Asks for

The ad says“Experience with cloud platforms (e.g., AWS, GCP, Azure) and workload managers (e.g., Ray, SLURM).”
The ad says“Experience with cloud platforms (e.g., AWS, GCP, Azure) and workload managers (e.g., Ray, SLURM).”
The ad says“3+ years of experience in designing and implementing distributed ML training frameworks, with recent experience using e.g. Megatron, DeepSpeed, HuggingFace Accelerate, FSDP, vLLM, and/or verl.”
The ad says“3+ years of experience in designing and implementing distributed ML training frameworks, with recent experience using e.g. Megatron, DeepSpeed, HuggingFace Accelerate, FSDP, vLLM, and/or verl.”
The ad says“Familiarity with containerization tools (e.g., gRPC, Docker, Kubernetes).”
The ad says“3+ years of experience in designing and implementing distributed ML training frameworks, with recent experience using e.g. Megatron, DeepSpeed, HuggingFace Accelerate, FSDP, vLLM, and/or verl.”
The ad says“Experience with cloud platforms (e.g., AWS, GCP, Azure) and workload managers (e.g., Ray, SLURM).”
The ad says“Familiarity with containerization tools (e.g., gRPC, Docker, Kubernetes).”
The ad says“3+ years of experience in designing and implementing distributed ML training frameworks, with recent experience using e.g. Megatron, DeepSpeed, HuggingFace Accelerate, FSDP, vLLM, and/or verl.”
The ad says“Familiarity with containerization tools (e.g., gRPC, Docker, Kubernetes).”
The ad says“3+ years of experience in designing and implementing distributed ML training frameworks, with recent experience using e.g. Megatron, DeepSpeed, HuggingFace Accelerate, FSDP, vLLM, and/or verl.”
The ad says“Experience with GPU profiling tools (e.g. PyTorch profiler, PyProf, NVIDIA Nsight).”
The ad says“Experience with GPU profiling tools (e.g. PyTorch profiler, PyProf, NVIDIA Nsight).”
The ad says“Experience with GPU profiling tools (e.g. PyTorch profiler, PyProf, NVIDIA Nsight).”
The ad says“Experience with cloud platforms (e.g., AWS, GCP, Azure) and workload managers (e.g., Ray, SLURM).”
The ad says“Experience with cloud platforms (e.g., AWS, GCP, Azure) and workload managers (e.g., Ray, SLURM).”
The ad says“Familiarity with data infrastructures and platforms (e.g., vector databases).”
The ad says“3+ years of experience in designing and implementing distributed ML training frameworks, with recent experience using e.g. Megatron, DeepSpeed, HuggingFace Accelerate, FSDP, vLLM, and/or verl.”
The ad says“3+ years of experience in designing and implementing distributed ML training frameworks, with recent experience using e.g. Megatron, DeepSpeed, HuggingFace Accelerate, FSDP, vLLM, and/or verl.”

We are seeking a senior distributed machine learning (ML) research developer to join our team working on a novel AI safety agenda. In this role, you will work closely with ML research scientists to solve difficult training and inference problems using very large models.

Key Responsibilities

Collaborate with researchers to accelerate research, model training and inference, and facilitate the use of large-scale models in distributed computing environments.
Investigate performance bottlenecks, profile research experiment code, debug reported issues, and optimize the utilization of computing resources.
Develop tools and libraries to simplify and orchestrate the use of distributed computing resources for research experiments.
Establish, document, and maintain best practices for large-scale, distributed ML model development workflows.

Skills and Qualifications

A degree in a relevant computer science field (e.g., computer science, computer engineering, software engineering) is required. An advanced degree (master’s or PhD) related to machine learning or distributed ML systems is preferred but not required if the candidate demonstrates exceptional abilities and experience.
3+ years of experience in designing and implementing distributed ML training frameworks, with recent experience using e.g. Megatron, DeepSpeed, HuggingFace Accelerate, FSDP, vLLM, and/or verl.
Ability to collaborate effectively with cross-functional teams, document best practices, and stay updated with the latest advancements in ML and software development.
Experience with cloud platforms (e.g., AWS, GCP, Azure) and workload managers (e.g., Ray, SLURM).
Experience with GPU profiling tools (e.g. PyTorch profiler, PyProf, NVIDIA Nsight).
Familiarity with containerization tools (e.g., gRPC, Docker, Kubernetes).
Familiarity with data infrastructures and platforms (e.g., vector databases).
A track record of contributing to high-quality research projects in deep learning.

The title of Engineer is used for reference purposes and may or may not be the official title of the applicant based on jurisdiction.

About LawZero

LawZero is a non-profit organization committed to advancing research and creating technical solutions that enable safe-by-design AI systems. Its scientific direction is based on new research and methods proposed by Professor Yoshua Bengio, the most cited AI researcher in the world. Based in Montreal, LawZero’s research aims to build non-agentic AI that learns primarily to understand the world rather than to act in it, giving truthful answers to questions based on transparent and externalized probabilistic reasoning. Such AI systems could be used to accelerate scientific discovery, to provide oversight for agentic AI systems, and to advance the understanding of AI risks and how to avoid them. LawZero believes that AI should be cultivated as a global public good—developed and used safely towards human flourishing. For more information, visit www.lawzero.org

You belong here

At LawZero, diversity is important to us. We value a work environment that is fair, open and respectful of differences. We welcome applications from highly qualified individuals interested in working towards our mission in a respectful, inclusive and collaborative setting.

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