Associate Scientist, Postdoctoral Fellow - Computational Protein Design

Merck & Co.

Cambridge (MA)

On-site

USD 82,000 - 92,000

Full time

8 days ago

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Benefits offered by this job

Annual bonus

Long-term incentive

Medical, dental, vision insurance

Retirement benefits (401(k))

Paid holidays and vacation

Job summary

Merck & Co. in Cambridge, MA seeks a postdoctoral scientist to push AI/ML and physics-based design in computational protein engineering. You will explore ensemble-aware strategies and deliver tools for next-generation biotherapeutics, with publication opportunities and rapid data-driven validation.

Embedded in Discovery Biologics, you will benchmark methods, generate data, and share models with internal and external audiences to accelerate therapeutic pipelines.

Qualifications

PhD in a quantitative field related to computational protein design.
Experience with benchmarks and data curation for ML design tools.
Strong Python and ML framework skills (PyTorch/JAX).

Responsibilities

Implement benchmarking strategies for computational protein design approaches.
Develop ensemble-aware generative design and scoring methodologies.
Disseminate findings via manuscripts and high-quality models/tools.

Skills

Python

PyTorch

JAX

Collaboration

Scientific communication

Education

PhD in Computational Biology/Bioinformatics/CS/Biophysics/Computational Chemistry

PhD expected by Spring 2027

Related quantitative discipline

Tools

RFdiffusion

BoltzGen

ProteinMPNN

AlphaFold

ESMFold

Job description

Job Description

Our company's Discovery Biologics is seeking a talented postdoctoral scientist motivated to push the boundaries of computational protein engineering and next-generation biotherapeutics design. In this role, you will develop and apply state-of-the-art artificial intelligence (AI) and machine learning (ML) tools in conjunction with physics-based modeling to address fundamental challenges facing computational protein design and structure prediction. The successful candidate will explore how ensemble-aware computational protein design (e.g. target conformational flexibility, state selectivity, multi-state binder design) can directly impact therapeutic pipelines and develop the methodology and tools to realize these unique advantages. Embedded in the Disruptive Technology group in our Cambridge, MA facility, the candidate will leverage our company's state-of-the-art capabilities and expertise in computational design, high-throughput production, and characterization for rapid data generation and model validation.

As a postdoctoral fellow, your focus will be to deliver broadly relevant, high-impact science and computational models and tools. Publication of this work in peer-reviewed journals and scientific conferences is expected and the scope of the research project is designed specifically for external publication opportunity.

Key Responsibilities:

Implement rigorous benchmarking strategies for evaluating state-of-the-art computational protein design approaches.
Develop and validate ensemble-aware generative design and scoring methodologies for the advancement of next-generation biotherapeutics.
Communicate findings within our company and the broader scientific community by authoring high-impact manuscripts and distributing high-quality models and tools.

Education Requirements:

Must currently hold a PhD OR
Receive a Ph.D. no later than spring 2027
Ph.D. in Computational Biology, Bioinformatics, Computer Science, Biophysics, Computational Chemistry, Machine Learning, or a related quantitative discipline.

Required Experience and Skills:

Hands-on experience developing and/or applying computational protein design tools (e.g., RFdiffusion, BoltzGen, ProteinMPNN) and structure prediction algorithms (e.g., AlphaFold, Boltz, ESMFold) for binder design. Understanding of relevant experimental techniques for evaluation and characterization.
Quantitative evaluation mindset with a demonstrated ability to construct rigorous benchmarks, curate high-quality datasets, and apply critical-thinking and analytical skills for evaluation.
Strong proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX), alongside experience with high-performance computing and reproducible software development.
Demonstrated commitment to collaboration in highly-interdisciplinary environments.
Strong track record of clear and effective scientific communication in addition to first-author peer-reviewed publications and presentations at major conferences.

Preferred Experience and Skills:

Experience training or fine-tuning generative models (diffusion, flow-matching, etc.) or foundation models is highly desireable.
Experience with conformational ensemble generation and analysis - ensemble prediction methods (e.g., AF-cluster, AlphaFlow, BioEmu) and/or molecular dynamics tools (e.g., AMBER, Desmond, GROMACS, OpenMM).
Familiarity with energetic and free-energy methods for protein-protein interfaces (e.g., Rosetta/PyRosetta, Flex ddG, FEP).
Experimental experience is a plus but not required. Familiarity with protein production, display technologies, and NGS-based selections with an understanding of how experimental data informs model development is advantageous.

The salary range for this role is: $82,000-$92,000

This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee's position within the salary range will be based on several factors including, but not limited to relevant education, qualifications, certifications, experience, skills, geographic location, government requirements, and business or organizational needs.

The successful candidate will be eligible for annual bonus and long-term incentive, if applicable.

We offer a comprehensive package of benefits. Available benefits include medical, dental, vision healthcare and other insurance benefits (for employee and family), retirement benefits, including 401(k), paid holidays, vacation, and compassionate and sick days.

Required Skills:

Biophysics, Computational Biology, Computational Biophysics, Computational Methods, Machine Learning (ML), Protein Structure Modeling, Protein Structure Prediction

US and Puerto Rico Residents Only:

Our company is committed to inclusion, ensuring that candidates can engage in a hiring process that exhibits their true capabilities.

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San Francisco Residents Only:

We will consider qualified applicants with arrest and conviction records for employment in compliance with the San Francisco Fair Chance Ordinance

Los Angeles Residents Only:

We will consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance

Employee Status:

Regular

Relocation:

Domestic

VISA Sponsorship:

Yes

Travel Requirements:

10%

Flexible Work Arrangements:

Not Applicable

Shift:

Not Indicated