{"id":1730734,"url":"https://alion.io/job/cerebras-systems-senior-research-engineer-inference-ml","title":"ML Research Scientist - Inference 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":74,"open_postings":25,"ghost_share":0,"stale_share":0.84,"repost_share":0,"time_to_fill_p50_days":86,"computed_at":"2026-10-19T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":180000,"max_usd":338000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":451},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Cerebras","optional":false},{"name":"Computer Vision","optional":false},{"name":"Edge AI","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"SGLang","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"Transformers","optional":false},{"name":"vLLM","optional":false},{"name":"C++","optional":true},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"HPC","optional":true},{"name":"Mixture of Experts","optional":true},{"name":"Post-training","optional":true},{"name":"Quantization","optional":true},{"name":"TPU","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-10-02T23:45:55Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-19T22:46:29Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"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\nAs anML Research Scientist on the Inference ML team at Cerebras Systems, you will invent and evaluate advanced algorithms, methods and model architectures for efficient inference on our flagship Cerebras architecture. You'll work alongside ML researchers and engineers to design, prototype, validate, and optimize methods and models, contributing to cutting-edge inference research on the world's fastest AI accelerator.\nYou will focus on pushing the frontier of speculative decoding, large-model pruning and compression, sparse attention, and sparsity-driven techniques to deliver low-latency, high-throughput inference at scale.\nHybrid role in Toronto, ON, CA or Sunnyvale, CA, USA.\nResponsibilities\nResearch and prototype novel inference algorithms and model architectures that exploit the unique capabilities of Cerebras hardware, with emphasis on speculative decoding, pruning/compression, sparse attention, and sparsity.\n\nDesign and evaluate new transformer architectures and variants for NLP and computer vision to improve the trade-offs among quality, latency, throughput, and compute.\n\nIdentify and pursue high-impact research directions at the intersection of model architecture, inference systems and hardware.\n\nTrain or adapt models where needed, and measure and analyze inference performance using Cerebras tools to maximize throughput and minimize latency.\n\nCollaborate with engineering and performance teams to understand practical constraints and help translate successful research into usable systems, from inception through delivery.\n\nPublish and present research findings and contribute to open-source releases where appropriate.\n\nMinimum Qualifications\nPhD in machine learning, computer science, or a closely related technical field with 3+ years of relevant research or industry experience, OR a master’s degree with an exceptional record of original, independent research and 4+ years of relevant experience.\n\nDeep understanding of modern ML architectures and generative models in language and/or vision domains, machine learning systems, and the principles that govern their inference behavior.\n\nAbility to reason about model and algorithm performance, particularly for inference workloads.\n\nAbility to identify practically relevant research questions, develop novel methods, and evaluate their effects on model quality and inference efficiency.\n\nEvidence of research impact in machine learning, statistics, information theory, or a closely related field, with clear relevance to modern ML, through publications in leading conferences or journals, high-quality preprints, or widely used open-source contributions.\n\nStrong Python programming skills and proficiency with relevant ML frameworks such as PyTorch, Transformers, vLLM, or SGLang.\n\nPreferred Qualifications\nOriginal research in one or more of: speculative decoding, neural network pruning and compression, sparse attention, quantization, mixture-of-experts models, sparsity, post-training techniques, or inference-aware design.\n\nTrack record of owning complex ML or inference projects end-to-end, from initial idea through iterative research, implementation, and evaluation to measurable impact in production.\n\nExperience designing novel model architectures.\n\nStrong foundation in performance optimization on specialized hardware (e.g., GPUs, TPUs, or HPC interconnects), with insights into how hardware characteristics can create new algorithmic opportunities.\n\nFamiliarity with large-scale model training and deployment, including performance and cost trade-offs in production systems.\n\nExperience with Triton, CUDA, or C++ is a plus.\n\nRequired Skills & Attributes\nSelf-directed mindset with a demonstrated ability to identify and tackle the most impactful problems.\n\nCollaborative and generous with colleagues, with the ability to communicate ideas clearly across research and engineering teams.\n\nGenuine passion for AI and a drive to push the limits of inference performance.\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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