AIRx Director, Computational & AI Biologics Design Lead

## About this role

Reporting to the Head of AIRx, the Computational & AI Biologics Design Lead sits at the scientific heart of the Takeda Boston (TBOS) Large Molecule Pod. You will drive in silico biologics design, apply generative AI and structure-informed methods to antibody and large-molecule programs, and connect Takeda’s internal AI/ML platform capabilities to the day-to-day decisions of a fast-moving drug-hunting team. This is a deeply hands-on role expected to operate with urgency and independence while remaining tightly integrated with biology, protein engineering, and translational science colleagues across the pod.

### What you will do day-to-day

You will define and drive the computational design strategy across the pod’s large-molecule programs, including antibody, VHH, and multispecific or fusion formats, from early format selection through lead optimization. That includes designing and prioritizing molecular candidates using generative AI/ML and computational modeling approaches and providing ranked shortlists with quantified uncertainty to the pod. You will integrate structural biology data to inform format selection, epitope targeting, and interface optimization; oversee virtual screening, binding affinity prediction, and developability risk assessment; and establish approaches to accelerate lead optimization by compressing DMTA cycles through AI-guided design.

You will act as the pod’s primary computational interface to Takeda’s AI/ML research platform, evaluating and benchmarking new AI design tools against the pod’s specific biologics modalities. You will define and steward data requirements for AI model training within the pod, including how to structure data return from experimental campaigns, annotation standards, and integration with Takeda’s data infrastructure, and contribute to building and curating AI/ML training datasets from pod experimental outputs.

You will be a hands-on computational authority within pod governance, preparing and presenting in silico analyses for PRC reviews, design review boards, and candidate declaration milestones. You will ensure computational requirements are integrated early in external experimental campaigns and interface with discovery automation capabilities to define assay and data readout specifications when programs enter automated workflows.

Beyond internal work, you will maintain deep subject-matter expertise in advances in AI for biologics design and structure prediction, translate emerging capabilities into actionable proposals, represent Takeda’s computational biologics capabilities with external partners and at conferences, and contribute to publications and IP filings as appropriate. Mentorship within the AIRx context and helping shape computational biologics practices across the broader research organization are also expected.

### Qualifications and technical profile

You must have a Ph. D in Computational Biology, Bioinformatics, Structural Biology, Computer Science, or a closely related discipline and 10+ years of drug discovery experience with a demonstrated track record of computational impact on large-molecule or biologics programs; industry experience is strongly preferred. Deep expertise in antibody and protein sequence, structure, and function modeling is required, with proficiency in generative or predictive AI frameworks applied to biologics design.

You should have broad proficiency in computational tools relevant to biologics spanning structural analysis, molecular simulation, developability prediction, and bioinformatics. Strong coding skills (Python required) are mandatory, and you should have experience building and deploying ML models in a drug discovery context, with familiarity with cloud-based compute and MLOps practices. The role requires demonstrated ability to operate both as a technical individual contributor and as a cross-functional scientific partner in a fast-paced, program-driven environment, and to present complex computational findings to biologists, clinical scientists, and senior leadership with clarity and scientific rigor.

Preferred experience includes work with multispecific antibody formats and their engineering, developability, and PK/PD considerations; integrating physics-based modeling with deep learning approaches; defining data requirements and governance for AI/ML platform development across multiple programs or sites; operating with external AI design partners including co-design workflows and campaign-level data return; and a track record of contributing to IND-enabling programs with familiarity with candidate declaration criteria and biologics CMC considerations.

### Location, work arrangement, and compensation

The position will be based in Cambridge, MA. This position is currently classified as “hybrid” by Takeda’s Hybrid and Remote Work policy. Locations listed include Boston, MA.

For Location: Boston, MA U.S. Base Salary Range: $177,000.00 - $278,080.00

The estimated salary range reflects an anticipated range for this position. The actual base salary offered may depend on a variety of factors, including the qualifications of the individual applicant for the position, years of relevant experience, specific and unique skills, level of education attained, certifications or other professional licenses held, and the location in which the applicant lives and/or from which they will be performing the job. The actual base salary offered will be in accordance with state or local minimum wage requirements for the job location.

U.S. based employees may be eligible for short-term and/ or long-term incentives and to participate in medical, dental, vision insurance, a 401(k) plan and company match, short-term and long-term disability coverage, basic life insurance, a tuition reimbursement program, paid volunteer time off, company holidays, and well-being benefits, among others. U.S. based employees are also eligible to receive, per calendar year, up to 80 hours of sick time, and new hires are eligible to accrue up to 120 hours of paid vacation.

### Our expectations about how you work

We expect you to define decision frameworks and scientific standards that influence candidate prioritization, progression, and overall portfolio direction. Your proposals will directly shape what gets engineered, what gets deprioritized, and what ultimately reaches the clinic. You will partner closely with the Biologics Discovery Lead and collaborate with translational and DMPK scientists to model PK/PD behavior, TMDD, and species cross-reactivity in silico to inform study design and reduce in vivo cycle time.

Takeda is an equal opportunity employer committed to diversity and providing equal employment opportunities without regard to protected characteristics in accordance with applicable law.