Interested in this AI/ML Engineer role at Takeda Pharmaceuticals?
Apply Now →Skills & Technologies
About This Role
By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use. I further attest that all information I submit in my employment application is true to the best of my knowledge.
Job Description
At Takeda, we are a forward-looking, world-class R&D organization that unlocks innovation and delivers transformative therapies to patients. By focusing R&D efforts on three therapeutic areas and other targeted investments, we push the boundaries of what is possible to bring life-changing therapies to patients worldwide.
The AI/ML organization at Takeda is building a team to transform how medicines are discovered. Our goal is to apply AI and machine learning across the entire drug discovery process, not just isolated steps, but as an integrated approach from target identification through development. This requires discernment: knowing which models and methods fit each problem, and the creativity to adapt when they don't. We work with foundational models, generative approaches, and autonomous systems, but the tools only matter when paired with people who understand the science deeply enough to use them well. Our team brings together computational scientists, biologists, engineers, and drug hunters. If you want to contribute your expertise to hard problems alongside colleagues with different perspectives, and help shape how AI delivers real impact in drug discovery, we'd like to hear from you.
Position Overview
We are seeking an innovative and dynamic AI/ML Associate Director with a passion for leveraging AI/ML across large molecule drug discovery and development — including antibody design, bispecifics, ADCs, fusion proteins, and other biologics modalities to join our Large Molecule AI/ML team. This role will be part of a multidisciplinary team focused on integrating advanced computational methods with cutting-edge experimental strategies to drive breakthrough discoveries in large molecule therapeutics and deepen our understanding of disease biology. The ideal candidate will have a strong background in computational biology, machine learning, and structural modeling and specifically with the application of AI/ML in diverse biologics modalities, with a demonstrated ability to embed AI/ML capabilities directly within drug programs to address a broad range of discovery and development needs. Beyond technical execution, this Associate Director is expected to operate with a high degree of strategic autonomy: shaping the team’s methodological roadmap, mentoring scientists across functions, and serving as a primary AI/ML voice to senior cross-functional stakeholders in Program teams.
Key Responsibilities
- Develop and implement state-of-the-art AI/ML methodologies for de novo biologics design and optimization across modalities including antibodies, bispecifics, VHH/nanobodies including fine-tuning protein language models and generative protein design.
- Develop, implement, and deploy advanced machine learning algorithms for the multi-objective optimization of antibodies, antigens, ADCs, and other biologics.
- Build tools to incorporate data from integrated Design-Predict-Make-Confirm cycles with automated experimental platforms generating quality data at scale needed for project-specific and foundational models.
- Innovate, develop, and apply predictive models for protein design and developability engineering, utilizing large-scale NGS, in vitro, in vivo and other proprietary in-house and external data sources.
- Manage and process large-scale biological datasets for model training and evaluation
- Stay abreast of advancements in NLP, ML, and generative AI to build novel tools that enhance therapeutic discovery and development.
- Communicate complex scientific ideas effectively to both technical and non-technical audiences, fostering collaboration across multidisciplinary teams.
- Proactively identify and champion opportunities to apply AI/ML across the drug program portfolio, engaging program teams early to scope computational contributions and translate scientific questions into tractable ML problems.
- Represent the AI/ML function as a scientific and strategic partner to senior cross-functional stakeholders, including Discovery Biology, CMC, and Program Leadership, contributing to portfolio-level decision-making.
Qualifications
- PhD degree in a scientific discipline (or equivalent) with 6+ years relevant experience, or MS with 12+ years relevant experience, or BS with 14+ years relevant experience Proven track record in developing machine learning models for chemical and biological data, including AI/ML-enabled molecular generation and affinity prediction.
- Demonstrated experience in modeling antibody/ antigen sequence, structure and interaction.
- Proficiency in programming languages such as Python and experience with cloud computing capabilities.
- Strong analytical and problem-solving skills, with demonstrated creativity and the ability to contribute individually and collaboratively.
- Versatile communicator who can elucidate complex ideas to non-specialists and commitment to continuous improvement and innovation.
- Demonstrated learning agility, and scientific curiosity while maintaining focus on driving greater impact in the face of uncertainty and change.
- Strong problem-solving aptitude and strategic thinking with an entrepreneurial mindset.
Preferred Qualifications & Skills:
- Experience developing or applying modern ML architectures for antibody design models (LLMs, diffusion models, flow-matching, Bayesian Optimization, GNNs, etc.)
- Experience designing de novo binders for specified targets and epitopes
- Experience analyzing NGS-derived antibody repertoires for sequence- and structure-based design
- Experience with molecular simulation and conformational analysis techniques
ADDITIONAL INFORMATION
- The position will be based in Cambridge, MA
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:
$154,400.00 - $242,550.00
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.
For information about our benefits, please click here.
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
Boston, MAWorker Type
EmployeeWorker Sub-Type
RegularTime Type
Full timeJob 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 $154K-$242K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($198K) sits 6% below the category median. Disclosed range: $154K to $242K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Takeda Pharmaceuticals AI Hiring
Takeda Pharmaceuticals has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $242K - $278K.
Location Context
AI roles in Boston pay a median of $215,000 across 323 tracked positions.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.