Staff Machine Learning Engineer

Tempus • Boca Raton, FL

238d • $190,000 - $230,000

About The Position

Tempus is seeking 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. You will play a pivotal role in designing, building, and optimizing the foundational data infrastructure that powers Tempus's most advanced generative AI models. 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 improve patient care, optimize clinical workflows, and accelerate life-saving medical research. This is a critical, high-impact position for driving the practical application of cutting-edge AI to revolutionize healthcare.

Requirements

• Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
• Proven track record (8+ years of industry experience) in designing, building, and operating large-scale data pipelines and infrastructure in a production environment.
• Strong experience working with massive, heterogeneous datasets (TBs+) and modern distributed data processing tools and frameworks such as Apache Spark, Ray, or Dask.
• Strong, hands-on experience with tools and libraries specifically designed for large-scale ML data handling, such as Hugging Face Datasets, MosaicML Streaming, or similar frameworks.
• Understanding of the data challenges specific to training large models (Foundation Models, LLMs, Multimodal Models).
• Proficiency in programming languages like Python and experience with modern distributed data processing tools and frameworks.
• Proven ability to bring thought leadership to the product and engineering teams, influencing technical direction and data strategy.
• Experience mentoring junior engineers and collaborating effectively with cross-functional teams (Research Scientists, ML Engineers, Platform Engineers, Product Managers, Clinicians).
• Excellent communication skills, capable of explaining complex technical concepts to diverse audiences.
• Strong bias-to-action and ability to thrive in a fast-paced, dynamic research and development environment.
• A pragmatic approach focused on delivering rapid, iterative, and measurable progress towards impactful goals.

Nice To Haves

• Advanced degree (PhD) in Computer Science, Engineering, Bioinformatics, or a related field.
• Contributions to relevant open-source projects.
• Direct experience working with clinical or biological data (EHR, genomics, medical imaging).

Responsibilities

• Architect and build sophisticated data processing workflows responsible for ingesting, processing, and preparing multimodal training data that seamlessly integrate with large-scale distributed ML training frameworks and infrastructure (GPU clusters).
• Develop strategies for efficient, compliant data ingestion from diverse sources, including internal databases, third-party APIs, public biomedical datasets, and Tempus's proprietary data ecosystem.
• Utilize, optimize, and contribute to frameworks specialized for large-scale ML data loading and streaming (e.g., MosaicML Streaming, Ray Data, HF Datasets).
• Collaborate closely with infrastructure and platform teams to leverage and optimize cloud-native services (primarily GCP) for performance, cost-efficiency, and security.
• 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.
• Optimize data storage for efficient large scale training and knowledge access.
• Orchestrate, monitor, and troubleshoot complex data workflows using tools like Airflow, Kubeflow Pipelines.
• Establish robust monitoring, logging, and alerting systems for data pipeline health, data drift detection, and data quality assurance, providing feedback loops for continuous improvement.
• Analyze and optimize data I/O performance bottlenecks considering storage systems, network bandwidth and compute resources.
• Actively manage and seek optimizations for the costs associated with storing and processing massive datasets in the cloud.

Benefits

• Incentive compensation
• Restricted stock units
• Medical and other benefits depending on the position

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Industry: Professional, Scientific, and Technical Services
• Education Level: Master's degree
• Number of Employees: 1,001-5,000 employees