Applied Machine Learning Research Scientist

Cerebras

. Apply post-training techniques such as RLVR, RLHF, and GRPO to improve model performance .

Posted 11/6/2026full-timeToronto • CanadaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and optimizing machine learning pipelines, particularly for large language models, while applying advanced techniques such as RLVR and RLHF. Proficient in Python programming and familiar with ML frameworks like PyTorch, with a strong foundation in machine learning fundamentals and deep learning architectures.

Highest-signal resume keywords

Python ProgrammingMachine Learning FrameworksLarge Language ModelsReinforcement LearningDistributed Training Frameworks

ATS Keywords

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Hard Skills

Machine Learning SystemsML PipelinesModel Performance EvaluationData PipelinesDeep Learning ArchitecturesTransformersDebugging ML SystemsOptimization TechniquesSynthetic Data ApproachesCode Contributions

Tools & Technologies

PyTorchFSDPMegatron

Certifications & Qualifications

Bachelor’s DegreeMaster’s Degree

Industry Keywords

Reinforcement LearningModel Fine-TuningLLM PretrainingEvaluation PipelinesScalable Implementations

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Apply post-training techniques such as RLVR, RLHF, and GRPO to improve model performance
Build and maintain evaluation pipelines to measure model performance across tasks and domains
Debug issues across the ML stack, including data pipelines, training jobs, model outputs, and mixed or lower precision computation
Collaborate with researchers to translate ML ideas into efficient, scalable implementations
Design, implement, and scale ML pipelines across LLM pretraining, fine-tuning, and alignment
Work with large datasets, including dataset generation, filtering, and synthetic data approaches
Optimize training and inference workflows for performance, efficiency, and reliability
Contribute high-quality, maintainable code to shared ML infrastructure
Turn modern machine learning techniques into scalable, high-performance systems
Improve workflows for LLM pretraining, fine-tuning, and reinforcement learning-based post-training

Requirements

What you’ll need

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
4+ years of experience, including internships, research, or industry experience, working with machine learning systems
Strong programming skills in Python
Experience with ML frameworks such as PyTorch
Solid understanding of machine learning fundamentals
Familiarity with deep learning architectures, particularly transformers
Ability to read and understand modern ML papers and implement key ideas
Experience with large language models, including training, fine-tuning, and evaluation
Familiarity with reinforcement learning concepts
Experience with distributed training frameworks such as FSDP or Megatron
Experience with large-scale datasets and data pipelines
Experience debugging or optimizing ML systems for performance
Contributions to meaningful codebases, projects, or open-source systems

Benefits

Comp & perks

Publish and open source cutting-edge AI research
Job stability with startup vitality
Simple, non-corporate work culture that respects individual beliefs
Continuous learning, growth and support
Equal employment opportunity and inclusive work environment