{"id":1421336,"url":"https://alion.io/job/cerebras-systems-ml-runtime-and-kernel-engineer-core-ml","title":"ML Runtime and Kernel Engineer - Core ML","company":{"id":31,"name":"Cerebras Systems","domain":"cerebras.ai","url":"https://alion.io/company/cerebras-systems","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":73,"open_postings":26,"ghost_share":0,"stale_share":0.885,"repost_share":0,"time_to_fill_p50_days":80,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States","Toronto, Canada"],"countries":["US","CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":198000,"max_usd":434000,"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":"AI Agents","optional":false},{"name":"C++","optional":false},{"name":"Cerebras","optional":false},{"name":"Edge AI","optional":false},{"name":"JAX","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"HPC","optional":true},{"name":"KV Cache","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-09-28T17:29:09Z","employer_posted_date":"2026-09-28","last_verified_at":"2026-10-16T22:12:14Z","board_verified":true,"closed_at":null,"days_open":18,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":18},"description":"Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. \nThis order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.\nCerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.\nAbout The Role\nThe Core ML team develops novel machine learning algorithms that take advantage of the unique capabilities of the Cerebras Wafer-Scale Engine. Our work spans efficient LLM training and inference, parallel and diffusion-based generation, sparsity, scaling laws, and training dynamics.\nWe are looking for an engineer to bridge the gap between promising research ideas and efficient execution on Cerebras systems. You will work across ML frameworks, compilers, runtimes, and low-level kernels to implement new algorithmic capabilities, diagnose performance bottlenecks, and turn research prototypes into robust, high-performance demonstrations.\nDepending on your background, your work may emphasize runtime capabilities such as token orchestration, scheduling, communication, and distributed execution; low-level kernel development for novel ML operations; or a combination of both.\nResponsibilities\nDesign and implement runtime components and high-performance kernels required by novel Core ML algorithms.\n\nTranslate research prototypes into efficient implementations for the Cerebras platform, including reference implementations and comparisons on GPUs where useful.\n\nProfile and debug performance across the ML framework, compiler, runtime, communication, and kernel layers.\n\nOptimize computation, memory movement, communication, and concurrency for large-scale training and low-latency inference.\n\nDevelop benchmarks, instrumentation, and automated tests that validate functionality, performance, and numerical correctness.\n\nCollaborate closely with Core ML researchers and compiler, runtime, kernel, and inference engineers to evaluate design alternatives and deliver end-to-end capabilities.\n\nContribute to software architecture and roadmap decisions by identifying recurring limitations and high-leverage platform improvements.\n\nSkills & Qualifications\nBachelor’s, Master’s, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Electrical Engineering, or a related field.\n\nExperience developing high-performance systems software, ML systems, runtimes, compilers, or computational kernels.\n\nStrong programming skills in C++ and Python.\n\nSolid understanding of parallel programming, memory management, concurrency, data structures, and performance optimization.\n\nProven ability to debug and profile complex software across multiple layers of a system.\n\nFamiliarity with modern machine learning architectures and frameworks such as PyTorch or JAX.\n\nAbility to work effectively with researchers and translate evolving algorithmic requirements into reliable software.\n\nPreferred Skills & Qualifications\nExperience with CUDA, Triton, low-level assembly, accelerator programming, or a C-like domain-specific language.\n\nExperience with compiler internals, distributed runtimes, custom hardware interfaces, or HPC systems.\n\nUnderstanding of machine learning fundamentals and ML systems, with the ability to reason about how algorithmic choices affect accuracy, systems implementation and performance.\n\nFamiliarity with LLM training or inference, including attention, KV-cache management, parallel generation, or distributed execution.\n\nExperience developing software in an industrial or academic research environment where requirements evolve through experimentation.\n\nContributions to significant open-source systems, ML frameworks, compilers, or kernel libraries.\n\nWhy Join Cerebras\nPeople who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:\nBuild a breakthrough AI platform beyond the constraints of the GPU.\n\nPublish and open source their cutting-edge AI research.\n\nWork on one of the fastest AI supercomputers in the world.\n\nEnjoy job stability with startup vitality.\n\nOur simple, non-corporate work culture that respects individual beliefs.\n\nFind out more about what it's like to work at Cerebras here!\nApply today and become part of the forefront of groundbreaking advancements in AI!\nCerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.\nThis website or its third-party tools process personal data. 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