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Staff Machine Learning Engineer, Oncology foundation Model

Staff Machine Learning Engineer, Oncology foundation Model

Tempus, Inc.Chicago, IL, US
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Staff Machine Learning Engineer page is loaded

Staff Machine Learning Engineer

Apply locations Chicago, New York City, Remote - Illinois, Redwood City

Time type : Full time | Posted on : Posted 30+ Days Ago | Job requisition id : JR202500372

Passionate about precision medicine and advancing the healthcare industry?

Recent advancements in underlying technology have made it possible for AI to impact clinical care significantly. Tempus' proprietary platform connects real-world evidence to deliver real-time, actionable insights to physicians, providing critical information about treatments for patients at the right time.

What You’ll Do :

We seek an experienced Staff Machine Learning Engineer with expertise in large-scale multimodal model systems engineering to join our AI team. You will design, build, and optimize data infrastructure powering Tempus's generative AI models, enabling analysis of complex data types like genomics, pathology images, radiology scans, and clinical notes . This role is crucial for applying AI to improve healthcare outcomes.

Focus :

Your main focus will be to architect, build, and maintain the data infrastructure supporting large multimodal generative models, managing datasets from ingestion to knowledge integration, supporting AI learning from real-world evidence.

Key Responsibilities :

  • Design data processing workflows for multimodal data integration with ML training frameworks (GPU clusters).
  • Develop strategies for data ingestion from various sources, ensuring compliance and efficiency.
  • Utilize frameworks like MosaicML Streaming, Ray Data, HF Datasets for data handling.
  • Collaborate with infrastructure teams to leverage cloud services (primarily GCP).
  • Build connectors for knowledge sources such as knowledge graphs, biomedical literature, and ontologies.
  • Optimize data storage and access for large-scale training and knowledge retrieval.
  • Manage data workflows using tools like Airflow, Kubeflow Pipelines.
  • Implement monitoring and alerting for data pipelines, ensuring quality and performance.
  • Analyze and address data I / O bottlenecks, manage cloud storage costs.

Required Skills and Experience :

  • Master's degree in Computer Science, AI, or related field.
  • 8+ years in building and operating large-scale data pipelines in production.
  • Experience with heterogeneous datasets and frameworks like Apache Spark, Ray, Dask.
  • Hands-on with large-scale ML data tools like Hugging Face Datasets, MosaicML Streaming, WebDataset, Petastorm.
  • Understanding of training large models (Foundation Models, LLMs, Multimodal Models).
  • Proficiency in Python and distributed data processing tools.
  • Leadership skills : influence on technical and data strategy, mentoring, cross-team collaboration, excellent communication, ability to work in fast-paced environments.
  • Preferred Qualifications :

  • PhD in Computer Science, Engineering, Bioinformatics, or related field.
  • Open-source contributions.
  • Experience with clinical or biological data (EHR, genomics, imaging).
  • Pay Range :

    NY : $190,000 - $230,000 USD

    California : $190,000 - $230,000 USD

    Illinois : $170,000 - $210,000 USD

    Remote (USA) : $170,000 - $210,000 USD

    Salary varies by location and experience. Tempus offers comprehensive benefits including incentive compensation, stock units, medical, and other benefits.

    For remote roles in Los Angeles, criminal history may be considered due to job responsibilities involving sensitive information. Qualified applicants will be considered per applicable laws.

    We are an equal opportunity employer, committed to diversity and inclusion.

    About UsWhy Work Here?

    We’re looking for people who can change the world, question the status quo, and tackle tough problems. We value curiosity, passion, and a drive to build and learn continuously. Join us to address one of humanity’s most significant challenges.

    #J-18808-Ljbffr

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