Apheris

• Own cloud and application security, including AWS security architecture, IAM, network security, and secure configuration management • Drive and continually improve application security practices, including secure coding guidance, threat modeling support, and automated security testing in the SDLC • Lead the incident response program, including playbook development, on-call readiness, threat detection, and response coordination • Manage vulnerability management processes, ensuring risks are identified, triaged, and remediated effectively with engineering teams • Maintain and evolve security tooling, including monitoring, logging, SIEM/alerting, and secrets management • Collaborate with engineering and platform teams to embed security considerations into design and architectural decisions • Contribute to the unified control framework, ensuring strong security foundations for ISO 27001 and SOC 2 • Own corporate and IT security, including endpoint management (e.g., MDM), identity and access management, and oversight of the external IT provider • Lead security reviews by our customers, acting as a confident, trusted partner to enterprise clients throughout their evaluation process • Stay ahead of emerging threats, technologies, and best practices to continuously uplift Apheris’ security posture

Role Description We are looking for a hands-on Security Lead with strong technical depth to define, operationalize, and scale Apheris’ security capabilities. You will own cloud and application security, incident response, IT and corporate security, and security tooling across our environment. You will collaborate closely with engineering, product, and leadership, ensuring that security is embedded throughout our systems, operations, and development processes. You will maintain and evolve the unified security control framework, build scalable security processes, and lead efforts to detect, respond to, and remediate threats. Your focus is on anticipating risks, enabling teams, and ensuring that security is a natural part of day-to-day work. If you thrive at the intersection of technical depth, operational excellence, and cross-functional collaboration, while also influencing architecture, processes, and culture, this role is for you. What you will do - Own cloud and application security, including AWS security architecture, IAM, network security, and secure configuration management. - Drive and continually improve application security practices, including secure coding guidance, threat modeling support, and automated security testing in the SDLC. - Lead the incident response program, including playbook development, on-call readiness, threat detection, and response coordination. - Manage vulnerability management processes, ensuring risks are identified, triaged, and remediated effectively with engineering teams. - Maintain and evolve security tooling, including monitoring, logging, SIEM/alerting, and secrets management. - Collaborate with engineering and platform teams to embed security considerations into design and architectural decisions. - Contribute to the unified control framework, ensuring strong security foundations for ISO 27001 and SOC 2. - Own corporate and IT security, including endpoint management (e.g., MDM), identity and access management, and oversight of the external IT provider. - Lead security reviews by our customers, acting as a confident, trusted partner to enterprise clients throughout their evaluation process. - Stay ahead of emerging threats, technologies, and best practices to continuously uplift Apheris’ security posture. Qualifications - A degree in computer science, engineering, or equivalent hands-on experience in technical roles. - 5+ years of experience in security engineering, cloud security, application security, or similar technical security roles. - Hands-on expertise with AWS security architecture, identity and access management, network security, and modern DevOps practices. - Experience implementing or supporting secure development lifecycle (SDLC) practices, collaborating closely with engineering. - Strong understanding of modern authentication, authorization, secrets management, and infrastructure-as-code security. - Demonstrated experience handling security incidents, vulnerability management, or threat detection. - Demonstrated experience with large-enterprise security and compliance expectations, including how major corporates conduct security reviews and vendor due-diligence processes. - Experience maintaining compliance programs such as ISO 27001 or SOC 2. - Ability to build strong relationships across engineering and influence secure design decisions. - A pragmatic, solution-oriented mindset that balances security, usability, and speed. - Experience mentoring team members and helping them grow their security skills. Nice to have - Experience acting as a Data Protection Officer or supporting regulation like GDPR. - Experience leading and developing teams, with the ability to mentor others and scale a security function in a fast-growing environment. - Understanding of pharma deployment environments and integrations with common R&D platforms (e.g., Schrödinger Live Design, Benchling). - Experience working in B2B SaaS environments, particularly with AI-powered or data-intensive products, and an understanding of the security considerations that come with them. - Experience working directly with external partners, customers, and users in fast-moving, high-stakes projects. What we offer you - Industry-competitive compensation, including early-stage virtual share options. - Remote-first working – work where you work best. - Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget. - Generous holiday allowance. - Office Days at our Berlin HQ or a different European location (3x per year). - A high-caliber, execution-focused team with experience from leading organizations.

Role Description We are looking for a Forward-Deployed Cheminformatician to own how binding data is prepared across our co-folding focused networks and initiatives. Binding data is the input that decides whether our co-folding and binding-affinity models perform in real drug programs. It arrives from pharma partners in heterogeneous shapes: - Different assay registries - Different metadata - Different chemical-representation standards - Different choices on qualifiers, replicates, and censoring We need someone who turns this into a repeatable, well-documented preparation pipeline that pharma representatives can run alongside us, and that scales to the public-data corpus we build for our own model training. This is half engineering, half forward-deployed work. You will: - Define the protocol, harden it with validators and scripts, integrate it into the Apheris products, run it with each new partner, and own the equivalent pipeline for the public binding-data corpus. Qualifications - BSc, MSc, PhD or equivalent in cheminformatics, computational chemistry, or a related field - 3+ years preparing biological assay data in a discovery setting - Fluency in Python and RDKit - Hands-on experience curating quantitative binding assay data (KD, Ki, IC50, pIC50) and HTS data - Good engineering code writing skills (version control, tested modular scripts, validators) - Comfortable forward-deployed with pharma medicinal chemists and biologists - Enjoy turning a messy ad-hoc cleaning job into a repeatable protocol Requirements - SMILES normalization, tautomer/ionization/stereochemistry handling, and scaffold extraction are second nature - Understanding of activity cliffs and model training - Ability to sit in a sense-check meeting and pull out what is actually meant by a column label Benefits - Industry-competitive compensation, including early-stage virtual share options - Remote-first work — work where you work best - Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget - Generous holiday allowance - Office Days at our Berlin HQ or a different European location (3x per year) - A high-calibre, execution-focused team with experience from leading organizations

• Lead teams building and delivering federated large molecule AI systems, staying hands-on across antibody modeling, co-folding, binder prediction, and developability • Build and implement ML applications large biomolecular foundation models such as OpenFold, Boltz-2 and ESM • Own delivery of these against committed milestones and ensure high-quality model releases ship on time • Translate ambiguous scientific and technical goals into clear plans, priorities, workstreams, and decisions • Guide evaluation decisions and build on them to deliver results packages to external stakeholders • Surface risks, blockers, bugs, timeline changes, and technical trade-offs early, with clear recommendations • Align consortium members on objectives, evaluation criteria, data requirements, timelines, and delivery expectations • Work with product, engineering, research, and leadership to ensure application requirements shape the model roadmap.

Role Description We are looking for a technical lead to own delivery of our large molecule AI model programs. This is a hands-on leadership role at the intersection of foundation models, structural biology, protein engineering, and federated learning. You will lead teams building and operationalizing large-scale ML systems for: - Antibody modeling - Co-folding - Developability prediction - Biologics discovery You will turn ambitious scientific goals into reliable model systems that can be evaluated, released, and used in real drug discovery workflows. Your responsibilities include: - Setting technical direction and driving execution - Challenging modeling decisions and turning ambiguity into executable plans - Managing risks and dependencies - Mentoring senior engineers and ML scientists - Diving 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. Qualifications - PhD, MSc, or equivalent experience in a relevant field - 5+ years applying ML to complex scientific or biological problems - Experience in structural biology, antibody engineering, biologics discovery, developability prediction, binder prediction, or protein design - Hands-on experience with modern ML systems in Python and PyTorch - Experience with large-scale models such as OpenFold, AlphaFold, Boltz, ESM, or similar Requirements - MLOps or ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows - Ability to define success criteria and validate model quality - Experience leading delivery of complex ML projects - Comfortable operating as a player-coach, mentoring while contributing directly to modeling, experimentation, or architecture - Ability to work effectively with product, research, leadership, customers, and scientific stakeholders Nice to have - Experience with federated learning, privacy-preserving ML, distributed training, or other multi-party training environments - Experience on production-grade model delivery in regulated, enterprise, pharmaceutical, biotech, or other high-trust environments - Publication record in top-tier ML, computational biology, or structural biology venues such as NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar Benefits - Industry-competitive compensation, including early-stage virtual share options - Remote-first working – work where you work best - Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget - Generous holiday allowance - Office Days at our Berlin HQ or a different European location (3x per year) - A high-calibre, execution-focused team with experience from leading organizations

• 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 related logistics • 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 • 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

• Lead teams building and delivering federated co-folding models • Build and implement ML applications in structural biology • Own delivery against committed milestones ensuring high-quality model releases • Translate ambiguous scientific and technical goals into clear plans, priorities, and decisions • Guide evaluation decisions and build deliverables for external stakeholders • Surface risks, blockers, and technical trade-offs with clear recommendations • Align consortium members on objectives, evaluation criteria, and delivery expectations

Role Description We are looking for a technical lead to own delivery of our AI Structural Biology model programs. This is a hands-on leadership role at the intersection of foundation models, structural biology, and federated learning. You will turn ambitious scientific goals into reliable model systems that can be evaluated, released, and used in real drug discovery workflows. - Set technical direction, drive execution, and challenge modeling decisions. - Turn ambiguity into executable plans while managing risks and dependencies. - Mentor senior engineers and ML scientists, getting into technical depth when needed. - Lead teams building and delivering federated co-folding models. - Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2, and ESMFold. - Own delivery against committed milestones and ensure high-quality model releases ship on time. - Translate ambiguous scientific and technical goals into clear plans, priorities, workstreams, and decisions. - Guide evaluation decisions and build on them to deliver results packages to external stakeholders. - Surface risks, blockers, bugs, timeline changes, and technical trade-offs early, with clear recommendations. - Align consortium members on objectives, evaluation criteria, data requirements, timelines, and delivery expectations. - Work with product, engineering, research, and leadership to ensure application requirements shape the model roadmap. Qualifications - PhD, MSc, or equivalent experience in a relevant field. - 5+ years applying ML to complex scientific or biological problems, ideally in structural biology, protein modeling, co-folding, or binding prediction. - Hands-on experience with modern ML systems in Python and PyTorch. - Experience with or extended large-scale models such as OpenFold, AlphaFold, Boltz, ESM, or similar. - MLOps or ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows. - Experience leading delivery of complex ML projects, including setting technical direction and managing risks and dependencies. - Comfortable operating as a player-coach: mentoring engineers and ML scientists while contributing directly to modeling, experimentation, or architecture when needed. - Ability to work effectively with product, research, leadership, customers, and scientific stakeholders to turn ambiguous requirements into clear technical plans. Requirements - Experience with federated learning, privacy-preserving ML, distributed training, or other multi-party training environments (bonus). - Experience with Go or other systems programming languages (bonus). - Experience on production-grade model delivery in regulated, enterprise, pharmaceutical, biotech, or other high-trust environments (bonus). - Publication record in top-tier ML, computational biology, or structural biology venues such as NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar (bonus). Benefits - Industry-competitive compensation, including early-stage virtual share options. - Remote-first working – work where you work best. - Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget. - Generous holiday allowance. - Office Days at our Berlin HQ or a different European location (3x per year). - A high-calibre, execution-focused team with experience from leading organizations.

• Set up and lead the dedicated ML Research team within AI Applications, working alongside existing engineering teams and establishing the research mandate for the organisation. • Design, enhance, and train foundation models at scale for structural biology and co-folding, addressing core challenges in protein interaction modelling and drug discovery. • Leverage large-scale proprietary structural biology and biophysical datasets to develop improved data pipelines and model architectures that capture geometric and physical priors. • Translate advances in structural biology ML and adjacent literature into practical modelling approaches for real-world drug discovery problems. • Lead cross-functional delivery across AISB, ADMET, engineering, product, and privacy teams, ensuring research outputs integrate into production workflows. • Collaborate with academic partners on co-folding and structural biology research, contributing to publications and presenting findings at leading conferences. • Represent Apheris in customer discussions and scientific forums, and help solve high-impact modelling problems across multiple pharma partners. • Build and mentor a high-performing team of ML researchers and engineers over time.

• Drive execution and growth across one or more of Apheris’ federated networks • Build new federated networks and scope new opportunities based on market demand and partner needs • Validate concepts with prospective partners through structured discussions and early design collaboration • Own and drive federated network execution across one or more Apheris networks • 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 and increasing the value delivered to partners over time • Act as the day-to-day counterpart to partner teams, working closely with senior scientists and leaders at top pharma companies • Help partners move from evaluating models to using them in active drug discovery programs