Cerebras is hiring a Applied ML Research Scientist in Toronto!
Cerebras, the company behind a wafer-scale AI chip substantially larger than conventional GPUs, is hiring an Applied ML Research Scientist for its Toronto team. The position sits between modelling and systems work: rather than inventing new algorithms, the focus is on understanding existing techniques deeply and making them run efficiently at scale on Cerebras hardware. The successful candidate will support LLM pretraining, fine-tuning and reinforcement-learning-based post-training, building pipelines, diagnosing failures and refining data and evaluation strategies. The posting notes a recent multi-year partnership with OpenAI involving a large-scale inference deployment, framing the role as production-oriented engineering rather than academic publication. Cerebras is hiring at multiple levels for this opening.
Key responsibilities
- Apply post-training methods such as RLVR, RLHF and GRPO to lift model quality
- Design and maintain evaluation pipelines spanning multiple tasks and domains
- Trace and fix problems across the ML stack, from data pipelines and training jobs to model outputs and mixed-precision numerics
- Partner with researchers to convert ML concepts into efficient, scalable implementations
- Build and scale pipelines across pretraining, fine-tuning and alignment stages
- Handle large datasets, including generation, filtering and synthetic data work
- Tune training and inference workflows for speed, efficiency and reliability
- Write maintainable code for shared ML infrastructure
Key requirements
- Bachelor's or Master's in computer science, engineering or a related discipline
- At least four years of combined internship, research or industry experience with ML systems
- Strong Python programming ability
- Hands-on experience with frameworks such as PyTorch
- Grounded understanding of machine learning fundamentals
- Familiarity with transformer architectures
- Able to read modern ML papers and reproduce their key ideas
- Seniority: Mid-level to senior (multiple levels open, 4+ years experience)
- Work mode: Not specified
- Key skills: Python, PyTorch, Large language models, Reinforcement learning (RLHF, RLVR, GRPO), Post-training and alignment, Evaluation pipelines, Distributed training (FSDP, Megatron), Large-scale data pipelines, Mixed-precision debugging, Model optimization
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