Senior Research Engineer - Inference ML - United States and Canada, United States

Originally posted 2 October 2026 by the employer.

Adapt advanced language and vision models to run efficiently on the Cerebras architecture.

About the role

As a Senior Research Engineer on the Inference ML team, you will adapt today's most advanced language and vision models to run efficiently on the Cerebras architecture. This role focuses on pushing the frontier of speculative decoding, large-model pruning and compression, sparse attention, and sparsity-driven techniques.

What you'll do

• Design, implement, and optimize transformer architectures for NLP and computer vision on Cerebras hardware.
• Research and prototype novel inference algorithms and model architectures emphasizing speculative decoding, pruning/compression, sparse attention, and sparsity.
• Train models to convergence, perform hyperparameter sweeps, and analyze results.
• Bring up new models on the Cerebras system, validate functional correctness, and troubleshoot 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.

What you'll need

• 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.
• Proficiency with at least one major ML framework (PyTorch, Transformers, vLLM, or SGLang).
• Deep understanding of transformer-based models in language and/or vision domains, with demonstrated experience implementing and optimizing them.
• Strong foundation in performance optimization on specialized hardware (e.g., GPUs, TPUs, or HPC interconnects).
• Deep understanding of modern ML architectures and strong intuition for optimizing their performance, particularly for inference workloads using sparse attention, pruning/compression, and speculative decoding.

Nice to have

• 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.
• Experience with speculative decoding, neural network pruning and compression, sparse attention, quantization, sparsity, post-training techniques, and inference-focused evaluations.
• Familiarity with large-scale model training and deployment, including performance and cost trade-offs in production systems.
• Triton/CUDA experience.

Skills: AI accelerator, inference algorithms, transformer architectures, PyTorch, speculative decoding, large language models