Member of Technical Staff, Integration/RL Team (Research Engineer)

Highlights

• Scale distributed RL for LLM post-training to unprecedented levels.
• Build tooling that accelerates frontier model research and training.
• Optimize SFT, preference tuning and RL to speed up training cycles.

What they offer

• Weekly $75 lunch stipend
• Full health and dental benefits including mental health budget
• RRSP matching, 401K and Pension Scheme
• 100% parental leave top-up for up to 6 months
• Arts, culture, fitness, wellness and workspace enrichment benefits
• Education and learning stipend for conferences, courses and coaching
• 6 weeks of paid vacation (30 working days)
• Travel budget for remote office visits plus annual company offsite
• $500 home office stipend and co-working benefit

Ideal candidate

A software-obsessed engineer passionate about quality and clean code who thrives in fast-paced research environments and loves collaborating to tune large models and unblock scientists.

Requirements

• Extremely strong software engineering skills
• Value test-driven development, clean code and reducing technical debt
• Proficiency in Python and ML frameworks such as JAX, PyTorch and/or XLA/MLIR
• Experience using and debugging large-scale distributed training strategies, including memory/speed profiling
• Experience with distributed training infrastructure (Kubernetes) and frameworks like Ray
• Hands-on experience with post-training, with emphasis on scalability and performance
• Experience in ML, LLM and RL academic research