Member of Engineering (Pre-training / Data Engineering)

Poolside
United States, United Kingdom, EMEA, London
Workplace: RemoteFull timeFunction: Education & TrainingSkills: ["Problem-solving","Collaboration","Communication","Attention to detail"]

Core member of the Pretraining Data team building and scaling the Model Factory to train, scale, and experiment with foundation models. You’ll architect and maintain high-performance pipelines transforming trillions of tokens, bridge raw data to GPU clusters, and collaborate with Pretraining, Posttraining, Evals, and Product to deliver diverse, high-quality datasets for ML models.

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FursaFursa
Poolside
Poolside
7 months ago

Member of Engineering (Pre-training / Data Engineering)

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 12 hours agoStatus: Live

Job Summary

Core member of the Pretraining Data team building and scaling the Model Factory to train, scale, and experiment with foundation models. You’ll architect and maintain high-performance pipelines transforming trillions of tokens, bridge raw data to GPU clusters, and collaborate with Pretraining, Posttraining, Evals, and Product to deliver diverse, high-quality datasets for ML models.
Location: United States, United Kingdom, EMEA, London
Workplace: Remote
Employment Type: Full time
Job Function: Education & Training

Key Responsibilities

  • •Build and maintain high-performance pipelines for trillions of tokens.
  • •Deliver diverse and high-quality datasets for pre-training foundation models.
  • •Collaborate with Pretraining, Posttraining, Evals and Product to ensure alignment on model quality.
  • •Bridge raw web crawl data and GPU clusters through data modeling, sorting, and distributed pipeline optimization.
  • •Contribute to ingestion, deduplication, and streaming systems for petabyte-scale data.

Pay and Benefits

Perks:Remote WorkHealth InsuranceHome OfficeLearning Budget

Key Requirements

  • •Expert-level Python knowledge with the ability to write clean, maintainable code.
  • •Strong algorithmic foundations and hands-on experience building production-grade, distributed data systems for machine learning.
  • •Proficiency with Polars, Dask, or PySpark and libraries like Polars, Dask, or PySpark.
  • •Experience with orchestration tools (Slurm, Airflow, or Dagster) and observability/reliability tooling (CI/CD, Grafana, Prometheus).
  • •Infra familiarity: Git, Docker, Kubernetes, cloud managed services; knowledge of batched inference (e.g., vLLM).
Experience:Machine learningAIDistributed systems
Skills:Problem-solvingCollaborationCommunicationAttention to detail
Tech Stack:PythonSlurmAirflowDagsterGrafanaPrometheusGitDockerKubernetesVLLMPolarsDaskPySpark

Company Brief

Poolside
Builds foundation models, coding agents, and enterprise systems to automate and accelerate software development; offers on-prem/VPC deployments, developer tooling, and forward-deployed engineering for high‑security environments.
Industry: AI & Machine Learning
Company Size: Medium (51 to 250 employees)
Growth: Scaleup
Valuation: Unicorn (USD 1B+)
Funding: Series B
Headquarters: San Francisco, United States
Founded: 2023
WebsiteLinkedIn