Senior ML Research Engineer - Drug Discovery (Remote, Equity)

Apheris; Brussel; Remote; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Remote-first

Wellbeing budget

Mental health benefits

Work-from-home budget

Co-working stipend

Learning and development budget

Generous holiday allowance

Office days in EU locations

Job summary

Apheris is hiring a Senior Machine Learning Research Engineer to advance ML models for molecular and structural biology. This hands‑on role combines foundation models, structural biology, and federated learning to turn ambitious scientific goals into usable drug‑discovery ML tooling.

You will collaborate with customers and partners, staying current with the literature and applying best public approaches to improve model quality and real‑world applicability.

Qualifications

• PhD or MSc in a relevant field with research experience.
• Hands-on experience training and extending DL models for molecular/protein structure modelling.
• Ability to interrogate models, training, and benchmarks to drive improvements.

Responsibilities

• Develop and improve ML models for molecular/structural biology, incl. co-folding and binding affinity modelling.
• Build benchmarking and evaluation strategies; iterate based on data-driven insights.
• Diagnose data quality and pipeline issues affecting model quality.
• Stay updated with literature and identify public approaches for research.
• Collaborate with customers and external collaborators to support drug design use cases.

Skills

ML model development

Python

PyTorch

Distributed training

Structure-based modelling

Data quality analysis

Education

PhD or MSc in ML, computational biology, computational chemistry, bioinformatics, physics

Tools

PyTorch

Multi-GPU frameworks

Job description

Apheris is hiring a Senior Machine Learning Research Engineer to advance ML models for molecular and structural biology. This hands‑on role combines foundation models, structural biology, and federated learning to turn ambitious scientific goals into usable drug‑discovery ML tooling.

You will collaborate with customers and partners, staying current with the literature and applying best public approaches to improve model quality and real‑world applicability.