essential in navigating complex deployment challenges, validating systems, and ensuring minimal disruption to our user services. If you thrive in tackling ambitious problems at the intersection of automation and resource management, this position offers the opportunity to make a significant impact.

At Inferact, we are on a mission to establish vLLM as the premier AI inference engine, revolutionizing AI progress by making inference both more accessible and efficient. Our founding team consists of the original creators and key maintainers of vLLM, positioning us uniquely at the nexus of cutting-edge models and advanced hardware.Role OverviewWe are seeking a passionate inference runtime engineer eager to explore and expand the frontiers of LLM and diffusion model serving. As models evolve and grow in complexity with new architectures like mixture-of-experts and multimodal designs, the demand for innovative solutions in our inference engine intensifies. This role places you at the heart of vLLM, where you will enhance model execution across a variety of hardware platforms and architectures. Your contributions will have a direct influence on the future of AI inference.

Who are we?At Cohere, our mission is to elevate intelligence to benefit humanity. We specialize in training and deploying cutting-edge models for developers and enterprises focused on creating AI systems that deliver extraordinary experiences such as content generation, semantic search, retrieval-augmented generation, and intelligent agents. We view our work as pivotal to the broad acceptance of AI technologies.We are passionate about our creations. Every team member plays a vital role in enhancing our models' capabilities and the value they provide to our customers. We thrive on hard work and speed, always prioritizing our clients' needs.Cohere is a diverse team of researchers, engineers, designers, and more, all dedicated to their craft. Each individual is a leading expert in their field, and we recognize that a variety of perspectives is essential to developing exceptional products.Join us in our mission and help shape the future of AI!Why this role?Are you excited about architecting high-performance, scalable, and reliable machine learning systems? Do you aspire to shape and construct the next generation of AI platforms that enhance advanced NLP applications? We are seeking talented Members of Technical Staff to join our Model Serving team at Cohere. This team is responsible for the development, deployment, and operation of our AI platform, which delivers Cohere's large language models via user-friendly API endpoints. In this role, you will collaborate with multiple teams to deploy optimized NLP models in production settings characterized by low latency, high throughput, and robust availability. Additionally, you will have the opportunity to work directly with customers to create tailored deployments that fulfill their unique requirements.