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Job Description
Title: Technical Product Owner, AI-native Large molecule Discovery
Location: Cambridge, MA
Key Partners: Product Lead (Business Lead), AI-native Large molecule Discovery teams, and external partner engineering squads.
This role is 100% dedicated to Takeda R&D's Labs of Tomorrow Program.
About the role:
As the Technical Product Owner, you will be the technical delivery counterpart to the Business Lead for Takeda's next-generation large molecule discovery AI-native software products. Where the Business Lead defines the scientific vision, customer experience, and value the product must deliver, the Technical Product Owner owns translating that vision into a buildable, sequenced, and technically sound delivery plan — and is accountable for the engineering execution, quality, and technical integrity of the product family. You will be the day-to-day technical leader for Takeda's Large molecule discovery product family, working in direct partnership with the Business Lead to convert scientific and business intent into an executable technical roadmap. You will own the backlog, architecture direction, and delivery cadence for the development squads building AI-native large molecule discovery capabilities, ensuring what gets built is technically robust, scalable, well-integrated with the broader Labs of Tomorrow ecosystem, and delivered in a way that enables the scientific and business value the Business Lead is accountable for.
You will combine strong technical product ownership discipline (backlog management, sprint planning, release management, stakeholder translation) with genuine biology fluency; enough to shape the redesign of large molecule processes into AI-first workflows alongside the business, converse credibly with medicinal chemists, computational chemists, and structural biologists, and make sound technical trade-offs that respect the underlying scientific method rather than working from a purely IT lens. You will operate at the product-team leadership level, working side-by-side with the Business Lead as a single aligned leadership pair, providing day-to-day direction to engineers, data scientists, ML scientists, and technical leads, and interfacing with other Technical Product Owners across the Labs of Tomorrow portfolio to ensure architectural coherence and reuse across the suite of products.
How will you contribute:
- Own and maintain the technical backlog and delivery roadmap for the Large molecule discovery product family, translating the Business Lead's strategic vision and prioritized capabilities into epics, user stories, and technically sequenced releases.
- Partner with the Business Lead as a single accountable pair, jointly owning outcomes: the Business Lead is accountable for scientific/business value and adoption; the Technical Product Owner is accountable for technical feasibility, delivery quality, timeline, and platform integrity.
- Work with a team of Product Owners to translate re-imagined AI-native discovery workflows into clear technical requirements, acceptance criteria, and system designs in partnership with architects, engineers, data scientists, and ML scientists.
- Support use case level Scrum Masters for sprint planning, backlog grooming, story writing, and release management for the development squad(s), ensuring the team has clear, well-specified, de-risked work at all times, and that individual use cases within the product family are aligned.
- Make and own technical trade-off decisions (build vs. buy, sequencing, technical debt vs. new capability, integration approach) within the intent and priorities set by the Business Lead and Labs of Tomorrow leadership.
- Ensure technical solutions are grounded in real laboratory and computational biology workflows — validating that what is built reflects how biologists, structural biologists, and computational scientists actually work, not just how requirements were documented.
- Own integration and architectural alignment with adjacent platforms (ELN/LIMS, compound registration, HTE, automated synthesis, assay data platforms, scientific data lakes/data models) in coordination with enterprise architecture and other product teams.
- Manage technical risk, dependencies, and blockers across engineering, data, ML, and infrastructure workstreams, escalating and resolving with DD&T, Computational Science, and external delivery partners as needed.
- Define and track engineering KPIs (velocity, quality, technical adoption/usage telemetry, system performance, reliability) that complement the Business Lead's scientific and business value metrics, feeding a shared, integrated view of product health.
- Collaborate with our external technology/build partners on technical scope, architecture decisions, delivery timelines, and code/solution quality.
- Identify opportunities where AI agents, predictive models, automation, and data platform capabilities can be technically implemented to redesign scientific workflows, and translate these into feasible, well-architected product features.
- Partner with other Technical Product Owners across the Labs of Tomorrow portfolio to ensure extensibility, shared components, and consistent technical standards across the product suite.
What you bring to Takeda:
- Bachelor's or Master's degree in Biology, Computational Biology, Chemical Engineering, Computer Science, Bioinformatics, or a related technical/scientific field; 8+ years of progressively responsible experience in software product ownership, technical program management, or engineering leadership, ideally within pharmaceutical, biotech, or life sciences R&D.
- Biology knowledge and prior hands-on biology laboratory experience (academic or industry) sufficient to understand medicinal/computational biology workflows, laboratory processes, and the scientific reasoning behind discovery decisions, and to engage credibly with bench and computational chemists.
- Demonstrated experience as a Technical Product Owner, Product Manager, or similar role, owning a backlog and technical roadmap for a software product or platform end to end.
- Strong grounding in Agile/Scrum delivery: backlog management, story writing and acceptance criteria, sprint planning, release management, and cross-functional ceremonies.
- Hands-on technical background (prior coding, data engineering, or scientific computing experience) sufficient to engage credibly with software engineers, computational scientists, data scientists, and ML scientists on design and trade-off discussions.
- Working knowledge of modern software architecture, cloud platforms, data platforms, and AI/ML system design principles as applied to scientific software.
- Experience translating ambiguous scientific or business requirements into clear, buildable technical specifications.
- Strong stakeholder management skills, with the ability to partner effectively with a Business Lead/Product Lead as a joint leadership pair and to manage technical stakeholders across engineering, data science, and infrastructure teams.
- Experience directing external technology/development partners, holding them accountable to technical scope, timeline, and quality.
- Strong communication skills, able to translate between scientific stakeholders, business leadership, and technical delivery teams.
- Ability to operate effectively in a complex, matrixed environment, influencing without direct authority.
Preferred:
- Experience with scientific software platforms such as ELN/LIMS, compound registration systems, high-throughput experimentation (HTE) platforms, automated synthesis systems, or assay data platforms.
- Exposure to bioinformatics tools, molecular data standards (e.g., SMILES, SDF, InChI), scientific data models, or AI-ready scientific data practices.
- Experience with AI/ML-enabled discovery applications, including predictive modeling, generative biology, or agentic AI workflows.
- Prior experience working in a laboratory automation or digital lab environment.
- Relevant certifications in Agile/Scrum product ownership (e.g., CSPO, SAFe POPM).
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.
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 $177K-$278K range is above the 75th percentile 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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($227K) sits 7% above the category median. Disclosed range: $177K to $278K.
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
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