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Senior AI Product Manager – Drug Discovery ML

Remote: 
Full Remote
Contract: 
Full time
Work from: 
Anywhere

Offer summary

Qualifications:

Background in computational chemistry, cheminformatics, computational biology, or bioinformatics., Experience as a Product Manager in life sciences, specifically in AI/ML products for drug discovery., Strong understanding of pharma R&D workflows and the ability to translate complex scientific concepts into product features., Familiarity with transformer-based models and ML-based property prediction technologies..

Key responsibilities:

  • Define the vision, product strategy, and roadmap for AI products in drug discovery.
  • Own the end-to-end product lifecycle from discovery through delivery and iteration.
  • Collaborate with cross-functional teams to deliver AI products based on foundational models.
  • Engage with biopharma customers to understand their workflows and advocate for user needs.

Apheris logo
Apheris Computer Hardware & Networking Startup https://www.apheris.com/
11 - 50 Employees
See all jobs

Job description

About the role
At Apheris, we power federated data network in life sciences to address the data bottleneck in training highly performant ML models. Publicly available, molecular datasets are insufficient to train high-quality ML models that meet industry requirements. Our product addresses this by hosting networks where biopharma organizations collaboratively train higher quality models on their combined data. The Apheris product is a set of drug discovery applications - enriched with the proprietary data of network participants. Our federated computing infrastructure with built-in governance and privacy controls ensure that the data IP and ownership always stays with the data custodians.

As we are doubling down on structural biology and ADMET use cases as a focus area within our drug discovery work, we are looking for a Senior AI Product Manager to lead the vision, strategy, and execution of our AI model products in these domains. This is a hands-on, high-impact role focused on advancing the state of the art in applying foundational models to structural biology and ADMET problems. You’ll work closely with our leadership team and cross-functionally with ML engineers, domain experts, customers, and our core product teams to build cutting-edge AI systems that translate into meaningful advances in drug discovery.

You should bring deep expertise in building AI products for drug discovery as well as an understanding of foundational models for protein structure prediction and ADMET. You must also understand the application of these models in drug discovery workflows and have a track record of setting strategy, breaking down complex technical problems, and delivering impactful AI products.

If you want to be part of a mission-driven team building cutting-edge AI systems for life sciences – and you know what it takes to move from foundational models to domain-specific impact – this role is for you. 
What you will do
  • Define the vision, product strategy and roadmap for our AI products in drug discovery (structure-based and ligand-based methods) 
  • Own the end-to-end product lifecycle from discovery through delivery and iteration
  • Translate the complex needs of computational scientists, drug discovery teams, and R&D stakeholders into clear product requirements and user-centric features
  • Collaborate with cross-functional teams of ML engineers, software engineers, data engineers, and domain experts to deliver AI products based on e.g. OpenFold-3 and a chemistry foundation model (e.g. for ADMET)
  • Represent our AI products in conversations with biopharma customers, technical roadmap discussions, and partner collaborations
  • Work with users of the AI products (e.g., computational chemists, medicinal chemists, structural biologists) in pharma R&D to deeply understand their workflows, pain points, and unmet needs 
  • Champion a user-first culture by continuously advocating for the needs, workflows, and goals of pharma R&D scientists
  • Stay on top of AI-driven drug discovery trends (e.g., foundation models, multimodal learning, graph-based representations) to inform product strategy
  • Define success metrics and measure impact through scientific outcomes, user adoption, and model performance benchmarks
 
What we expect from you 
  • By Month 3: Develop a deep understanding of the Apheris product, the AI models in development and the scientific workflows of our pharma R&D users. Build strong relationships with internal teams and key customers. Define initial product roadmaps for both structural-based methods and ligand-based methods (focused on ADMET) model, aligned with user needs and business goals.
  • By Month 6: Deliver a validated product or model iteration into production use, with supporting documentation and measurable user impact. Establish structured feedback loops with pharma R&D users to inform product iteration and model development priorities. Work cross-functionally to ensure model evaluation, reproducibility, and deployment pipelines are aligned with user and infrastructure needs.
  • By Month 12: Own the full product strategy and execution for multiple ML-powered products in drug discovery. Demonstrate clear, sustained impact of AI products on adoption and customer value across key pharma partners. Shape the strategic planning for the AI product portfolio, helping position Apheris as a leader in federated, AI-powered drug discovery.
You should apply if
  • You have a background in computational chemistry, cheminformatics, computational biology, bioinformatics or similar
  • You have experience as a Product Manager within life sciences, where you built AI/ML products that apply ML to real-world drug discovery problems
  • You have a thorough understanding of the pharma R&D landscape, including typical workflows in areas like computational chemistry, medicinal chemistry and generally AI-based drug discovery
  • You can translate complex scientific or ML concepts into intuitive product features and communicate effectively with both technical and scientific stakeholders
  • You have experience working with ML engineers, data scientists, and biopharma domain experts to ship impactful, production-ready AI products
  • You have a track record of defining and executing product strategy in data- and model-intensive environments (e.g., involving predictive models, simulations, or structure data)
  • You are familiar with technologies such as transformer-based models, foundation models, cheminformatics/structural biology tools, or ML-based property prediction
  • You have strong collaboration skills with cross-functional teams including platform, infrastructure, and customer-facing roles
  • You are comfortable with ambiguous, fast-evolving product spaces and the ability to drive clarity and momentum from early-stage ideas
  • You have a user-first mindset with a passion for solving real-world scientific problems with elegant, practical AI solutions
Bonus points if
  • You have experience building and training transformer-based models (e.g. AlphaFold, ESMFold, OpenFold) or graph-based models using PyTorch, PyTorch Lightning, or similar frameworks
  • You understand the data challenges of structural biology 
  • You understand how structural biology and ADMET models are used in the drug discovery lifecycle and can align your work to practical use cases
  • You have experience in federated learning, privacy-preserving ML, or secure model training
  • You’ve published in top-tier ML or biology journals/conferences (e.g., NeurIPS, ICML, Nature Methods, Bioinformatics)
  • You have experience guiding product direction in a fast-paced, research-oriented environment
What we offer you
  • Industry-competitive compensation, incl. early-stage virtual share options
  • Remote-first working – work where you work best, whether from home or a co-working space near you
  • Great suite of benefits, including a wellbeing budget, mental health benefits, a work-from-home budget, a co-working stipend and a learning and development budget
  • Generous holiday allowance
  • Quarterly All Hands meet-up at our Berlin HQ or a different European location
  • A fun, diverse team of mission-driven individuals with a drive to see AI and ML used for good
  • Plenty of room to grow personally and professionally and to shape your own role
About Apheris
Apheris powers federated life sciences data networks, addressing the critical challenge of accessing proprietary data locked in silos due to IP and privacy concerns. Publicly available datasets are insufficient to train high-quality ML models that meet industry requirements. Our product addresses this by enabling life sciences organizations to collaboratively train higher quality models on complementary data from multiple parties. We are now doubling down on two key areas of interest: structural biology and ADMET. 
Logistics
Our interview process is split into three phases:
  1. Initial Screening: If your application matches our requirements, we invite you to an initial video call to explore the fit. In this 30-45 minutes interview, you will get to know us and the role. The interviewer will be interested in your relevant experiences and skills, as well as answer any question on the company and the role itself that you may have.
  2. Deep Dive: In this phase, a domain expert from our team will assess your skills and knowledge required for the role by asking you about meaningful experiences or your solutions for specific scenarios in line with the role we are staffing.
  3. Final Interview: Finally, we invite you for up to three hours of targeted sessions with our founders, talking about our culture and meeting future co-workers on the ground.
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Experience

Industry :
Computer Hardware & Networking
Spoken language(s):
English
Check out the description to know which languages are mandatory.

Hard Skills

Product ManagementProtein StructuresDrug DevelopmentData EngineeringBioinformaticsComputational ChemistryHuman-Centered DesignCheminformaticsModel ValidationProduct StrategyAderant (Software)Computational BiologyTransformer DesignPyTorch (Machine Learning Library)Advanced Distributed LearningPyTorch (Machine Learning Library)

Other Skills

  • Collaboration
  • Communication
  • Problem Solving
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