More jobs:
Director, Generative AI
Job in
Boston, Suffolk County, Massachusetts, 02298, USA
Listed on 2026-01-01
Listing for:
BioSpace
Full Time
position Listed on 2026-01-01
Job specializations:
-
IT/Tech
AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Join to apply for the Director, Generative AI role at Bio Space.
We are inviting individuals with deep knowledge of machine learning and artificial intelligence with extensive experience in Generative AI to join us in the Shinr
AI Center for AI/ML at Takeda, based in Cambridge, MA. At the Shinr
AI Center, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the application of artificial intelligence in developing innovative medicine for patients. We like to solve problems, take initiative, pitch in when needed, and are enthusiastic about trying new things. We are looking for curious thinkers who like to tackle challenging, real-world problems in a rewarding environment where your contributions are valued and have a direct impact.
We are building a diverse team whose skills, experiences, and backgrounds complement one another. Extensive experience working in Pharma or Biotech is optional. A strong curiosity for a deeper understanding of human health and disease to deliver innovative medicine for patients is a must.
Accountabilities
• Partner with data science teams, domain experts, and business units to identify and prioritize opportunities to leverage machine learning and particularly generative AI and agentic AI to drive decision making and automation across all levels of the R&D organization.
• Translate business needs into clearly scoped machine learning projects, and take a hands‑on approach to steer solution design and implementation.
• Educate, demonstrate, guide, and enable the application of machine learning and particularly generative AI in various pharmaceutical R&D operations and scientific domains.
• Identify, monitor, and validate relevant external AI/ML developments, cultivate relationships with external domain experts and partners, and report and present emerging novel developments within the organization to further innovation and shape long‑term strategy and governance.
• Proactively build relationships across the company to inform your work and contribute to internal and external collaborations, through involvement in working groups, and the writing of insightful, engaging, and actionable opinion pieces that are easily digestible by internal decision makers and stakeholders.
• Be the leading voice for building common capability and approaches and for adopting best practices.
• Work in collaboration with our Ethics and Governance teams to ensure our AI/ML applications are developed ethically and provide broad benefits to our patients and business.
• Help talented, driven, enthusiastic AI/ML engineers and data scientists across the company grow professionally.
• Measure, document, and communicate impacts of the Center’s efforts.
Education, Behavioural Competencies and Skills
• A track record of partnering cross‑functionally with a wide range of stakeholders and cross‑functional teams to develop and deploy novel data solutions in production environments.
• Demonstrated passion for making complex technology more accessible and the ability to communicate complex technical topics simply and convincingly to a wide range of audiences.
• Demonstrated ability in translating big picture business and product ideas into micro use cases and has a strong focus on solving core problems to deliver simple solutions.
• Experience recognizing and communicating the implications of emerging technologies.
• Excellent communication, prioritization, and interpersonal skills, with a high level of attention to detail.
• An advanced degree (M.S., PhD.) in mathematics, applied statistics, computer science, machine learning or similar. With 8+ years of experience architecting, building, launching, and maintaining end‑to‑end ML systems from whiteboard to production at scale across a range of models and platforms, such as building agentic and LLM based solutions; fine‑tuning large language models for domain specific applications; designing transfer learning strategy to enable learning from small datasets;
demonstrated authority in a variety of AI/ML problems and domains, with depth in at least two (computer vision, natural language processing, geometric…
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