{"id":42503,"url":"https://alion.io/job/zyphra-research-engineer-ai-performance-kernel-optimization","title":"Research Engineer - AI Performance & Kernel Optimization","company":{"id":8989,"name":"Zyphra","domain":"zyphra.com","url":"https://alion.io/company/zyphra","size_band":"1-10","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"D","score":48,"open_postings":16,"ghost_share":0.875,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":168000,"max_usd":368000,"period":"year","method":"role_country_seniority_unknown","sample_n":4027},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"AWS Trainium","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"HPC","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"TPU","optional":false},{"name":"Triton","optional":false},{"name":"World Models","optional":false}],"status":"live","first_seen_at":"2026-03-16T22:54:23Z","employer_posted_date":"2026-03-16","last_verified_at":"2026-10-16T04:07:32Z","board_verified":true,"closed_at":null,"days_open":213,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":213},"description":"Zyphra is an artificial intelligence company based in San Francisco, California.\nThe Role:\nAs a Research Engineer - AI Performance & Kernel Optimization, you will improve and optimize the performance of our large-scale language model training and inference stacks. You will work closely with our pretraining and inference teams to identify bottlenecks, design and implement highly optimized kernels, and push the limits of throughput, latency, and hardware utilization across a range of accelerator platforms. This role is suited for someone who enjoys deep systems work, cares about performance at every level of the stack, and is excited to translate low-level optimizations into meaningful gains for frontier-scale AI systems.\nYou’ll Work Across:\nKernel development and optimization for large-scale ML workloads, using any level of the stack from PTX/assembly to CUDA, HIP, Triton, or other GPU DSLs\n\nPerformance tuning for training and inference stacks across GPUs and other accelerators\n\nProfiling and eliminating bottlenecks in memory movement, communication, scheduling, and compute utilization\n\nOptimizing distributed training and inference systems for large MoE models, including large-scale model parallelism\n\nPortability and optimization across non-NVIDIA hardware, with special interest in AMD hardware such as the MI300x and MI355x\n\nCollaboration with research and infrastructure teams to turn systems improvements into real-world model training and inference gains\n\nWhat We're Looking For / Requirements:\nStrong engineering aptitude for building reliable, high-performance systems\n\nExcellent low-level performance intuition and the ability to reason about hardware-software interactions\n\nAre excited to rapidly learn new systems, tools, and hardware environments\n\nExcellent communication and collaboration skills, with the ability to work effectively across research and engineering teams\n\nEnjoy diving deep into the weeds and hunting down the last 10-20% of performance\n\nQualifications / Additional Skills:\nExperience writing highly performant GPU kernels at any level of abstraction-PTX, CUDA, HIP, Triton, or other kernel DSLs\n\nExperience optimizing ML workloads for large-scale training, ideally in language model pretraining or inference environments\n\nExperience with non-NVIDIA accelerator hardware, such as AMD, AWS Trainium, Google TPU, Qualcomm, ARM, Intel, and custom ASICs\n\nStrong understanding of distributed training systems and parallelism schemes, including data parallelism, tensor/model parallelism, pipeline parallelism, sharding, and communication/computation overlap\n\nExperience with performance engineering in other demanding parallel computing environments such as HPC, quantitative finance, scientific computing, graphics, compilers, or numerical simulation\n\nStrong systems intuition around memory hierarchy, bandwidth constraints, kernel fusion, launch overhead, communication overhead, and hardware utilization\n\nExperience using profiling and debugging tools to drive performance improvements\n\nFamiliarity with infrastructure underlying large-scale training and inference, including collective communication libraries, and runtime performance analysis\n\nBackground in a highly technical field such as physics, mathematics, theoretical computer science, computer science, or electrical engineering\n\nAny HPC experience is a strong plus\n\nWhy Work at Zyphra:\nOur research methodology is grounded in methodical, step-by-step approaches to ambitious goals. Both deep research and engineering excellence are equally valued\n\nWe strongly value new and crazy ideas and are very willing to bet big on new ideas\n\nWe move as quickly as we can; we aim to minimize the bar to impact as low as possible\n\nWe all enjoy what we do and love discussing AI\n\nBenefits and Perks:\nComprehensive medical, dental, vision, and FSA plans\n\nCompetitive compensation and 401(k) plan\n\nRelocation and immigration support on a case-by-case basis\n\nIn-office snacks and meals provided\n\nUnlimited PTO and company holidays\n\nIn-person team in San Francisco with a collaborative, high-energy environment","description_format":"text","description_chars":4100,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Unlimited PTO"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Foundation Models","AI Research Labs"],"lifecycle":[{"event":"open","at":"2026-03-16T22:54:23Z"}],"visa":[],"liveness":{"score":3,"band":"cold","label":"Long shot","p_open":1,"p_active":0.093,"p_room":0.28,"age_days":213,"expected_fill_days":26,"reasons":["conf:1","stale_co","ghost","win:tail","crowd:"],"computed_at":"2026-10-16T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/zyphra-research-engineer-ai-performance-kernel-optimization","json_url":"https://alion.io/job/zyphra-research-engineer-ai-performance-kernel-optimization.json","meta":{"generated_at":"2026-10-16T06:44:48Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":4553,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-17T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":8989},"rest":"https://alion.io/mcp/rest/get_company?id=8989"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fzyphra-research-engineer-ai-performance-kernel-optimization"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fzyphra-research-engineer-ai-performance-kernel-optimization"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fzyphra-research-engineer-ai-performance-kernel-optimization"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/zyphra-research-engineer-ai-performance-kernel-optimization\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fzyphra-research-engineer-ai-performance-kernel-optimization"}]}