• AI Structural Biology (AISB) Network: Top 20 pharma companies collaborate in the field of co-folding, structure-based binding affinity predictions and antibody design.
• ADMET Network: Top 50 pharma companies and biotechs collaborate to improve small-molecule property prediction and expand to further drug modalities.
• Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability datasets for secure ML training, without data leaving each partner’s environment.

• Scope new network opportunities based on market demand and partner needs
• Validate concepts with prospective partners through structured discussions and early design collaboration
• Play an active role in business development by identifying potential partners, having introductory conversations, and securing commitments
• Set up new networks, including defining initial scope, timelines, and partner contributions

• Own execution across one or more Apheris networks, working directly with leading pharma partners to turn their data into shared, high-performance models
• Drive federated network management end-to-end, from partner data onboarding and harmonization to model aggregation and delivery of weights and inference containers in collaboration with Apheris’ technical and domain experts
• Maintain and push forward a clear execution plan across partners, spanning data readiness, training runs, and benchmarking milestones

• Define how Apheris networks evolve, including adding new endpoints, expanding to new modalities, and increasing the value delivered to partners over time
• Work with BD and product to launch new network tracks and bring additional pharma partners into the network
• Turn emerging opportunities into scoped initiatives, proposals, and execution plans

• Act as the day-to-day counterpart to partner teams, working closely with senior scientists and leaders at top pharma companies
• Lead working sessions on model performance, benchmarking results, and deployment approaches together with Apheris’ domain experts, shaping how federated models are used in practice
• Drive discussions toward clear decisions, whether on evaluation criteria, rollout strategies, or next areas of investment
• Build trusted relationships with partners while pushing conversations forward and maintaining a high bar for progress
• Identify where to push, expand scope, or activate additional stakeholders 

• Bring federated models into real-world usage by driving deployment into partner environments and integration into existing workflows
• Help partners move from evaluating models to using them in active drug discovery programs
• Track how models are used across the network and identify opportunities to expand impact across teams and programs
• Continuously improve how Apheris models are delivered and consumed in collaboration with Product, making it faster and easier for partners to realize value

• AI Structural Biology (AISB) Network: Pharmaceutical companies collaborate in the field of co-folding, structure-based binding affinity predictions and antibody design.
• ADMET Network: Pharmaceutical and biotech companies collaborate to improve small-molecule property prediction and expand to further drug modalities.
• Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability datasets for secure ML training, without data leaving each partner’s environment.

• Develop a deep understanding of the Apheris product, our current structural biology and ADMET initiatives, and the key scientific questions emerging from our networks. Define the initial research roadmap for AI Applications and begin hands-on work on the regularisation and generalisation of co-folding models. 

• Deliver initial results and customer-ready analyses for the first AI Applications workstreams, especially around co-folding model generalisation. Establish strong collaboration patterns across AISB, ADMET, engineering, privacy, and external academic partners. Clarify the capability and hiring plan for the team. 

• Lead a functioning ML Research team embedded within the broader AI Applications organisation, working across multiple initiatives at Apheris. Own a portfolio of applied research workstreams spanning co-folding and ADMET, and be recognised as a trusted technical authority in customer discussions, academic collaborations, and external scientific settings.

• You hold a postgraduate degree (PhD or MSc) in Computer Science, Machine Learning, Computational Biology, or a related field, and have 7+ years of relevant experience, including 3+ years in technical leadership. 
• You have strong experience applying machine learning to biological problems, particularly in structural biology (e.g. cofolding, protein modelling) or adjacent domains such as ADMET. 
• You have a proven publication track record in top-tier ML or computational biology venues (e.g. NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar). 
• You have hands-on experience with modern ML systems (Python, PyTorch) and have worked with or extended large-scale models (e.g. OpenFold, Boltz, or similar). 
• You are comfortable operating as a player-coach: setting technical direction, leading teams, and contributing directly to modelling and experimentation. 
• You are effective in cross-functional and customer-facing environments and can translate ambiguous scientific problems into clear technical approaches.

• You have experience in early-stage biotech or in building ML systems or research functions from scratch. 
• You have experience training large models, including distributed training across GPU clusters or cloud platforms such as AWS, Azure, or Lambda. 
• You have strong ML Ops and machine learning infrastructure experience, particularly with Kubernetes-based workflows. 
• You have experience developing QSAR models with classical machine learning or deep learning methods. 
• You have experience writing Triton kernels or otherwise optimising model performance at the systems level. 
• You have experience in federated learning, privacy-preserving ML, or other multi-party training environments.

We enable leading pharmaceutical teams to discover and develop drugs faster. We host the industry’s largest federated data networks for drug discovery AI, spanning co-folding, ADMET, and antibody developability. 

Across these networks, models are trained on proprietary industry datasets to achieve higher performance and broader applicability while keeping data control and IP protected. We deliver these superior models through drug discovery applications that enable teams to run them at scale, further customize them, and integrate them into existing R&D workflows.  

• AI Structural Biology (AISB) Network: Nine top-20 pharma companies collaborate in the field of co-folding, structure-based binding affinity predictions and antibody design.
• ADMET Network: Five top-50 pharma and biotechs collaborate to improve small-molecule property prediction and expand to further drug modalities (e.g., PROTACs).
• Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability datasets for secure ML training, without data leaving each partner’s environment.

• Support talent operations and employee experience topics, contributing to a strong employee journey from onboarding onwards, collaborating with our people lead
• Plan and execute Office Days, primarily in Berlin, as well as virtual and in-person company events, investor meetings, and customer or partner gatherings, such as meetups alongside conferences
• Own general office and admin topics, including the relationship with our co-working space, office supplies, incoming mail, supplier communication, and company merch 
• Support employees with travel bookings and relatedlogistics
• Coordinate with vendors and suppliers, manage relationships, and ensure smooth delivery of services 
• Maintain electronic and paper records, ensuring information and documents are organized, filed correctly, and easily accessible 
• Support the rollout and maintenance of company-wide operational processes and policies, and help the team follow them 
• Proactively identify and implement improvements to day-to-day operations

Executive Assistant to the CEO (~30%)

• Act as reliable point of contact and ensure that the CEO’s time is protected & used effectively, collaborating with the Chief of Staff on prioritization as needed
• Organize & coordinate appointments, meetings &business travel across time zones while ensuring seamless day-to-day support
• Support prioritization & coordination of incoming requests in alignment with internal & external stakeholders, handling correspondence where appropriate
• Support the Leadership Team with logistics when needed

We enable leading pharmaceutical teams to discover and develop drugs faster. We host the industry’s largest federated data networks for drug discovery AI, spanning co-folding, ADMET, and antibody developability. 

Across these networks, models are trained on proprietary industry datasets to achieve higher performance and broader applicability while keeping data control and IP protected. We deliver these superior models through drug discovery applications that enable teams to run them at scale, further customize them, and integrate them into existing R&D workflows.  

• AI Structural Biology (AISB) Network: Nine top-20 pharma companies collaborate in the field of co-folding, structure-based binding affinity predictions and antibody design.
• ADMET Network: Five top-50 pharma and biotechs collaborate to improve small-molecule property prediction and expand to further drug modalities (e.g., PROTACs).
• Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability datasets for secure ML training, without data leaving each partner’s environment.

We enable leading pharmaceutical teams to discover and develop drugs faster. We host the industry’s largest federated data networks for drug discovery AI, spanning co-folding, ADMET, and antibody developability.

Across these networks, models are trained on proprietary industry datasets to achieve higher performance and broader applicability while keeping data control and IP protected. We deliver these superior models through drug discovery applications that enable teams to run them at scale, further customize them, and integrate them into existing R&D workflows. 

• AI Structural Biology (AISB) Network: Eight top-20 pharma companies collaborate in the field of co-folding, structure-based binding affinity predictions and antibody design.
• ADMET Network: Five top-50 pharma and biotechs collaborate to improve small-molecule property prediction and expand to further drug modalities (e.g., PROTACs).
• Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability datasets for secure ML training, without data leaving each partner’s environment.

You will serve as the CTO’s proxy across customer preparation, technical follow-ups, and internal coordination. You will operate with significant autonomy and discretion from day one. You will be expected to anticipate issues, surface trade-offs, and keep the organization focused and moving at pace. This role is designed for someone with a strong technical foundation who thrives in fast-paced environments, enjoys turning ambiguity into concrete outputs, and is comfortable operating in front of customers.

• AI Structural Biology (AISB) Network: Pharmaceutical companies collaborate in the field of co-folding, structure-based binding affinity predictions and antibody design.
• ADMET Network: Pharmaceutical and biotech companies collaborate to improve small-molecule property prediction and expand to further drug modalities.
• Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability datasets for secure ML training, without data leaving each partner’s environment.

• You have a PhD, MSc, or equivalent experience in a relevant field, plus 5+ years applying ML to complex scientific or biological problems, ideally in structural biology, protein modeling, co-folding, or binding prediction.
• You have hands-on experience with modern ML systems in Python and PyTorch, and have worked with or extended large-scale models such as OpenFold, AlphaFold, Boltz, ESM, or similar.
• You have MLOps or ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows.
• You can define success criteria,validate model quality, and ensure ML releases are robust enough for real-world use.
• You have led delivery of complex ML projects, including setting technical direction, managing risks and dependencies, and driving teams toward high-quality releases.
• You are comfortable operating as a player-coach: mentoring engineers and ML scientists while contributing directly to modeling, experimentation, or architecture when needed.
• You can work effectively with product, research, leadership, customers, and scientific stakeholders to turn ambiguous requirements into clear technical plans.

• You have experience with federated learning, privacy-preserving ML, distributed training, or other multi-party training environments.
• You have experience with Go or other systems programming languages.
• You have worked on production-grade model delivery in regulated, enterprise, pharmaceutical, biotech, or other high-trust environments.
• You have a publication record in top-tier ML, computational biology, or structural biology venues such as NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar.
  

We enable leading pharmaceutical teams to discover and develop drugs faster. We host the industry’s largest federated data networks for drug discovery AI, spanning co-folding, ADMET, and antibody developability. 

Across these networks, models are trained on proprietary industry datasets to achieve higher performance and broader applicability while keeping data control and IP protected. We deliver these superior models through drug discovery applications that enable teams to run them at scale, further customize them, and integrate them into existing R&D workflows.  

• AI Structural Biology (AISB) Network:Pharmaceuticalcompanies collaborate in the field of co-folding, structure-based binding affinitypredictionsand antibody design.
• ADMET Network:Pharmaceutical and biotech companiescollaborate to improve small-molecule property prediction and expand to further drug modalities.
• Antibody Developability Network:Pharma partners collaborate to federate historical and purpose-built antibody developability datasets for secure ML training, without data leaving each partner’s environment.

You will turn ambitious scientific goals into reliable model systems that can be evaluated, released, and used in real drug discovery workflows. 
 
You will set technical direction, drive execution, challenge modeling decisions, and turn ambiguity into executable plans, while managing risks and dependencies, mentoring senior engineers and ML scientists, and getting into technical depth when needed. 
 
We are looking for someone who has led demanding ML delivery before and knows how to move from research-led or open-source prototypes to robust model systems. 

• You have a PhD, MSc, or equivalent experience in a relevant field, plus 5+ years applying ML to complex scientific or biological problems, ideally instructural biology, antibody engineering, biologics discovery, developability prediction, binderpredictionorprotein design.