Sr Scientist, Deep Learning

Company Description

Insmed is a global biopharmaceutical company on a mission to transform the lives of patients living with serious and rare diseases. Our most valuable resource is our employees, and everything we do is motivated by a patients-first mentality. We are dedicated to growing our team with talented individuals from around the world who are willing to challenge the status quo, solve problems, and work collaboratively with a sense of urgency and compassion.

Guided by our core values of collaboration, accountability, passion, respect, and integrity, we aim to foster an inclusive, diverse, and flexible work environment, where our employees are recognized for leaning in and rolling up their sleeves. If you share our vision and want to work with the most dedicated people in the biopharma industry, come to Insmed to accelerate your career.

Recognitions

 

Consistently Ranked Science’s Top Employer

Insmed is dedicated to creating a collaborative environment where our team can thrive. Every day, our employees turn their passion for science and research into innovative solutions for patients. That’s why we’ve been named the No. 1 company to work for in the biopharma industry in Science’s Top Employers survey for four years in a row.

 

  

 

 

A Certified Great Place to Work®

We believe our company is truly special, and our employees agree. In July 2024, we became Great Place to Work-certified in the U.S. for the fourth year in a row. We are also honored to have been listed on the Best Workplaces in Biopharma™, Best Workplaces in New York™, PEOPLE® Companies That Care, Best Workplaces for Women™, Best Workplaces for Millennials™, and Best Medium Workplaces™ lists.

  

 

Overview

We seek a new member to join our computational team in developing and applying cutting-edge AI-driven protein engineering methods to design next-generation biotherapeutics. While AI methods are already radically improving our ability to rapidly engineer new and modified proteins with desired properties, there remain gaps between prediction and reality, challenges in sampling from the massive, complex design space, and difficulties in leveraging sparse, noisy task-specific experimental data. This role will lead some of our efforts in developing and deploying deep learning models and generative methods to address these issues. The computational biology group works closely with highly collaborative and nimble biologics research teams who conduct associated protein engineering, in vitro and in vivo screening, and immunological analysis. Consequently, new methods and models can immediately be experimentally tested and employed in the pursuit of potent new biotherapeutic agents for clinical translation.

Responsibilities

Responsibilities

• Develop and deploy innovative deep learning models to represent and predict protein properties such as stability, activity, and immunogenicity, and associated methods to generate improved or novel biotherapeutic candidates accordingly.
• Develop and deploy models and methods to iteratively optimize and learn from portfolios of protein engineering experiments, exploring and exploiting the design space around a biotherapeutic target.
• Develop and deploy models and methods to integrate multi-modal, multi-scale protein data, including from large sequence, structure, and functional databases as well as relatively limited target-specific experimental data.

Qualifications

• PhD in Computer Science, Statistics, Mathematics, or a related discipline, along with relevant postdoctoral or industry experience.
• Thorough understanding of state-of-the-art deep learning models and methods; extensive experience in designing, building, training, and evaluating them; demonstrated facility deploying, streamlining, optimizing, and scaling them using PyTorch-based libraries and frameworks.
• Experience with protein models, e.g., structure and interaction prediction (AlphaFold, Boltz-1, etc.), foundational protein language models (ESM, SaProt, etc.), structure-based inverse folding models (IF1, Protein MPNN, etc.), and diffusion/flow methods spanning protein sequence and structure (Chroma, RFDiffusion, etc.).
• Experience in computational protein design, computational structural biology, or bioinformatics is beneficial.

This position can be remote or hybrid in our research labs in NJ or NH.

Salary Range

$115,000 - $161,867 a year

Compensation & Benefits

At Insmed, we’re committed to investing in every team member’s total well-being, now and in the future. Our benefit programs vary by country but we offer the following to all Insmed team members, regardless of geographic location:

• Flexible approach to how we work
• Health benefits and time-off plans
• Competitive compensation package, including bonus 
• Equity Awards (Long-Term Incentives)
• Employee Stock Purchase Plan (ESPP)

 

For more informaion on U.S. benefits click here.

 

 

Additional Information

Insmed Incorporated is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.

 

Unsolicited resumes from agencies should not be forwarded to Insmed. Insmed will not be responsible for any fees arising from the use of resumes through this source. Insmed will only pay a fee to agencies if a formal agreement between Insmed and the agency has been established. The Human Resources department is responsible for all recruitment activities; please contact us directly to be considered for a formal agreement.

 

Insmed is committed to providing access, equal opportunity, and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. To request reasonable accommodation to participate in the job application or interview process, please contact us by email at TotalRewards@insmed.com and let us know the nature of your request and your contact information. Requests for accommodation will be considered on a case-by-case basis. Please note that only inquiries concerning a request for reasonable accommodation will be responded to from this email address.

 

Applications are accepted for 5 calendar days from the date posted or until the position is filled.