(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 developing tests to detect cancer using a standard blood draw. To do this, Freenome uses a multiomics platform that combines tumor and non-tumor signals with machine learning to find cancer in its earliest, most-treatable stages.
Cancer is relentless. This is why Freenome is building the clinical, economic, and operational evidence to drive cancer screening and save lives. Our first screening test is for colorectal cancer (CRC) and advanced adenomas, and it’s just the beginning.
Founded in 2014, Freenome has ~500 employees and more than $1.1B in funding from key investors, such as the American Cancer Society, Andreessen Horowitz, Anthem Blue Cross, Bain Capital, Colorectal Cancer Alliance, DCVC, Fidelity, Google Ventures, Kaiser Permanente, Novartis, Perceptive Advisors, RA Capital, Roche, Sands Capital, T. Rowe Price, and Verily.
At Freenome, we aim to impact patients by empowering everyone to prevent, detect, and treat their disease. This, together with our high-performing culture of respect and cross-collaboration, is what motivates us to make every day count.
Become a Freenomer
Do you have what it takes to be a Freenomer? A “Freenomer” is a determined, mission-driven, results-oriented employee fueled by the opportunity to change the landscape of cancer and make a positive impact on patients’ lives. Freenomers bring their diverse experience, expertise, and personal perspective to solve problems and push to achieve what’s possible, 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
- 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 pre-IPO equity, cash bonuses, and a full range of medical, financial, and other benefits dependent on the position offered. Please note that individual total compensation for this position will be determined at the Company’s sole discretion and may vary based on several factors, including but not limited to, location, skill level, years and depth of relevant experience, and education. We invite you to check out our career page @ https://careers.freenome.com/ for additional company information.
Freenome is proud to be an equal opportunity employer and we value diversity. Freenome does not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
Applicants have rights under Federal Employment Laws.
- Family & Medical Leave Act (FMLA)
- Equal Employment Opportunity (EEO)
- Employee Polygraph Protection Act (EPPA)
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