Senior ML Research Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 73d ago
The date the source published, not the day we noticed it (2026-08-21). Last seen at its source 5h ago.
We have tracked this listing since 27 Oct 2026 (6 days). The employer's own board has carried it every time we have read it, most recently 5 hours ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Not stated
The description states no restriction of its own. This is the source's own tag.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Personio employer's own board first seen 6d ago · last seen 5h ago
The listing
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 into further drug modalities.
- Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability data sets for secure ML training, without data leaving each partner’s environment.
About the role
We are looking for a Senior Machine Learning Research Engineer to help drive the research and development of machine learning models for molecular and structural biology.
This is a hands-on role at the intersection of foundation models, structural biology, and federated learning. You'll execute research projects, turning ambitious scientific goals into frontier ML models that can be evaluated, released, and used in real drug-discovery workflows.
About you
What you will do
- Develop and improve ML models in molecular and structural biology, such as co-folding and binding affinity models, for drug design applications and workflows, driving them from ideation through prototyping iterations to robust tooling.
- Build effective benchmarking and evaluation strategies for model evaluation and iterate and refine existing modelling approaches based on data-driven insights.
- Diagnose and resolve data quality and pipeline issues that affect model quality.
- Stay up to date with a rapidly evolving research literature and identify best public approaches to aid in research and development.
- Collaborate with customers, partner-facing engineers, and external collaborators to support real-world drug design use cases.
What we expect from you
- You have a PhD or MSc in machine learning, computational biology, computational chemistry, bioinformatics, physics, or a related field, with at least 2 years of professional experience applying ML to scientific problems.
- You have hands-on experience training, fine-tuning and extending deep learning models for molecular or protein structure modelling.
- You are able to rigorously interrogate ML models, their training, and scientific benchmarks, and translate insights into impactful improvements.
- You are an expert in Python and PyTorch, can produce reliable and clean code, and are comfortable with multi-GPU and distributed training.
- You have deep familiarity with structural biology and protein–ligand data formats, quality metrics and tooling.
- You proactively identify opportunities to contribute scientifically in order to impact the organizational goals.
Nice to have
- You have experience in federated learning, privacy-preserving ML, or secure model training.
- You have experience developing ML models for drug design in pharmaceutical or biotech environments.
- You have published at top-tier ML or structural biology venues or have contributed to open-source projects in that space.
What we offer you
- Industry-competitive compensation, including early-stage virtual share options
- Remote-first working – work where you work best, whether from home or a co-working space near you
- Great suite of benefits, including a wellbeing budget, mental health benefits, a work-from-home budget, a co-working stipend and a learning and development budget
- Generous holiday allowance
- Office Days at our Berlin HQ or a different European location (3x a year)
- A fun, diverse team of mission-driven individuals with experience across leading organizations and a drive to see AI and ML used for good
Logistics
Our mission statement