over 1 year ago
Base Salary
$170k - $230k/yr
Responsibilities
- Architect and build workflows for ingesting, processing, and preparing multimodal training data for distributed ML training on GPU clusters.
- Develop compliant ingestion strategies for internal databases, third-party APIs, public biomedical datasets, and Tempus proprietary data.
- Use, optimize, and contribute to large-scale ML data loading and streaming frameworks.
- Collaborate with infrastructure and platform teams to optimize GCP cloud-native services for performance, cost, and security.
- Build connectors and data loaders for knowledge graphs, structured databases, biomedical literature repositories such as PubMed, and curated ontologies.
- Optimize storage for large-scale model training and knowledge access.
- Orchestrate, monitor, and troubleshoot data workflows with Airflow and Kubeflow Pipelines.
- Establish monitoring, logging, alerting, data drift detection, and data quality systems for pipeline health.
- Analyze and improve data I/O performance across storage, network, and compute resources.
- Manage and optimize cloud costs associated with storing and processing massive datasets.
Requirements
- Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
- 8+ years of industry experience designing, building, and operating large-scale production data pipelines and infrastructure.
- Strong experience with massive heterogeneous datasets of terabytes or more and distributed data processing tools such as Apache Spark, Ray, or Dask.
- Hands-on experience with large-scale ML data frameworks such as Hugging Face Datasets, MosaicML Streaming, WebDataset, or Petastorm.
- Experience with MLOps tools and platforms such as MLflow, Kubeflow, or SageMaker Pipelines.
- Understanding of data challenges involved in training foundation models, LLMs, and multimodal models.
- Proficiency in Python and modern distributed data processing frameworks.
- Ability to influence technical direction and data strategy, mentor junior engineers, and collaborate cross-functionally.
- Excellent communication skills and ability to explain complex technical concepts to diverse audiences.
- Preferred: PhD or other advanced degree in Computer Science, Engineering, Bioinformatics, or a related field.
- Preferred: Contributions to relevant open-source projects.
- Preferred: Direct experience with clinical or biological data, including EHR, genomics, or medical imaging.
Benefits
- Tempus offers a full range of benefits, which may include medical and other benefits depending on the position.
- Benefits may include restricted stock units and incentive compensation.
- The role may be performed remotely in the USA or from New York, California, or Illinois, with location-specific salary ranges.
Tech Stack
Categories
Data EngineeringML Engineering
About Tempus
Tempus is a technology company advancing precision medicine through the practical application of artificial intelligence in healthcare. With one of the world’s largest libraries of multimodal data, and an operating system to make that data accessible and useful, Tempus provides AI-enabled precision medicine solutions to physicians to deliver personalized patient care and in parallel facilitates discovery, development and delivery of optimal therapeutics. The goal is for each patient to benefit from the treatment of others who came before by providing physicians with tools that learn as the company gathers more data. For more information, visit tempus.com.