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About This Role
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Job Description
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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.
Purpose
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.
Key Accountabilities
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.
Qualifications \& Competencies
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.
For Location:
Boston, MAU.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. U.S. based employees may be eligible 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.
EEO Statement
*Takeda is proud in its commitment to creating a diverse workforce and providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, parental status, national origin, age, disability, citizenship status, genetic information or characteristics, marital status, status as a Vietnam era veteran, special disabled veteran, or other protected veteran in accordance with applicable federal, state and local laws, and any other characteristic protected by law.*
Locations
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Boston, MAWorker Type
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EmployeeWorker Sub\-Type
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RegularTime Type
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Job Exempt
Yes
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Salary Context
This $177K-$278K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Takeda Pharmaceuticals, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($227K) sits 23% above the category median. Disclosed range: $177K to $278K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Takeda Pharmaceuticals AI Hiring
Takeda Pharmaceuticals has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $278K - $407K.
Location Context
AI roles in Boston pay a median of $216,350 across 460 tracked positions. That's 8% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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