Machine Learning Engineer – Small Molecules

Remote (UTC +/- 2 hrs)

Full-time

Permanent employee

About Apheris

At Apheris, we are building the future of how AI is applied in pharmaceutical R&D.

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: 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.

About the role

We're looking for a Machine Learning Engineer to help drive the technical execution of our cofolding and structural-biology model programs.

This is a hands-on role at the intersection of foundation models, structural biology, and federated learning. You'll work closely with our technical lead and leadership team, turning ambitious scientific goals into model systems that can be evaluated, released, and used in real drug-discovery workflows.

You should bring practical experience working with contemporary models for protein structure and molecular complex prediction, and be comfortable moving from research-led or open-source prototypes to production-grade model systems.

What you will do

Fine-tune and extend foundation models for biomolecular cofolding, complex structure prediction and related drug-discovery tasks.
Own the applied modelling lifecycle, from data preparation and experimental design through benchmarking, evaluation and delivery.
Work with customers and academic partners to translate scientific questions and proprietary data into concrete modelling projects.
Build reliable, scalable pipelines for model training, inference and deployment, including in federated or multi-party environments.
Collaborate across research, product and engineering, communicating model limitations, risks and technical trade-offs clearly.

What we expect from you

You should apply if:

You have strong hands-on experience building and training modern ML systems in Python and PyTorch.
You have worked with protein structure, cofolding or related structural-biology models such as Boltz, OpenFold, AlphaFold-derived or similar systems.
You understand structural-biology data and can design sound preprocessing, training and evaluation workflows.
You can take a research or open-source prototype through to a reliable model system used by scientists or customers.
You can own ambiguous technical problems and communicate effectively with scientific, product and engineering stakeholders.

Bonus points if:

Experience with federated learning, privacy-preserving ML or distributed training.
Experience delivering ML systems in pharmaceutical, biotech or other high-trust environments.
Relevant publications or open-source contributions.

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-calibre, execution-focused team with experience from leading organizations