Senior Scientist, Computational… at Pfizer · Cambridge

Senior Scientist, Computational Biotherapeutics Engineering Location: United States - Massachusetts - Cambridge Use Your Power for Purpose At Pfizer, our purpose is to deliver breakthroughs that transform patients' lives. Central to this mission is our Research and Development team, which strives to convert advanced science and cutting-edge technologies into impactful therapies and vaccines. Whether you are engaged in discovery sciences, ensuring drug safety and efficacy, or supporting clinical trials, your role is crucial. You will leverage innovative design and process development capabilities to expedite the delivery of top-tier medicines to patients globally. What You Will Achieve Pfizer’s BioMedicine Design department is seeking a scientist with expertise in computational AI/ML methods spanning protein modeling, representation learning, and generative model development.  You will join a computational team working closely with experimental scientists on discovery and optimization of industry‑leading biotherapeutics. You will implement, evaluate, and apply state‑of‑the‑art AI/ML methods to advance biotherapeutic discovery and engineering, integrating new models into scalable discovery workflows and decision‑making. By collaborating across departments, you will shape the next generation of AI/ML architectures, training strategies, and evaluation approaches for biotherapeutic design.  This role offers the opportunity to directly influence the design of clinical molecules, helping achieve Pfizer’s goal of breakthroughs that change patients’ lives. How You Will Achieve It Implement advanced cutting-edge AI and machine learning workflows for computational protein design (including fine-tuning protein language models and generative protein design) in HPC or scalable cloud computing environments Collaborate with machine learning colleagues on the design and training of AI/ML models for antibody developability engineering.  Apply these models to optimize leads for antibody drug discovery projects. Stay informed about developments in NLP, ML, and generative AI to create innovative solutions for molecular discovery, design, and optimization to advance therapeutic discovery and development. Serve as a technical expert in deep learning models for protein sequence and structure, supporting discovery teams with AI/ML‑driven design strategies. Analyze large‑scale sequence, structure, and experimental datasets to learn representations linking protein features to developability and pharmaceutical properties. Communicate complex scientific ideas, model behavior, limitations, and design recommendations to both technical and non-technical audiences, fostering collaborations across multidisciplinary teams. Collaborate with computational and wet lab experts to optimize the computational developability platform, offering both individual and team-based innovative solutions. Qualifications Must‑Have PhD in biochemistry, computational chemistry, computational biology, machine learning, or a related field with 0 t 3 years of experience OR Master's Degree in biochemistry, computational chemistry, computational biology, machine learning with 7 to 8 years of experience OR BA/BS with 9 to 11 years of experience Demonstrated track record (including publications or equivalent impact) of using AI/ML‑driven protein modeling/design to influence project direction and strategy Hands‑on experience using and interrogating modern AI/ML models for protein representation, structure prediction, or generation (e.g., transformer or diffusion-based approaches). Strong understanding of protein structure, sequence–structure relationships, and model evaluation. ·Experience programming in Python and using modern scientific or machine learning libraries (e.g., NumPy/SciPy, scikit-learn, PyTorch), including training and evaluation workflows. Experience working with large biological datasets and bioinformatics resources. Nice to Have Experience with protein language models (e.g., ESM‑family models), generative structure models (e.g., RFdiffusion, BoltzGen, BindCraft), and structural prediction AI models (e.g. AlphaFold) Familiarity with equivariant or structure-aware neural networks. Knowledge of antibody structure, multispecific design, or developability modeling. Experience running and scaling deep learning workloads on HPC/GPU/cloud environments using technologies such as Slurm, AWS, or Google cloud. Experience with structure-based molecular modeling software (Rosetta, Schrödinger, MOE, FoldX) Work Location Assignment: Hybrid Hybrid colleagues are required to be onsite or in-person for an average of 2.5 weekdays (Monday to Friday) per week The annual base salary for this position ranges from $93,600.00 to $156,000.00. In addition, this position is eligible for participation in Pfizer’s Global Performance Plan with a bonus target of 12.5% of the base salary and eligibility to participate in our share based long term incentive program. We offer comprehensive and generous benefits and programs to help our colleagues lead healthy lives and to support each of life’s moments. Benefits offered include a 401(k) plan with Pfizer Matching Contributions and an additional Pfizer Retirement Savings Contribution, paid vacation, holiday and personal days, paid caregiver/parental and medical leave, and health benefits to include medical, prescription drug, dental and vision coverage. Learn more at Pfizer Candidate Site – U.S. Benefits | (uscandidates.mypfizerbenefits.com). Pfizer compensation structures and benefit packages are aligned based on the location of hire. The United States salary range provided does not apply to Tampa, FL or any location outside of the United States. Relocation assistance may be available based on business needs and/or eligibility. Candidates must be authorized to be employed in the U.S. by any employer. U.S. work visa sponsorship (such as TN, O-1, H-1B, etc.) is not available for this role now or in the future. Sunshine Act Pfizer reports payments and other transfers of value to health care providers as required by federal and state transparency laws and implementing regulations.  These laws and regulations require Pfizer to provide government agencies with information such as a health care provider’s name, address and the type of payments or other value received, generally for public disclosure.  Subject to further legal review and statutory or regulatory clarification, which Pfizer intends to pursue, reimbursement of recruiting expenses for licensed physicians may constitute a reportable transfer of value under the federal transparency law commonly known as the Sunshine Act.  Therefore, if you are a licensed physician who incurs recruiting expenses as a result of interviewing with Pfizer that we pay or reimburse, your name, address and the amount of payments made currently will be reported to the government.  If you have questions regarding this matter, please do not hesitate to contact your Talent Acquisition representative. EEO & Employment Eligibility Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status.  Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA.  Pfizer is an E-Verify employer.  This position requires permanent work authorization in the United States. Pfizer endeavors to makewww.pfizer.com/careersaccessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process and/or interviewing, please emaildisabilityrecruitment@pfizer.com. This is to be used solely for accommodation requests with respect to the accessibility of our website, online application process and/or interviewing. Requests for any other reason will not be returned. Research and Development