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We’re looking for Research Engineers who enjoy getting their hands dirty: building experiments, debugging models, scaling training pipelines, and turning research ideas into working systems. This is a highly practical role. You’ll work closely with scientists and other engineers to implement new methods, run large-scale experiments, and help shape the infrastructure that supports our research programs. You don’t need to have everything figured out yet. We care more about curiosity, strong fundamentals, and a willingness to learn quickly in a fast-moving research environment.
Job Responsibility:
Building experiments
Debugging models
Scaling training pipelines
Turning research ideas into working systems
Work closely with scientists and other engineers to implement new methods, run large-scale experiments, and help shape the infrastructure that supports our research programs
Requirements:
Have a strong engineering background in machine learning, NLP, or related areas (through a Master’s degree, industry experience, or equivalent hands-on work)
Enjoy writing clean, reliable code and building systems that others can use and extend
Are comfortable experimenting, running ablations, analyzing results, and iterating quickly
Have experience with deep learning frameworks and model optimization techniques (PyTorch, distributed training, RLHF, finetuning, evaluation frameworks)
Like collaborating closely with researchers and translating ideas into practical implementations
Are excited to grow your research instincts while staying grounded in engineering excellence
What we offer:
An open and inclusive culture and work environment
Work closely with a team on the cutting edge of AI research
Weekly lunch stipend, in-office lunches & snacks
Full health and dental benefits, including a separate budget to take care of your mental health
100% Parental Leave top-up for up to 6 months
Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement
Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend