Job Closed
This listing is no longer active.
Governed, private, secure data access for ML and analytics
Director of ML Research – AI Applications
Location
Europe
Posted
77 days ago
Salary
0
Seniority
Lead
Job Description
Director of ML Research – AI Applications
Apheris
• 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.
Job Requirements
- 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.
- Bonus points if 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.
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
Related Guides
Related Categories
Related Job Pages
More Artificial Intelligence Jobs
• Develop a bespoke performance management framework tailored for a fast-paced AI startup (including performance reviews, 360-degree feedback, and competency mapping). • Implement AI-driven tools to analyze sentiment in feedback and identify patterns of high performance or potential burnout. • Move away from annual reviews to a "Continuous Performance Management" model with real-time feedback and regular check-ins. • Use AI to map current team skills against industry benchmarks and predict future talent needs. • Establish leading indicators of performance using data from Slack, Jira, or CRM rather than just lagging indicators. • Ensure the new system seamlessly integrates with our existing OKR framework. • Conduct workshops and create guides for leads on how to give/receive feedback and conduct meaningful 1-on-1s.
• Develop a bespoke performance management framework tailored for a fast-paced AI startup (including performance reviews, 360-degree feedback, and competency mapping). • Implement AI-driven tools to analyze sentiment in feedback and identify patterns of high performance or potential burnout. • Move away from annual reviews to a "Continuous Performance Management" model with real-time feedback and regular check-ins. • Use AI to map current team skills against industry benchmarks and predict future talent needs. • Establish leading indicators of performance using data from Slack, Jira, or CRM rather than just lagging indicators. • Ensure the new system seamlessly integrates with our existing OKR framework. • Conduct workshops and create guides for leads on how to give/receive feedback and conduct meaningful 1-on-1s.
• Develop a bespoke performance management framework tailored for a fast-paced AI startup (including performance reviews, 360-degree feedback, and competency mapping). • Implement AI-driven tools to analyze sentiment in feedback and identify patterns of high performance or potential burnout. • Move away from annual reviews to a "Continuous Performance Management" model with real-time feedback and regular check-ins. • Use AI to map current team skills against industry benchmarks and predict future talent needs. • Establish leading indicators of performance using data from Slack, Jira, or CRM rather than just lagging indicators. • Ensure the new system seamlessly integrates with our existing OKR framework. • Conduct workshops and create guides for leads on how to give/receive feedback and conduct meaningful 1-on-1s.
Practice Operations – Artificial Intelligence
CBIZTrusted local advisors enhanced by specialists nationwide. (NYSE: CBZ)
• Support the preparation, formatting, and assembly of proposals, RFP responses, presentations, and supporting sales materials. • Coordinate and manage the end-to-end RFP process, including intake, qualification, calendaring, compliance tracking, kickoff coordination, response collection, review cycles, and final submission support. • Help organize response plans, responsibilities, review cycles, and deadlines to keep proposal and RFP pursuits on track. • Maintain RFP schedules, requirements matrices, content trackers, and submission checklists to improve consistency, responsiveness, and quality control. • Coordinate content collection from internal stakeholders and help organize proposal inputs, deadlines, and version control. • Edit and proofread content to ensure clarity, consistency, accuracy, and adherence to brand standards. • Maintain proposal content libraries, templates, resumes, case studies, and standard response materials. • Assist with scheduling meetings, tracking deliverables, and monitoring proposal milestones. • Collaborate with sales, marketing, and solution teams to help produce polished, client-ready deliverables. • Support administrative and logistical tasks related to pursuits, client presentations, and business development initiatives. • Use applicable technology tools to manage documents, shared files, and production workflows efficiently.
