About AbbVie

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us atwww.abbvie.com. Follow @abbvie onLinkedIn,Facebook,Instagram,XandYouTube.

Position Overview:

The PrincipalMachine Learning Scientist II will advance deep learning methods for biologics discovery at AbbVie.

This individual contributor role focuses on advancing sequence- and structure-based deep learningmodelsfor protein and antibody designwith emphasis on adapting them to the constraints of designing therapeutic antibodies.The work centers on building models that improve early decision-making in molecule prioritization andguidemoleculeoptimization,while balancingmultiple properties,in orderto reduce drug discovery timelines and increase the probability of successin patients.

A major focus of the role is extending protein modeling to supportthe complexitiesofbiotherapeutics, includingcomplexmultispecificformats,sequence diversity, conformational flexibility, and induced-fit behavior, whichpresent importantmodeling challenges and opportunities for innovation.

The role requires close collaboration with antibody engineers, computational physicists, and biologists to translatedomain conceptsintomodelingdecisions that will drive therapeuticimpact.

Responsibilities:

• Advance sequence- and structure-conditioned generative modeling for biologics using protein language models, diffusion-based generative models, and graph- and geometry-based deep learning methods.
• Develop modeling strategies that improve therapeutic relevance, support multi-objective optimization, and surface liabilities earlier in the discovery process.
• Develop predictivemodeling approaches for multispecific biologics that optimize chain-complexation states, linker architecture, and higher-order molecular geometry toidentifydesigns with favorable efficacy and developability.
• Adapt protein models to CDR H3 loop behavior, including high diversity, conformational flexibility, and induced-fit effects.
• Build rigorousinternalbenchmarking frameworks, establish evaluation standardsfor external AI/ML platforms, and lead technical due diligence on partner capabilities.
• Use interpretability methods to uncover missing biological concepts, guide targeted model improvements.
• Partner with deployment engineers to deliver scalable, stable production modelsand model monitoring workflows.

• MSwith 14+yearsorPhDwith 8+yearsinmachine learning, computer science, applied mathematics, data science, computational biology, or a closely related field,
• 2+ years of experiencebuildingmodels with biological data.
• Deep expertise in modern deep learning methods, including transformers, graph neural networks, protein language models, diffusion models, or related architectures.
• Experience applying ML to sequence-based and structure-based biological problems.
• Strongtrack recordof adapting models to complex, real-world scientific constraints.
• Ability to reasonaboutopen-ended technical problems and define a path forward in ambiguous settings.
• Strong python programming skills and familiarity with modern MLtools and frameworks, including distributed training workflows.
• Ability to collaborate effectively with experimental and computational scientists.
• Ability to communicate technical tradeoffs clearly across disciplines.

Work Environment

• Hybrid role based in Worcester, MA, South San Francisco, CA or Cambridge, MA.
• On-site presence required 3 days per week. Relocation is available.
• This is an individual contributor role.

Applicable only to applicants applying to a position in any location with pay disclosure requirements under state orlocal law:

• The compensation range described below is the range of possible base pay compensation that the Companybelieves ingood faith it will pay for this role at the timeof this posting based on the job grade for this position.Individualcompensation paid within this range will depend on many factors including geographic location, andwemayultimatelypaymore or less than the posted range. This range may bemodifiedin thefuture.
• We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick),medical/dental/visioninsurance and 401(k) to eligibleemployees.
• This job is eligible toparticipatein our long-term incentiveprograms.

Note: No amount of payis considered to bewages or compensation until such amount is earned, vested, anddeterminable.The amount and availability of any bonus,commission, incentive, benefits, or any other form ofcompensation and benefitsthat are allocable to a particular employeeremainsin the Company's sole andabsolutediscretion unless and until paid andmay bemodifiedat the Companys sole and absolute discretion, consistent withapplicable law.

AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled.

US & Puerto Rico only - to learn more, visithttps://www.abbvie.com/join-us/equal-employment-opportunity-employer.html

US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more:

https://www.abbvie.com/join-us/reasonable-accommodations.html