Senior Research Engineer - Inference ML

# Senior Research Engineer - Inference MLCerebrasUnited States and CanadaFull-timeAI tools:PyTorchTransformersvLLMSGLangTritonCUDAUpload your resume once and Scout applies for youApply with ScoutShare this job:LinkedInXSave imageThis role adapts and optimizes language and vision models for inference on Cerebras hardware. You will work with researchers and engineers on inference methods such as speculative decoding, model compression, and sparse attention, taking projects from research and prototyping through validation and optimization.* Permanent* Hybrid* 2–7+ years, depending on education* Bachelor's, Master's, or PhD in a related technical field, o### Skills and expertisePythonC++PyTorchTransformersvLLMSGLangTritonCUDA### Key responsibilities* Implement and optimize transformer architectures for language and vision inference on Cerebras hardware.* Prototype inference algorithms involving speculative decoding, pruning, compression, and sparse attention.* Train, validate, profile, and optimize models and tools for inference performance.## How this job comparesCompared with 410 open senior machine learning jobs on October 10, 2026.Pay: This job does not publish its pay. The 143 open senior machine learning jobs that publish US pay start at $186K a year on average.Similar open jobs: We Love AI Jobs lists 410 open senior machine learning jobs at 176 companies.Remote work: This job is hybrid. 90 of the 410 senior machine learning jobs (22%) are remote.AI in this role: High. AI is the core of the work: building, training or deploying AI systems. The core work is adapting, training, evaluating, and optimizing language and vision models for inference. The posting specifically emphasizes model architectures and inference techniques such as speculative decoding and sparse attention.## Full descriptionCerebras 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.This 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.Cerebras 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.About The RoleAs a Senior Research Engineer on the Inference ML team at Cerebras Systems, you will adapt today's most advanced language and vision models to run efficiently on our flagship Cerebras architecture. You'll work alongside ML researchers and engineers to design, prototype, validate, and optimize models, gaining end-to-end exposure to cutting-edge inference research on the world's fastest AI accelerator.You 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.Responsibilities• Design, implement, and optimize state-of-the-art transformer architectures for NLP and computer vision on Cerebras hardware.• Research 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.• Train models to convergence, perform hyperparameter sweeps, and analyze results to inform next steps.• Bring up new models on the Cerebras system, validate functional correctness, and troubleshoot any integration issues.• Profile and optimize model code using Cerebras tools to maximize throughput and minimize latency.• Develop diagnostic tooling or scripts to surface performance bottlenecks and guide optimization strategies for inference workloads.• Collaborate across teams, including software, hardware, and product, to drive projects from inception through delivery.Minimum Qualifications• One of the following education and experience combinations:• Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related technical field AND 7+ years of ML software development experience, OR• Master’s degree in Computer Science or related technical field AND 4+ years of software development experience, OR• PhD in Computer Science or related technical field with 2+ years of relevant research or industry experience, OR• Equivalent practical experience.• 4+ years of experience testing, maintaining, or launching software products, including 2+ years of experience with software design and architecture.• 3+ years of experience in software development focused on machine learning (e.g., deep learning, large language models, or computer vision).• Strong programming skills in Python and/or C++.• Experience with Generative AI and Machine Learning systems.• Evidence of research impact in machine learning, such as publications at top conferences (NeurIPS, ICLR, ICML, ACL, EMNLP, MLSys) or comparable contributions to widely used open-source projects or high-quality preprints.Preferred Qualifications• Master’s degree or PhD in Computer Science, Computer Engineering, or a related technical field.• Experience independently driving complex ML or inference projects from prototype to production-quality implementations.• Hands-on experience with relevant ML frameworks such as PyTorch, Transformers, vLLM, or SGLang.• Experience with large language models, mixture-of-experts models, multimodal learning, or AI agents.
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