About the Role
\nAs a Software Engineer on the Storage team at Cursor, you'll own the data layer that underpins every product surface: the databases, caches, and the strategy for how teams provision, query, and scale their data stores.
\nMillions of developers depend on Cursor every day, and the future of our storage architecture is one of the highest-leverage problems at the company: get it right, and every team ships faster, every product surface gets more reliable, and Cursor can scale to meet explosive demand. You'll design and execute the path to a robust, multi-database topology built for that growth.
\nExample projects include...
\nYou may be a fit if
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_5558189c-8cd","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Cursor","sameAs":"https://cursor.com","logo":"https://logos.yubhub.co/cursor.com.png"},"x-apply-url":"https://cursor.com/careers/software-engineer-storage","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Postgres","MySQL","relational databases","database sharding","migration","decomposition","storage engines","operational excellence","backups","monitoring","query performance","capacity planning"],"x-skills-preferred":[],"datePosted":"2026-04-24T14:09:43.224Z","jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Postgres, MySQL, relational databases, database sharding, migration, decomposition, storage engines, operational excellence, backups, monitoring, query performance, capacity planning"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_377e69db-df1"},"title":"Database Engineer","description":"We're looking for a database engineer with deep experience building and scaling both structured and unstructured database platforms supporting distributed systems, data-intensive applications, and machine learning infrastructure.
\nAs a member of the Platform team, you will build and mature database foundations for Scale, leveraging industry-standard platforms. You will collaborate with stakeholders across the organisation, including software developers, platform engineers, machine learning scientists, customer operations, etc.
\nKey responsibilities include:
\nBuilding and maintaining high-performance database systems Collaborating with cross-functional teams to design and implement scalable database solutions Developing and optimising database queries and indexing strategies Ensuring data consistency and integrity across multiple systems Mentoring junior engineers and contributing to the growth of the team Improving engineering standards, tooling, and processes Working directly with engineering and sales teams to create backend database solutions to meet their challenging data and security needs Working with the Security Team on security compliance, pen tests, and mitigations that improve security across Scale Building systems capable of handling millions of frames of data every day, making it available to both our workforce and our internal teams with high availability.
\nThis role requires:
\n5+ years of industry experience as a database engineer post-graduation Engineering experience with building real-time and distributed system architecture Experience designing and self-hosting databases on industry-standard public cloud platforms Deep familiarity with design, architecture, optimisation, and tuning multiple database platforms such as MongoDB, Postgres, MySQL, DynamoDB, Redis Deep familiarity with SQL query optimisation, database indexing, scalability (partitioning/sharding), and replication Experience developing and optimising backup and restore functionality to meet RTO goals Intermediate experience in at least one coding language: Typescript, Python, Go, Java, C++ Experience working with Docker, Kubernetes, and Infra-as-Code (e.g. Terraform); bonus points for experience supporting GPU/ML workloads.
\nNice to haves:
\nPrior startup experience to help us grow responsibly Experience with AWS, Datadog, ElasticSearch Experience with cloud-based data warehouse solutions like Snowflake or Databricks Experience with cost optimisation strategies and techniques for database platforms Experience developing and designing intermediary data abstraction layers Mentored and grown members of your team or been a tech lead on large projects.
\nCompensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity-based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: Comprehensive health, dental, and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_377e69db-df1","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Scale","sameAs":"https://scale.com/","logo":"https://logos.yubhub.co/scale.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/scaleai/jobs/4688489005","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$162,400-$203,000 USD","x-skills-required":["database engineering","distributed systems","data-intensive applications","machine learning infrastructure","SQL query optimisation","database indexing","scalability","partitioning","sharding","replication","backup and restore functionality","Docker","Kubernetes","Infra-as-Code","Terraform","GPU/ML workloads"],"x-skills-preferred":["prior startup experience","AWS","Datadog","ElasticSearch","cloud-based data warehouse solutions","cost optimisation strategies","intermediary data abstraction layers"],"datePosted":"2026-04-24T13:03:06.404Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, CA; New York, NY"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"database engineering, distributed systems, data-intensive applications, machine learning infrastructure, SQL query optimisation, database indexing, scalability, partitioning, sharding, replication, backup and restore functionality, Docker, Kubernetes, Infra-as-Code, Terraform, GPU/ML workloads, prior startup experience, AWS, Datadog, ElasticSearch, cloud-based data warehouse solutions, cost optimisation strategies, intermediary data abstraction layers","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":162400,"maxValue":203000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_3513ac8f-9c4"},"title":"Staff Software Engineer, PostgreSQL","description":"You'll own Gamma's PostgreSQL infrastructure as we scale from 70 million users to hundreds of millions, and from terabytes of data to hundreds of terabytes. Your job is to make sure our database can handle orders of magnitude more usage without compromising performance.
\nThis is a deeply technical, hands-on role. You'll read and write code daily, dig into low-level systems, debug complex issues across massive datasets, and work on both core database scaling projects and application features. You'll collaborate closely with backend engineers, data engineers, and infrastructure teams to ensure our database architecture keeps pace with Gamma's growth.
\nOur team has a strong in-office culture and works in person 4–5 days per week in San Francisco. We love working together to stay creative and connected, with flexibility to work from home when focus matters most.
\nResponsibilities
\nRequirements
\nCompensation
\nThe base salary for this full-time position, which spans multiple internal levels depending on qualifications, ranges between $230K - $310K plus benefits & equity.
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_3513ac8f-9c4","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Gamma","sameAs":"https://gamma.com","logo":"https://logos.yubhub.co/gamma.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/gamma/f672c729-457f-4143-80e9-363ddf8a0870","x-work-arrangement":"hybrid","x-experience-level":"staff","x-job-type":"Full time","x-salary-range":"$230K - $310K","x-skills-required":["PostgreSQL","horizontal scaling","sharding","partitioning","complex query tuning","backend language","web APIs","Apache Kafka"],"x-skills-preferred":["TypeScript","Prisma","Apollo GraphQL","Terraform","AWS","AI/LLM tooling"],"datePosted":"2026-04-24T12:16:45.597Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"PostgreSQL, horizontal scaling, sharding, partitioning, complex query tuning, backend language, web APIs, Apache Kafka, TypeScript, Prisma, Apollo GraphQL, Terraform, AWS, AI/LLM tooling","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":230000,"maxValue":310000,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_2c095439-13b"},"title":"Principal Software Engineer","description":"Microsoft Advertising is seeking a Principal Software Engineer to join our Ads Engineering Platform team and advance the core capabilities of our ad-serving infrastructure,the engine that powers advertising across Bing Search, MSN, Microsoft Start, and shopping experiences in the Edge browser.
\nOur serving stack operates at massive global scale, delivering millions of ad requests per second through a geo-distributed, low-latency system that combines large-scale GPU/CPU inference, real-time bidding, and intelligent ranking pipelines.
\nThis role focuses on advancing the performance, efficiency, and scalability of the next generation of model serving and inference platforms for Ads.
\nAs a senior technical leader, you’ll design and optimize high-performance serving systems and GPU inference frameworks that drive measurable latency improvements and cost efficiency across Microsoft’s ad ecosystem.
\nYou’ll work across the stack,from CUDA kernel tuning and NUMA-aware threading to large-scale distributed orchestration and model deployment for deep learning and LLM workloads.
\nThis is a rare opportunity to shape the architecture of one of the world’s most advanced, mission-critical online serving platforms, collaborating with world-class engineers to deliver innovation at Internet scale.
\nMicrosoft’s mission is to empower every person and every organization on the planet to achieve more.
\nAs employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals.
\nEach day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
\nStarting January 26, 2026, Microsoft AI (MAI) employees who live within a 50-mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week.
\nThis expectation is subject to local law and may vary by jurisdiction.
\nResponsibilities:
\nDesign and lead the development of large-scale, distributed online serving systems,including GPU-accelerated and CPU-based ranking/inference pipelines,to process millions of ad requests per second with ultra-low latency, high throughput, and solid reliability.
\nArchitect and optimize end-to-end inference infrastructure, including model serving, batching/streaming, caching, scheduling, and resource orchestration across heterogeneous hardware (GPU, CPU, and memory tiers).
\nProfile and optimize performance across the full stack,from CUDA kernels and GPU pipelines to CPU threads and OS-level scheduling,identifying bottlenecks, tuning latency tails, and improving cost efficiency through advanced profiling and instrumentation.
\nOwn live-site reliability as a DRI: design telemetry, alerting, and fault-tolerance mechanisms; drive rapid diagnosis and mitigation of performance regressions or outages in globally distributed systems.
\nCollaborate and mentor across teams,driving architecture reviews, enforcing engineering excellence, promoting system-level optimization practices, and mentoring others in deep debugging, profiling, and performance engineering.
\nQualifications:
\nRequired Qualifications:
\nBachelor’s Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
\nPreferred Qualifications:
\nMaster’s Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor’s Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
\nIndustry experience in advertising or search engine backend systems, such as large-scale ad ranking, real-time bidding (RTB), or relevance-serving infrastructure.
\nHands-on experience with real-time data streaming systems (Kafka, Flink, Spark Streaming), feature-store integration, and multi-region deployment for low-latency, globally distributed services.
\nFamiliarity with LLM inference optimization,model sharding, tensor/kv-cache parallelism, paged attention, continuous batching, quantization (AWQ/FP8), and hybrid CPU–GPU orchestration.
\nDemonstrated success operating large-scale systems with SLA-based capacity forecasting, autoscaling, and performance telemetry; proven leadership in cross-functional architecture initiatives and technical mentorship.
\nPassion for performance engineering, observability, and deep systems debugging, with a solid drive to push the limits of serving infrastructure for the next generation of ads and AI models.
\nDeep expertise in GPU inference frameworks such as NVIDIA Triton Inference Server, CUDA, and TensorRT, including hands-on experience implementing custom CUDA kernels, optimizing memory movement (H2D/D2H), overlapping compute and I/O, and maximizing GPU occupancy and kernel fusion for deep learning and LLM workloads.
\nSolid understanding of model-serving trade-offs,batching vs. streaming, latency vs. throughput, quantization (FP16/BF16/INT8), dynamic batching, continuous model rollout, and adaptive inference scheduling across CPU/GPU tiers.
\nProven ability to profile and optimize GPU and system workloads,including tensor/memory alignment, compute–memory balancing, embedding table management, parameter servers, hierarchical caching, and vectorized inference for transformer/LLM architectures.
\nExpertise in low-level system and OS internals, including multi-threading, process scheduling, NUMA-aware memory allocation, lock-free data structures, context switching, I/O stack tuning (NVMe, RDMA), kernel bypass (DPDK, io_uring), and CPU/GPU affinity optimization for large-scale serving pipelines.
\n#MicrosoftAI Software Engineering IC5 – The typical base pay range for this role across the U.S. is USD $139,900 – $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 – $304,200 per year.
\nCertain roles may be eligible for benefits and other compensation.
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_2c095439-13b","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Microsoft AI","sameAs":"https://microsoft.ai","logo":"https://logos.yubhub.co/microsoft.ai.png"},"x-apply-url":"https://microsoft.ai/job/principal-software-engineer-41/","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$139,900 - $274,800 per year","x-skills-required":["C","C++","C#","Java","JavaScript","Python","NVIDIA Triton Inference Server","CUDA","TensorRT","Kafka","Flink","Spark Streaming","GPU inference frameworks","LLM inference optimization","model sharding","tensor/kv-cache parallelism","paged attention","continuous batching","quantization","AWQ/FP8","hybrid CPU–GPU orchestration","SLA-based capacity forecasting","autoscaling","performance telemetry","cross-functional architecture initiatives","technical mentorship","performance engineering","observability","deep systems debugging","low-level system and OS internals","multi-threading","process scheduling","NUMA-aware memory allocation","lock-free data structures","context switching","I/O stack tuning","kernel bypass","CPU/GPU affinity optimization"],"x-skills-preferred":[],"datePosted":"2026-04-24T12:12:57.301Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Redmond"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"C, C++, C#, Java, JavaScript, Python, NVIDIA Triton Inference Server, CUDA, TensorRT, Kafka, Flink, Spark Streaming, GPU inference frameworks, LLM inference optimization, model sharding, tensor/kv-cache parallelism, paged attention, continuous batching, quantization, AWQ/FP8, hybrid CPU–GPU orchestration, SLA-based capacity forecasting, autoscaling, performance telemetry, cross-functional architecture initiatives, technical mentorship, performance engineering, observability, deep systems debugging, low-level system and OS internals, multi-threading, process scheduling, NUMA-aware memory allocation, lock-free data structures, context switching, I/O stack tuning, kernel bypass, CPU/GPU affinity optimization","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":139900,"maxValue":274800,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_f2196e99-854"},"title":"Software Engineer - GenAI inference","description":"As a software engineer for GenAI inference, you will help design, develop, and optimize the inference engine that powers Databricks' Foundation Model API. You'll work at the intersection of research and production, ensuring our large language model (LLM) serving systems are fast, scalable, and efficient.
\nYour work will touch the full GenAI inference stack , from kernels and runtimes to orchestration and memory management. You will contribute to the design and implementation of the inference engine, and collaborate on model-serving stack optimized for large-scale LLMs inference.
\nKey responsibilities include:
\nRequirements include:
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_f2196e99-854","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Databricks","sameAs":"https://databricks.com","logo":"https://logos.yubhub.co/databricks.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/databricks/jobs/8202670002","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$142,200-$204,600 USD","x-skills-required":["software engineering","performance-critical systems","ML inference internals","CUDA","GPU programming","distributed systems","RPC frameworks","queuing","RPC batching","sharding","memory partitioning","instrumentation","tracing","profiling tools","ML researchers","complex system challenges"],"x-skills-preferred":[],"datePosted":"2026-04-18T15:54:17.777Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco, California"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"software engineering, performance-critical systems, ML inference internals, CUDA, GPU programming, distributed systems, RPC frameworks, queuing, RPC batching, sharding, memory partitioning, instrumentation, tracing, profiling tools, ML researchers, complex system challenges","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":142200,"maxValue":204600,"unitText":"YEAR"}}},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_58d220e6-02a"},"title":"Senior Site Reliability Engineer, Tenant Services: Geo","description":"Job Title: Senior Site Reliability Engineer, Tenant Services: Geo
\nWe are looking for a skilled Senior Site Reliability Engineer to join our Tenant Services, Geo team. As a Senior Site Reliability Engineer, you will be responsible for ensuring the smooth operation of our user-facing services and production systems.
\nAbout Us
\nGitLab is the intelligent orchestration platform for DevSecOps. It enables organisations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation.
\nResponsibilities
\nRequirements
\nNice to Have
\nBenefits
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_58d220e6-02a","directApply":true,"hiringOrganization":{"@type":"Organization","name":"GitLab","sameAs":"https://about.gitlab.com/","logo":"https://logos.yubhub.co/about.gitlab.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/gitlab/jobs/8490453002","x-work-arrangement":"remote","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Experience operating highly-available distributed systems at scale","Hands-on experience with at least one major cloud provider","Experience with Kubernetes and its ecosystem","Experience with infrastructure as code and configuration management tools","Strong programming skills in at least one general-purpose language"],"x-skills-preferred":["Experience working with disaster recovery technologies","Experience with managed/hosted environments similar to GitLab Dedicated","Prior work on large-scale data migrations or cutovers","Hands-on experience designing and operating database replication, backup/restore, and cutover workflows","Experience with multi-tenant architectures, sharding, or routing strategies in high-traffic SaaS platforms"],"datePosted":"2026-04-18T15:51:05.184Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Remote, India"}},"jobLocationType":"TELECOMMUTE","employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Experience operating highly-available distributed systems at scale, Hands-on experience with at least one major cloud provider, Experience with Kubernetes and its ecosystem, Experience with infrastructure as code and configuration management tools, Strong programming skills in at least one general-purpose language, Experience working with disaster recovery technologies, Experience with managed/hosted environments similar to GitLab Dedicated, Prior work on large-scale data migrations or cutovers, Hands-on experience designing and operating database replication, backup/restore, and cutover workflows, Experience with multi-tenant architectures, sharding, or routing strategies in high-traffic SaaS platforms"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_dd6ebd20-17d"},"title":"Research Scientist, Gemini Diffusion","description":"We're looking for a Research Scientist to join our team in London and help us accelerate our mission. As a Research Scientist, you will apply your deep scientific knowledge and research skills to advance paradigm-shifting research at a large scale. You will be at the heart of our efforts to deliver step-changes in the capabilities of our frontier models, with a significant focus on our Gemini Diffusion project.
\nYour work may involve brainstorming new disruptive ideas that could become the next generation of frontier AI models, particularly within the text diffusion space. You will prototype and develop these ideas with the rest of the team, contributing directly to Gemini Diffusion research. You will solve key research challenges by designing and executing experimental research on text diffusion models, sharing analyses, and proposing next steps. You will rigorously validate the theoretical and practical impact of our work at a large scale. You will work collaboratively with other Generative AI teams to move the technologies we develop out of the lab and into production. You will advance the fundamental architecture, algorithmic design, and capabilities of large-scale diffusion models. You will bring deep scientific expertise into our projects, sharing your insights and knowledge with other researchers and engineers.
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_dd6ebd20-17d","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Google DeepMind","sameAs":"https://deepmind.com/","logo":"https://logos.yubhub.co/deepmind.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/deepmind/jobs/7700399","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":null,"x-skills-required":["Advanced degree in computer science, electrical engineering, science, mathematics, or equivalent experience","Academic research experience in machine learning, publications, or research experience in related fields","Experience with some or all LLMs, Transformers, Diffusion models, Text diffusion, Large-scale distributed training","Strong communication skills (via discussion, presentation, technical and research writing, whiteboarding, etc.)","Programming experience, particularly with Python-based scientific libraries such as Numpy, Scipy, JAX, PyTorch, or TensorFlow"],"x-skills-preferred":["A track record of building software, either in open source or as part of a company product or research papers","Large-scale system design, distributed systems","Distributed computation for ML, especially in the context of accelerators (e.g., sharding, multi-host computation)","C++ or broader programming experience","Data engineering and visualisation"],"datePosted":"2026-03-16T14:39:03.737Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"London, UK"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Advanced degree in computer science, electrical engineering, science, mathematics, or equivalent experience, Academic research experience in machine learning, publications, or research experience in related fields, Experience with some or all LLMs, Transformers, Diffusion models, Text diffusion, Large-scale distributed training, Strong communication skills (via discussion, presentation, technical and research writing, whiteboarding, etc.), Programming experience, particularly with Python-based scientific libraries such as Numpy, Scipy, JAX, PyTorch, or TensorFlow, A track record of building software, either in open source or as part of a company product or research papers, Large-scale system design, distributed systems, Distributed computation for ML, especially in the context of accelerators (e.g., sharding, multi-host computation), C++ or broader programming experience, Data engineering and visualisation"},{"@context":"https://schema.org","@type":"JobPosting","identifier":{"@type":"PropertyValue","name":"YubHub","value":"job_4e51470c-8f1"},"title":"Software Engineer, Accelerators","description":"Software Engineer, Accelerators
\nLocation
\nSan Francisco
\nEmployment Type
\nFull time
\nDepartment
\nScaling
\nCompensation
\nThe base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
\nBenefits
\nAbout the Team
\nThe Kernels team at OpenAI builds the low-level software that accelerates our most ambitious AI research.
\nWe work at the boundary of hardware and software, developing high-performance kernels, distributed system optimizations, and runtime improvements to make large-scale training and inference more efficient.
\nOur work enables OpenAI to push the limits by ensuring models - from LLMs to recommender systems - to run reliably on advanced supercomputing platforms. That includes adapting our software stack to new types of accelerators, tuning system performance end-to-end, and removing bottlenecks across every layer of the stack.
\nAbout the Role
\nOn the Accelerators team, you will help OpenAI evaluate and bring up new compute platforms that can support large-scale AI training and inference.
\nYour work will range from prototyping system software on new accelerators to enabling performance optimizations across our AI workloads.
\nYou’ll work across the stack, collaborating with both hardware and software aspects - working on kernels, sharding strategies, scaling across distributed systems, and performance modeling.
\nYou'll help adapt OpenAI's software stack to non-traditional hardware and drive efficiency improvements in core AI workloads. This is not a compiler-focused role, rather bridging ML algorithms with system performance - especially at scale.
\nIn this role, you will:
\nPerform system-level performance modeling, debug bottlenecks, and drive end-to-end optimization.
\nYou might thrive in this role if you have:
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_4e51470c-8f1","directApply":true,"hiringOrganization":{"@type":"Organization","name":"OpenAI","sameAs":"https://jobs.ashbyhq.com","logo":"https://logos.yubhub.co/openai.com.png"},"x-apply-url":"https://jobs.ashbyhq.com/openai/f386b209-1259-4b79-bf5a-aa97fc7ce77b","x-work-arrangement":"onsite","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$295K – $380K • Offers Equity","x-skills-required":["AI infrastructure","kernels","systems","hardware-software co-design","accelerator platforms","TPUs","custom silicon","exploratory architectures","kernels","sharding","runtime systems","distributed scaling techniques","LLMs","CNNs","recommender models","hardware efficiency","performance modeling","system debugging","software stack adaptation","novel architectures"],"x-skills-preferred":["mobile accelerators","data center-scale AI hardware"],"datePosted":"2026-03-06T18:27:12.141Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"AI infrastructure, kernels, systems, hardware-software co-design, accelerator platforms, TPUs, custom silicon, exploratory architectures, kernels, sharding, runtime systems, distributed scaling techniques, LLMs, CNNs, recommender models, hardware efficiency, performance modeling, system debugging, software stack adaptation, novel architectures, mobile accelerators, data center-scale AI hardware","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":295000,"maxValue":380000,"unitText":"YEAR"}}}]}