Staff Machine Learning Engineer at Tempus

Chicago, Illinois, United States

Apply Now

Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

HealthcareIndustries

Requirements

Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field with a strong academic background in AI data engineering
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

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

Skills

Key technologies and capabilities for this role

Machine LearningGenerative AIMultimodal ModelsData InfrastructureData ProcessingGenomicsPathology ImagesRadiology ScansClinical NotesModel TrainingProduction Deployment

Questions & Answers

Common questions about this position

What is the salary for this Staff Machine Learning Engineer position?

This information is not specified in the job description.

Is this a remote position or what is the location requirement?

This information is not specified in the job description.

What skills are required for this role?

The role requires deep expertise in large-scale multimodal model systems engineering, architecting data processing workflows for multimodal training data, and experience with distributed ML frameworks like GPU clusters, MosaicML Streaming, Ray Data, HF Datasets, and GCP.

What is the company culture like at Tempus?

Tempus has a dynamic AI team focused on advancing precision medicine and healthcare through AI, with a collaborative environment involving close work with infrastructure and platform teams.

What makes a strong candidate for this position?

A strong candidate is an experienced Staff Machine Learning Engineer with deep expertise in large-scale multimodal model systems, proven ability to architect data infrastructure for generative AI, and skills in handling diverse data sources like genomics and clinical notes.

Tempus

AI-driven healthcare data analysis platform

About Tempus

Tempus focuses on enhancing patient outcomes through the use of data and artificial intelligence in healthcare. The company offers a platform that analyzes medical data to provide insights for physicians, helping them make better treatment decisions. This platform also aids pharmaceutical and biotech companies in drug development by identifying new targets and assessing treatment effectiveness. For patients, Tempus identifies personalized therapy options, particularly in cancer care, where their research has shown increased opportunities for tailored treatments. They have developed a pan-cancer organoid platform and a liquid biopsy assay for profiling circulating tumor DNA. Tempus generates revenue by charging healthcare providers and companies for access to their platform and insights, setting them apart from competitors by their focus on personalized medicine and extensive cancer research.

Chicago, IllinoisHeadquarters

2015Year Founded

$894.9MTotal Funding

IPOCompany Stage

AI & Machine Learning, Biotechnology, HealthcareIndustries

1,001-5,000Employees

Benefits

Relocation Assistance

Company Equity

Performance Bonus

Risks

Competition from partners like Genialis may lead to conflicts of interest.

Ambry Genetics acquisition could pose financial risks if synergies aren't realized.

Technical challenges may arise in integration with Flatiron's OncoEMR platform.

Differentiation

Tempus uses AI to analyze clinical and molecular data for precision medicine.

Their platform aids in personalized treatment decisions for cancer patients.

Tempus collaborates with biotech firms to enhance drug development using real-world data.

Upsides

Increased AI adoption in healthcare boosts Tempus' partnerships and collaborations.

Acquisition of Ambry Genetics expands Tempus' genetic testing capabilities.

Integration with Flatiron's OncoEMR enhances precision in cancer treatment plans.

Land your dream remote job 3x faster with AI

Try Jobo Free