AWS, Cloud computing, Google Cloud Platform (GCP), JAX framework, Python Programming, PyTorch, SQL
Key to insitro’s efforts to rethink drug discovery is the ability to extract biologically and clinically meaningful signals from high content clinical data and to align them with human genetic signals, thereby bridging to experimental data produced in our in vitro cellular systems. As the Head of Clinical Machine Learning, you will continue to build and lead a team comprising machine learning scientists with an expertise in clinical data, and will collaborate with cross-functional colleagues to envision and oversee our scientific work in leveraging insights from clinical data sets.
You and your team will work with clinical data from large human cohorts such as randomized clinical trials, electronic health records, national biobanks, and other sources. You will lead the development of cutting edge ML approaches to analyze and integrate large-scale multi-modal phenotypic datasets, including physiological monitoring, longitudinal clinical data, electronic health records, diverse biomarker data, and medical imaging modalities. You will develop machine learning models that use high-content data alongside clinical endpoints to disentangle the heterogeneity of patient disease state and disease progression, and to identify relevant associations with clinical outcomes and with disease drivers.
Your work will involve the development and deployment of cutting edge methods in machine learning, addressing challenges such as distribution shift, site and population variability, data missingness, longitudinal progression, class imbalance, and small sample sizes, among other unique challenges. You will need to develop fit-for-purpose approaches that utilize methods such as self-supervised learning, multi-task learning, few-shot learning, and more. You will work in collaboration with the software engineering team to develop these methods as robust, reusable platform components that can be deployed on large-scale datasets in a portable way.
You will also work closely with life scientists, statistical geneticists, bioengineers, medical scientists, machine learning scientists, and software engineers to integrate human-level data with our high-throughput in-house in vitro genomic and phenotypic data to identify therapeutic targets and develop drugs that have high efficacy and low toxicity.
In this role, you will:
You will be joining a vibrant biotech startup that has long-term stability due to significant funding, yet is in a high growth phase. A lot can change in this early and exciting phase, providing many opportunities for significant impact. You will work closely with a very talented team, learn a broad range of skills, and help shape insitro’s culture, strategic direction, and outcomes. Join us, and help make a difference to patients! This role is preferably based in the San Francisco Bay Area or Boston, but we are open to discussing other locations in the United States and the UK.
About You
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Compensation & Benefits at insitro
Our target starting salary for successful US-based applicants for this role is $235,000 - $300,000. To determine starting pay, we consider multiple job-related factors including a candidate’s skills, education and experience, the level at which they are actually hired, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.
This role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan, subject to the terms of those plans and associated policies.
In addition, insitro also provides our employees:
insitro is a data-driven drug discovery and development company using machine learning and data at scale to transform the way that drugs are discovered and developed for patients. insitro is developing predictive machine learning models to discover underlying biologic state based on human cohort data and in-house generated cellular data at scale. These predictive models can be brought to bear on key bottlenecks in pharmaceutical R&D.
South San Francisco, CA, USA
2-4 year
South San Francisco, CA, USA
4-6 year
South San Francisco, CA, USA
2-4 year