{"id":1421335,"url":"https://alion.io/job/cerebras-systems-ml-algorithm-mapping-and-performance-engineer-core-ml","title":"ML Algorithm Mapping and Performance 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"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":198000,"max_usd":433000,"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":"HPC","optional":false},{"name":"Machine Learning","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"JAX","optional":true},{"name":"KV Cache","optional":true},{"name":"LLM","optional":true},{"name":"PyTorch","optional":true},{"name":"PyTorch C++","optional":true},{"name":"Quantization","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-09-28T17:32:51Z","employer_posted_date":"2026-09-28","last_verified_at":"2026-10-16T13:52:32Z","board_verified":true,"closed_at":null,"days_open":17,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":17},"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.\nCerebras 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 algorithms for efficient large-scale training and inference. We are looking for an engineer who can determine how these algorithms should be mapped to the Cerebras architecture, when they outperform competing approaches, and how their advantages change as models, workloads, and hardware systems scale.\nYou will combine analytical performance modeling, empirical benchmarking, and hands-on prototyping to characterize the efficiency frontiers of emerging ML algorithms. Your work will span kernel-level and end-to-end performance, helping the team reason about trade-offs among model quality, latency, throughput, memory, communication, and compute utilization.\nThis role will directly influence which research ideas Core ML pursues, how those ideas are implemented on current Cerebras systems, and which capabilities should be considered in future generations of hardware and software.\nResponsibilities\nBuild analytical and empirical performance models for state-of-the-art ML training and inference algorithms.\n\nCharacterize asymptotic behavior and identify how algorithmic trade-offs change with model size, sequence length, batch size, parallelism, and hardware scale.\n\nConstruct Pareto frontiers across model quality, latency, throughput, memory footprint, communication, and compute cost.\n\nDevelop prototype implementations and benchmarks for the Cerebras WSE and relevant GPU or software baselines.\n\nAnalyze system behavior to identify kernel, compiler, runtime, communication, and algorithmic bottlenecks.\n\nEvaluate emerging techniques in areas such as parallel token generation, diffusion and speculative decoding, attention, sparsity, mixture-of-experts, low-precision computation, and distributed training.\n\nPartner with researchers and kernel, compiler, runtime, inference, and architecture teams to recommend high-value implementation and co-design directions.\n\nDevelop tools and visualizations that make performance projections, measurements, and design trade-offs understandable across engineering and research teams.\n\nClearly communicate conclusions, assumptions, limitations, and recommendations through technical reports, presentations, and design reviews.\n\nSkills & Qualifications\nBachelor’s, Master’s, PhD, or equivalent practical experience in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related field.\n\nStrong foundation in computer architecture, parallel computing, and systems performance.\n\nStrong understanding of machine learning fundamentals and ML systems, including how model and algorithmic choices affect compute, memory, communication, accuracy, and scaling behavior.\n\nExperience with analytical performance modeling, algorithmic complexity analysis, benchmarking, or system simulation.\n\nStrong analytical and problem-solving skills, including the ability to reason from first principles about compute, memory, and communication costs.\n\nProficiency in Python and comfort with C++.\n\nExperience profiling and debugging performance in an ML, HPC, CPU, GPU, or accelerator-based system.\n\nAbility to move between mathematical analysis, experimental validation, and practical engineering recommendations.\n\nPreferred Skills & Qualifications\nExperience with roofline analysis, CPU or GPU simulators, kernel optimization, or hardware-software co-design.\n\nFamiliarity with CUDA, Triton, PyTorch, JAX, or open-source LLM training and inference systems.\n\nUnderstanding of transformer internals, including attention variants, KV-cache strategies, model parallelism, sparsity, quantization, and parallel generation.\n\nResearch publications, patents, or significant open-source contributions related to ML systems, computer architecture, or computational efficiency.\n\nExperience evaluating technology choices for future hardware or software architectures.\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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