Member of Technical Staff (Data Research Engineer)

Member of Technical Staff (Data Research Engineer) What you’ll do - Work with researchers to define what “good data” means for our models, including quality metrics, validation checks, and acceptance thresholds - Design, build, and maintain scalable data pipelines for ingestion, filtering, transformation, and export (Spark, Ray, Airflow, or similar) - Build tools and workflows to explore large datasets and connect findings back to model results - Set up automated data health reporting: coverage, duplicates, corruption, distribution drift, labeling consistency, and other dataset-level signals - Track datasets, metadata, provenance, and versions so experiments are reproducible and it’s clear what data went into what run - Build, manage, and scale the compute used for data processing (GPU/CPU clusters), using Kubernetes and infrastructure-as-code tools like Terraform or Pulumi - Operate and improve our monitoring and telemetry stack (Datadog, Grafana, Weights & Biases) for both pipeline reliability and data quality signals - Own CI/CD and development tooling for the data stack (GitHub, Python, PyTorch), and automate repetitive workflows to reduce friction - Track and optimize throughput, storage, and compute utilization across pipelines and related assets - Participate in an on-call rotation to resolve issues and keep critical systems running What we’re looking for - Strong engineering and ML fundamentals with experience building and operating large-scale data and/or compute systems - Comfortable moving between “research questions” and production engineering: you can dig into data, run analyses, and also ship reliable systems - Practical experience with distributed processing and orchestration (Spark, Ray, Airflow, or equivalents) - Solid Python skills, and familiarity with the tooling around modern model training workflows (datasets, checkpoints, experiment tracking) - Experience running scalable infrastructure (Kubernetes, Terraform/Pulumi) and debugging performance/reliability issues in distributed systems - Strong instincts around data quality: how to measure it, how to monitor it, and how to prevent regressions as things scale - Able to work in a fast-moving environment, prioritize what matters, and communicate clearly with both researchers and engineers - Bonus: experience with large video datasets, dataset curation for training, or building internal tooling for evaluation/analysis in ML environments What we offer (compensation & benefits) - Competitive salary and equity - Private health coverage - Pension contribution (UK, Canada, US) - Unlimited paid vacation - Fully-distributed, async-first culture - Hardware setup of your choice - Stipends for phone, internet, and meals In our team, we approach our work with the dedication similar to Olympic athletes. Anticipate occasional late nights and weekends dedicated to our mission. We understand this level of commitment may not suit everyone, and we openly communicate this expectation. If you're motivated by deeply technical problems, a seemingly never-ending uphill battle and the opportunity to build (and own) a generational technology company, we can give you what you're looking for. All business roles at Moonvalley are hybrid positions by default, with some fully remote depending on the job scope. We meet a few times every year, usually in London, UK or North America (LA, Toronto) as a company. If you're excited about the opportunity to work on cutting-edge AI technology and help shape the future of media and entertainment, we encourage you to apply. We look forward to hearing from you! The statements contained in this job description reflect general details as necessary to describe the principal functions of this job, the level of knowledge and skill typically required and the scope of responsibility. It should not be considered an all-inclusive listing of work requirements. Individuals may perform other duties as assigned, including work in ... (truncated, view full listing at source)