Asks for

The ad says“5+ years of experience designing, implementing, and managing large-scale distributed data processing systems, or working within large-scale distributed ML data frameworks, with recent experience using e.g. Ray, Apache Spark, workflow orchestrators, Apache Arrow, and/or Parquet.”
The ad says“5+ years of experience designing, implementing, and managing large-scale distributed data processing systems, or working within large-scale distributed ML data frameworks, with recent experience using e.g. Ray, Apache Spark, workflow orchestrators, Apache Arrow, and/or Parquet.”
The ad says“Experience with dataset versioning, lineage, and reproducibility tooling.”
The ad says“5+ years of experience designing, implementing, and managing large-scale distributed data processing systems, or working within large-scale distributed ML data frameworks, with recent experience using e.g. Ray, Apache Spark, workflow orchestrators, Apache Arrow, and/or Parquet.”
The ad says“Familiarity with containerization tools (e.g., Docker, Enroot).”
The ad says“Familiarity with containerization tools (e.g., Docker, Enroot).”
The ad says“Experience profiling and optimizing throughput and cost across heterogeneous workloads, including GPU-accelerated stages.”
The ad says“Experience with workload managers (e.g., Ray, Kubernetes, Slurm).”
The ad says“Experience with dataset versioning, lineage, and reproducibility tooling.”
The ad says“5+ years of experience designing, implementing, and managing large-scale distributed data processing systems, or working within large-scale distributed ML data frameworks, with recent experience using e.g. Ray, Apache Spark, workflow orchestrators, Apache Arrow, and/or Parquet.”
The ad says“5+ years of experience designing, implementing, and managing large-scale distributed data processing systems, or working within large-scale distributed ML data frameworks, with recent experience using e.g. Ray, Apache Spark, workflow orchestrators, Apache Arrow, and/or Parquet.”
The ad says“Experience with dataset versioning, lineage, and reproducibility tooling.”
The ad says“Experience with workload managers (e.g., Ray, Kubernetes, Slurm).”
The ad says“Familiarity with data infrastructures and platforms (e.g., vector databases).”
The ad says“5+ years of experience designing, implementing, and managing large-scale distributed data processing systems, or working within large-scale distributed ML data frameworks, with recent experience using e.g. Ray, Apache Spark, workflow orchestrators, Apache Arrow, and/or Parquet.”

We are seeking a visionary and highly technical Senior Data Platform Engineer to architect, implement, scale, and maintain the data engine powering our next-generation frontier models.

In this high-impact role, you will bridge the gap between cutting-edge AI research and high-performance engineering, treating the data platform as an internal product with our researchers as your primary customers. You will be responsible for building automated, petabyte-scale data processing pipelines and for guaranteeing that everything they produce is efficient, reliable, and fully traceable. Our technical environment is not fixed and will evolve as our projects scale. We expect someone capable of evolving it, not only following industry trends, challenging it, and making sustainable decisions in close collaboration with our Research and Product teams.

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

Key Responsibilities

Scale and automate the data processing stack to handle petabytes of data and ensure its smooth operation.
Design the execution layer for pipeline stages of differing computational shape, matching each stage to an appropriate engine and keeping the pipeline saturated end to end.
Ensure efficient use of compute resources, including GPU access for compute-intensive data processing tasks.
Make pipelines reliable at scale, with graceful failure recovery, restartability, and observability that attributes bottlenecks and cost to the responsible stage.
Ensure all datasets, including the intermediate outputs of each transformation stage, are versioned, reproducible, and fully traceable to meet specific and dynamic experiment needs, and are accompanied by datasheets, in accordance with internal Data Governance policies.
Partner with the Research team to ensure the datasets you produce integrate seamlessly with the training pipelines.

Skills and Qualifications

A bachelor's degree in a relevant field (e.g., computer science, computer engineering, software engineering) is required.
5+ years of experience designing, implementing, and managing large-scale distributed data processing systems, or working within large-scale distributed ML data frameworks, with recent experience using e.g. Ray, Apache Spark, workflow orchestrators, Apache Arrow, and/or Parquet.
Demonstrated ownership of a data processing system under real throughput, reliability, and cost pressure. The specific frameworks matter less to us than evidence that you have had to reason about where a large pipeline breaks and why.
Experience profiling and optimizing throughput and cost across heterogeneous workloads, including GPU-accelerated stages.
Experience with dataset versioning, lineage, and reproducibility tooling.
Ability to collaborate effectively with cross-functional teams, document best practices, and stay updated with the latest advancements in large-scale data processing and software development.
Experience with workload managers (e.g., Ray, Kubernetes, Slurm).
Familiarity with containerization tools (e.g., Docker, Enroot).
Familiarity with data infrastructures and platforms (e.g., vector databases).

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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