Director of ML Research – AI Applications

This is a fully remote position, open to applicants in Europe.

📋 Description

• Establish and lead a specialized ML Research team within AI Applications, collaborating with existing engineering teams to define the research agenda for the organization.

• Create, improve, and scale foundation models for structural biology and co-folding, tackling fundamental challenges in protein interaction modeling and drug discovery.

• Utilize extensive proprietary structural biology and biophysical datasets to enhance data pipelines and model architectures that incorporate geometric and physical priors.

• Convert developments in structural biology ML and related literature into actionable modeling strategies for practical drug discovery challenges.

• Oversee cross-functional collaboration across AISB, ADMET, engineering, product, and privacy teams, ensuring that research outputs are seamlessly integrated into production workflows.

• Engage with academic collaborators on co-folding and structural biology research, contributing to publications and presenting results at prominent conferences.

• Represent Apheris in discussions with customers and scientific forums, aiding in the resolution of high-impact modeling issues across various pharmaceutical partners.

• Build and guide a top-performing team of ML researchers and engineers over time.

⛳️ Requirements

• You possess a postgraduate degree (PhD or MSc) in Computer Science, Machine Learning, Computational Biology, or a related discipline, with over 7 years of relevant experience, including more than 3 years in a technical leadership role.

• You have significant experience applying machine learning to biological challenges, especially in structural biology (e.g., co-folding, protein modeling) or related fields like ADMET.

• You have a strong publication record in prestigious ML or computational biology conferences (e.g., NeurIPS, ICML, ICLR, ISMB, RECOMB, or equivalent).

• You possess hands-on experience with contemporary ML systems (Python, PyTorch) and have worked with or enhanced large-scale models (e.g., OpenFold, Boltz, or similar).

• You are adept at functioning as a player-coach: setting technical direction, leading teams, and actively participating in modeling and experimentation.

• You excel in cross-functional and customer-facing settings and can effectively translate vague scientific problems into clear technical solutions.

• Bonus points for experience in early-stage biotech or in developing ML systems or research functions from the ground up.

• You have experience training large models, encompassing distributed training across GPU clusters or cloud platforms such as AWS, Azure, or Lambda.

• You possess strong ML Ops and machine learning infrastructure expertise, particularly with Kubernetes-based workflows.

• You have experience developing QSAR models using classical machine learning or deep learning techniques.

• You have experience writing Triton kernels or otherwise optimizing model performance at the systems level.

• You have experience in federated learning, privacy-preserving ML, or other multi-party training environments.

🏝️ Benefits

• Competitive compensation package, including early-stage virtual stock options.

• Remote-first work environment – choose your best workspace.

• 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 another European location (3 times a year).

• A high-caliber, execution-focused team with experience from leading organizations.