Senior Machine Learning Research Engineer, Healthcare Data (Remote)

This position is open to remote within the US or onsite at our headquarters in South San Francisco, CA.

Why join Freenome?

Freenome is a high-growth biotech company on a mission since 2014 to create tools that empower everyone to prevent, detect, and treat their disease. 

To achieve this mission, Freenome is developing next-generation blood tests to detect cancer in its earliest, most treatable stages using our multiomics platform and machine learning techniques. Our first blood test will detect early-stage colorectal cancer and advanced adenomas.

To fight the war on cancer, Freenome has raised more than $1.1B from leading investors including a16z, GV (formerly Google Ventures), T. Rowe Price, BainCapital, Perceptive Advisors, RA Capital Management, Roche, Kaiser Permanente Ventures, and the American Cancer Society’s BrightEdge Ventures. 

Are you ready for the fight? A ‘Freenomer’ is a mission-driven employee who is fueled by the opportunity to make a positive impact on patients' lives, who thrive in a culture of respect and cross collaboration, and whose work makes a significant impact on the company and their career. Freenomers are determined, patient-centric, and outcomes-driven. We build teams around divergent expertise, allowing us to solve problems and ascertain opportunities in unique ways. We are dedicated to advancing healthcare, one breakthrough at a time.

About this opportunity:

At Freenome, we are seeking a Senior Machine Learning Research Engineer to help grow the Freenome Computational Science team. The ideal candidate has strong knowledge of machine learning (ML) fundamentals and the ability to thrive in a highly cross-functional environment. This person is responsible for leading the model engineering efforts for predictive and prescriptive modeling of disease risks, interventions, and other outcomes from large sets of healthcare data. This role will partner closely with our clinical and product organizations. 

You are passionate about building ML pipelines and training ML models with scalability in mind, and you will have a significant impact on the continued growth of a high profile technology organization that is changing the landscape on early cancer detection.

The role reports to our Senior Manager of Machine Learning Research Engineering.

What you’ll do:

• Lead the engineering direction and development of machine learning modeling pipelines for electronic healthcare records
• Optimize existing pipelines to ensure high-performance data transformation, preprocessing, and ML model training, while addressing challenges related to scalability and memory management
• Partner closely with the risk modeling scientists, biostatisticians, clinical and business specialists to translate algorithms into production-grade machine learning pipelines
• Collaborate with platform engineering teams to interface with the infrastructure to build and train machine learning models
• Take a mindful, transparent, and humane approach to your work

Must haves:

• MS or equivalent research experience in a relevant, quantitative field such as Computer Science (AI or ML emphasis), Statistics, Mathematics, Engineering, or a related field (PhD preferred)
• 5+ years of post-MS industry experience working on ML and software engineering
• Strong knowledge of machine learning fundamentals
• Expertise in building ML pipelines for preprocessing input data and training ML models in production environments
• Practical and theoretical understanding of models and algorithms: Decision trees, neural networks; boosting and model aggregation; clustering and mixture modeling, etc.
• Proficiency in one or more ML frameworks: Pytorch, XGBoost, Jax, etc.
• Excellent ability to clearly communicate across disciplines and work collaboratively towards next steps in experimental iterations
• A high degree of self-awareness

Nice to haves

• Deep domain-specific experience in electronic healthcare data
• Experience in taking proof of concept implementation and scaling them to robust ML engineering pipelines
• Experience in distributed model training using Pytorch and/or Ray
• Experience in a production software engineering environment, including the use of automated regression testing, version control, and deployment systems
• Experience with containerized cloud computing environments, such as Docker in GCP or AWS
• Experience with data pipelines, such as Ray, Flyte, Kafka, Spark, Airflow, Argo, Hadoop, or Flink

Benefits and additional information:

The US target range of our base salary for new hires is $157,250 - $240,000. You will also be eligible to receive