Artificial Intelligence jobs in Canada

About Groq

Groq delivers fast, efficient AI inference. Our LPU-based system powers GroqCloud, providing businesses and developers with the speed and scale they need. Headquartered in Silicon Valley, we aim to make high-performance AI compute more accessible and affordable. When real-time AI is within reach, anything is possible. Build fast.

Senior Machine Learning Engineer: Post Training & Speculative Decoding

Mission: We are seeking a highly skilled Machine Learning Engineer to join our advanced model development team. This role focuses on pre-training, continued training, and post-training of models, with an emphasis on draft model optimization for speculative decoding and quantization-aware training (QAT). The ideal candidate has deep experience with training methodologies for open-weight models and performance tuning for inference.

Responsibilities & Outcomes:

• Lead pre-training and post-training efforts for draft models tailored to speculative decoding architectures.
• Conduct continued training and post-training of open-weight models for standard inference scenarios.
• Implement and optimize quantization-aware training pipelines for low-precision inference with minimal accuracy loss.
• Collaborate with model architecture, inference, and systems teams to evaluate model readiness across training and deployment stages.
• Develop tooling and evaluation metrics for training effectiveness, draft model fidelity, and speculative hit-rate optimization.
• Contribute to experimental designs for novel training regimes and speculative decoding strategies.

Ideal Candidates Have / Are:

• At least 5 years of experience in machine learning with a focus on model training.
• Proven experience with transformer-based architectures (e.g., LLaMA, Mistral, Gemma).
• Deep understanding of speculative decoding and draft model usage.
• Hands-on experience with quantization-aware training, including PyTorch QAT workflows or similar frameworks.
• Familiarity with open-weight foundation models and continued/pre-training techniques.
• Proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.

Preferred Qualifications:

• Experience optimizing models for fast inference and sampling in production environments.
• Exposure to distributed training, low-level kernel optimizations, and inference-time system constraints.
• Publications or contributions to open-source ML projects.

Attributes of a Groqster:

• Humility — Egos are checked at the door.
• Collaborative & Team Savvy — We make up the smartest person in the room together.
• Growth & Giver Mindset — Learn it all, share knowledge generously.
• Curious & Innovative — Take a creative approach to projects, problems, and design.
• Passion, Grit & Boldness — Limitless thinking fueling informed risk-taking.

If this sounds like you, we'd love to hear from you!

Compensation: At Groq, a competitive base salary is part of our comprehensive package, including equity and benefits. The exact salary range is TBD, based on skills, qualifications, experience, and internal benchmarks.

Location: Some roles may require proximity to our primary sites as indicated in the job description.

At Groq: We aim to hire and promote a diverse, exceptional workforce. Groq is an equal opportunity employer committed to diversity, inclusion, and belonging. We value differences in thought, beliefs, talent, expression, and backgrounds, believing they make us better.

Groq is an equal opportunity employer that considers all qualified applicants without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, disability, or protected veteran status. We also provide accommodations for individuals with disabilities during the application process.

Required Experience: Senior IC

Key Skills: Industrial Maintenance, Machining, Mechanical Knowledge, CNC, Precision Measuring Instruments, Schematics, Maintenance, Hydraulics, Plastics Injection Molding, Programmable Logic Controllers, Manufacturing, Troubleshooting

Employment Type: Full Time

Experience: [Specify years]

Vacancy: 1