Senior Research Engineer - Inference Ml

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.
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 atCerebrasSystems, you will adapt today’s most advanced language and vision models to run efficiently on our flagshipCerebrasarchitecture.You’llwork alongside ML researchers and engineers to design, prototype,validate, andoptimizemodels, gaining end-to-end exposure tocutting-edgeinference 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, andoptimizestate-of-the-arttransformer architectures for NLP and computer vision onCerebrashardware.
Research and prototype novel
inference algorithms
and model architectures that exploit the unique capabilities ofCerebrashardware, 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 theCerebrassystem,validatefunctional correctness, and troubleshoot any integration issues.
Profile and optimize model code usingCerebrastools 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, andproduct, to drive projects frominceptionthrough delivery.
Minimum Qualifications
One of the following education and experience combinations:
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, orSGLang .
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 is a big plus.
Required Skills & Attributes
Proficiencywith at least one major ML framework (
PyTorch, Transformers,vLLM, orSGLang
).
Deep understanding of transformer-based models in language and/or vision domains, withdemonstratedexperience implementing andoptimizingthem.
Proven ability to translate research ideas into robust code: implementing new model variants, training strategies, and evaluation workflows end-to-end.
Strong foundationin performance optimization on specialized hardware (e.G., GPUs, TPUs, or HPC interconnects).
Deep understanding of modern ML architectures and strong intuition foroptimizingtheir performance, particularly for inference workloads using
sparse attention, pruning/compression, and speculative decoding .
Track recordof owning problems end-to-end and autonomouslyacquiringwhatever knowledge is needed to deliver results.
Self-directed mindset with a demonstrated ability toidentifyand tackle the most impactful problems.
Collaborative approach with humility, eagerness to help colleagues, and commitment to team success.
Genuine passion for AI and a drive to push the limits of inference performance.
Hybrid role in Toronto, ON, CA or Sunnyvale, CA, USA.
Why Join CerebrasPeople 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:
Build a breakthrough AI platform beyond the constraints of the GPU.
Publish and open source their cutting-edge AI research.
Work on one of the fastest AI supercomputers in the world.
Enjoy job stability with startup vitality.
Our simple, non-corporate work culture that respects individual beliefs.
Find out more about what it’s like to work at Cerebras
here !
Cerebras 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.
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