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Director, Computational & AI Biologics Design Lead

Takeda Research is constructing a Lab of Tomorrow built on AI, automation, new ways of working, and talent with the singular vision of delivering differentiated medicines to the clinic at speed and cost. To catalyze these efforts, Takeda is creating two complementary units: AI Research Accelerator (AIRx) and Discovery Automation & Robotics (DAR).

AIRx will have a group of a dedicated group of experienced biologic drug hunters with the autonomy of a biotech and the resources of a leading pharmaceutical company. It is designed to incubate the future AI-powered operating models for large molecule discovery and deliver candidates to the clinic at industry leading speed and success rates.

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. As part of the AIRx team, this role serves as a strategic computational leader, driving in silico biologics design and shaping how AI-enabled biologics discovery is executed for select programs. This role drives in silico biologics design, applies generative AI and structure-informed methods to antibody and large-molecule programs, and connects Takeda's internal AI/ML platform capabilities to the day-to-day decisions of a fast-moving drug-hunting team.

In addition, the role defines decision frameworks and scientific standards that influence candidate prioritization, progression, and overall portfolio direction. Proposals from this role directly shape what gets engineered, what gets deprioritized, and what ultimately reaches the clinic. The role is deeply hands-on, with a mandate to operate with urgency and independence while remaining tightly integrated with biology, protein engineering, and translational science colleagues across the pod.

1. In Silico Biologics Design for Pod Programs

• 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.
• Design and prioritize molecular candidates using generative AI/ML and computational modeling approaches
• Serve as a scientific advisor to pod leadership on computational design decisions, influencing program direction and key trade-offs
• Partner closely with the Biologics Discovery Lead to translate computational proposals into testable engineering priorities; challenge and be challenged on scientific assumptions in equal measure.
• Integrate structural biology data into design strategies to inform format selection, epitope targeting, and interface optimization.
• Oversee virtual screening, binding affinity prediction, and developability risk assessment for candidate sequences; provide ranked shortlists with quantified uncertainty to the pod.
• Establish and improve approaches to accelerate lead optimization by compressing DMTA cycles through AI-guided design, with the goal of achieving the target candidate profile in fewer rounds.
• Collaborate with translational and DMPK scientists to model PK/PD behavior, TMDD, and species cross-reactivity in silico, informing study design and reducing in vivo cycle time.

2. AI/ML Platform Interface and Data Strategy

• Serve as the pod's primary computational interface to Takeda's AI/ML research platform; evaluate and benchmark new AI design tools against the pod's specific biologics modalities and program needs.
• Define and steward data requirements for AI model training within the pod: structure data return from experimental campaigns, annotation standards, and integration with Takeda's data infrastructure.
• Contribute to building and curating AI/ML training datasets from pod experimental outputs to enable continuous model improvement.
• Guide development and refinement of computational workflows to enable pod scalability, speed and reproducibility across the DMTA cycle; document methods to support cross-pod learning.

3. Pod Integration and Scientific Operations

• Act as a hands-on computational authority within pod governance: prepare and present in silico analyses for PRC reviews, design review boards, and candidate declaration milestones.
• Ensure computational requirements are integrated early in external experimental campaigns to maximize data return value.
• Interface with Takeda's discovery automation capabilities to define assay and data readout specifications for pod programs entering automated workflows when applicable.

4. Scientific Leadership and External Engagement

• Maintain deep subject-matter expertise by staying current with advances in AI for biologics design and structure prediction; translate emerging capabilities into actionable proposals for the pod.
• Identify and translate relevant external innovations into opportunities that enhance pod capabilities and programs
• Represent Takeda's computational biologics capabilities in interactions with external partners, at conferences, and in the scientific community; contribute to publications and IP filings as appropriate.
• Provide scientific mentorship within the AIRx context and help shape computational biologics practices across the broader research organization.

Expected Requirements:

• PhD in Computational Biology, Bioinformatics, Structural Biology, Computer Science, or a closely related discipline.
• 10+ years of drug discovery experience with a demonstrated track record of computational impact on large-molecule or biologics programs; industry experience strongly preferred.
• Deep expertise in antibody and protein sequence, structure, and function modeling, with proficiency in generative or predictive AI frameworks applied to biologics design.
• Broad proficiency in computational tools relevant to biologics, spanning structural analysis, molecular simulation, developability prediction, and bioinformatics.
• Strong coding skills (Python required); experience building and deploying ML models in a drug discovery context; familiarity with cloud-based compute and MLOps practices.
• Demonstrated ability to operate as both a technical individual contributor and a cross-functional scientific partner in a fast-paced, program-driven environment.
• Versatile communicator: able to present complex computational findings to biologists, clinical scientists, and senior leadership with clarity and scientific rigor.

Preferred

• Experience with multispecific antibody formats and the associated engineering, developability, and PK/PD considerations.
• Experience integrating physics-based modeling with deep learning approaches to improve prediction accuracy and generalization.
• Prior experience defining data requirements and governance for AI/ML platform development across multiple programs or sites.
• Experience operating within or alongside an external AI design partner environment, including co-design workflows and campaign-level data return.
• Track record of contributing to IND-enabling programs; familiarity with candidate declaration criteria and biologics CMC considerations.

ADDITIONAL INFORMATION

• The position will be based in Cambridge, MA. This position is currently classified as "hybrid" by Takeda's Hybrid and Remote Work policy

Takeda Compensation and Benefits Summary

We understand compensation is an important factor as you consider the next step in your career. We are committed to equitable pay for all employees, and we strive to be more transparent with our pay practices.

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U.S. Base Salary Range:

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.

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Job Exempt