Job Description

We seek an experienced and highly skilled Staff Machine Learning Engineer with deep expertise in large-scale multimodal model systems engineering to join our dynamic AI team. Your unique skills and knowledge will be pivotal in designing, building, and optimizing the foundational data infrastructure that powers our most advanced generative AI models. We value your expertise and believe you will be an integral part of our team.

Your work will directly enable the training and deployment of robust, production-ready multimodal systems that analyze complex data types (like genomics, pathology images, radiology scans, and clinical notes) to significantly improve patient care, optimize clinical workflows, and accelerate life-saving medical research. This critical, high-impact position plays a key role in driving the practical application of cutting-edge AI to revolutionize healthcare, a mission we are deeply committed to and that should inspire your work every day.

Your primary focus will be architecting, building, and maintaining the critical data infrastructure supporting our large multimodal generative models. This includes managing the entire lifecycle of vast datasetsfrom ingesting and processing diverse training data to integrating and retrieving extensive knowledge sources used to augment model capabilities.

Key Responsibilities:

As a technical leader in this space, you will be:

1. Architect and build sophisticated data processing workflows for ingesting, processing, and preparing multimodal training data that seamlessly integrate with large-scale distributed ML training frameworks and infrastructure (GPU clusters).

2. Develop efficient, compliant data ingestion strategies from diverse sources, including internal databases, third-party APIs, public biomedical datasets, and Tempus's proprietary data ecosystem.

3. Utilize, optimize, and contribute to frameworks specialized for large-scale ML data loading and streaming (e.g., MosaicML Streaming, Ray Data, HF Datasets).

4. Collaborate closely with infrastructure and platform teams to leverage and optimize cloud-native services (primarily GCP) for performance, cost-efficiency, and security.

5. Engineer efficient connectors and data loaders for accessing and processing information from diverse knowledge sources, such as knowledge graphs, internal structured databases, biomedical literature repositories (e.g., PubMed), and curated ontologies.

6. Optimize data storage for efficient large-scale training training and knowledge access.

7. Orchestrate, monitor, and troubleshoot complex data workflows using tools like Airflow and Kubeflow Pipelines.

8. Establish robust monitoring, logging, and alerting systems for data pipeline health, data drift detection, and data quality assurance, providing feedback loops for continuous improvement.

9. Analyze and optimize data I/O performance bottlenecks considering storage systems, network bandwidth, and compute resources.

10. Actively manage and seek optimizations for the costs associated with storing and processing massive datasets in the cloud.

Required Skills and Experience:

1. BSc in Computer Science, Artificial Intelligence, Software Engineering, or a related field. A strong academic background with a focus on AI data engineering.

2. Proven track record in designing, building, and operating large-scale data pipelines and infrastructure in a production environment.

3. Strong experience working with massive, heterogeneous datasets (TBs+) and modern distributed data processing tools and frameworks like Apache Spark, Ray, or Dask.

4. Strong, hands-on experience with tools and libraries designed explicitly for large-scale ML data handling, such as Hugging Face Datasets, MosaicML Streaming, or similar frameworks (e.g., WebDataset, Petastorm). Experience with MLOps tools and platforms (e.g., MLflow, Kubeflow, SageMaker Pipelines).

5. Understanding the data challenges specific to training large models (Foundation Models, LLMs, Multimodal Models).

6. Proficiency in programming languages like Python and experience with modern distributed data processing tools and frameworks.

Leadership and collaboration:

1. Proven ability to bring thought leadership to the product and engineering teams, influencing technical direction and data strategy.

2. Experience mentoring junior engineers and collaborating effectively with cross-functional teams (Research Scientists, ML Engineers, Platform Engineers, Product Managers, and Clinicians).

3. Excellent communication skills, capable of explaining complex technical concepts to diverse audiences.

4. Strong bias-to-action and ability to thrive in a fast-paced, dynamic research and development environment.

5. A pragmatic approach focused on delivering rapid, iterative, measurable progress towards impactful goals.

Preferred Qualifications:

1. Advanced degree in Computer Science, Engineering, Bioinformatics, or a related field.

2. Contributions to relevant open-source projects.

3. Direct experience working with clinical or biological data (EHR, genomics, medical imaging).

PLEASE NOTE THAT THIS ROLE IS ONLY OPEN TO CANDIDATES WHO ARE RESIDENTS AND AUTHORIZED TO WORK IN THE USA

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