Job Description

AI Research Scientist, Foundation Models

Our Artificial Intelligence Machine Learning (AI/ML) capabilities are critical accelerators to our mission of inventing new medicines that save and improve lives. Core to the Data, AI, and Genome Sciences (DAGS) function is an AI/ML-first approach to improving target and biomarker discovery, validation and selection, and elucidating complex disease mechanisms. As a senior AI scientist, you will be responsible for pre-training and fine-tuning biological foundation models, analyzing pre-trained models posthoc, building rigorous benchmarks for evaluating foundation models, and serving in-house trained foundation models. Your work will advance our understanding of complex diseases and support the development of innovative therapeutic strategies. You will be part of a cross-functional team of computational biologists, bioinformaticians, data scientists, software engineers, and machine learning engineers who strive to identify therapeutic targets. 

Primary Responsibilities:

• Collaborate with cross-functional teams to identify research questions and data requirements and develop appropriate solutions.

• Develop and train transformer-based (and related state-space models) foundation models for -omics data.

• Interpret and post-hoc analyze pre-trained models.

• Rigorously benchmark and evaluate the performance of both in-house and publicly available models.

• Host and serve in-house models and make them accessible to scientists across our Company.

• Stay up to date with the latest advancements in machine learning and statistics and apply relevant advancements to improve existing methodologies and models.

• Publish research findings in relevant conferences and journals and actively contribute to the scientific community through knowledge sharing and collaborations.

Required Education, Experience and Skills:

• PhD, MS, or BS in Computer Science, Statistics, Physics, or a related field and 0-3+ years of full-time experience (with PhD), 4+ years of experience (with MS), or 7+ years of experience (with BS).

• Expertise in machine learning and in training, evaluating, and debugging models and data at scale.

• Excellent software design and development skills and strong proficiency in Python.

• Experience with standard deep learning frameworks like PyTorch and the Huggingface ecosystem for working with transformer-based foundation models.

• Excellent communication skills and ability to work collaboratively in a multi-disciplinary team.

• Interest in life sciences problems and disease biology, and willing to learn from and teach others.

Preferred Skills and Experience:

• Demonstrated experience working with models that require multiple GPUs for training and inference. 

• Relevant publications in scientific journals and experience contributing to research communities, including NeurIPS, ICML, ICLR, etc

• Experience with pre-training and multi-modal training of (biological or otherwise) foundation models is a strong plus.

• Familiarity with biological data and previous experience with protein language models and foundation models for omics is a strong plus.

NOTICE FOR INTERNAL APPLICANTS

In accordance with Managers' Policy - Job Posting and Employee Placement, all employees subject to this policy are required to have a minimum of twelve (12) months of service in current position prior to applying for open positions.

If you have been offered a separation benefits package, but have not yet reached your separation date and are offered a position within the salary and geographical parameters as set forth in the Summary Plan Description (SPD) of your separation package, then you are no longer eligible for your separation benefits package. To discuss in more detail, please contact your HRBP or Talent Acquisition Advisor.

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Effective September 5, 2023, employees in office-based positions in the U.S. will be working a Hybrid work consisting of three total days on-site per week, Monday - Thursday, although the specific days may vary by site or organization, with Friday designated as a remote-working day, unless business critical tasks require an on-site presence.This Hybrid work model does not apply to, and daily in-person attendance is required for, field-based positions; facility-based, manufacturing-based, or research-based positions where the work to be performed is located at a Company site; positions covered by a collective-bargaining agreement (unless the agreement provides for hybrid work); or any other position for which the Company has determined the job requirements cannot be reasonably met working remotely. Please note, this Hybrid work model guidance also does not apply to roles that have been designated as “remote”.

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