ML Engineer – Large Molecules

Job not on LinkedIn

🔥 52 minutes ago

🇩🇪 Germany – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 21%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Apheris

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2019

🏥 Healthcare

💼 Consulting

📦 Logistics

Healthcare • Consulting • Logistics

Apheris is a company that specializes in enabling secure and compliant data collaboration across distributed data environments, particularly for enterprises. The company's solutions empower organizations to engage in federated machine learning and analytics, facilitating the building of models without needing to move sensitive data, thus preserving privacy and security. Apheris focuses on providing technology that allows multi-party data ecosystems and partnerships, with a strong emphasis on compliance, particularly in regulated industries like pharmaceuticals and biotech. Their technology is trusted by major pharmaceutical companies to support AI-driven drug discovery initiatives without compromising proprietary data.

📋 Description

• Build, fine-tune and extend large biomolecular models such as OpenFold, Boltz-2 and ESM for antibody modeling, co-folding, binder prediction and developability • Turn research code and prototypes into reliable components that run in federated training and evaluation pipelines • Design evaluations and benchmarks, and deliver results packages for consortium partners • Own workstreams through to release against agreed milestones, raising risks and trade-offs early • Work with product, engineering, research and consortium members to ensure model work meets real application needs • Build, train and evaluate ML systems for antibody modeling, co-folding, developability prediction and biologics discovery • Work with proprietary pharmaceutical data across federated networks • Convert research-led or open-source prototypes into models that can be evaluated, released and used in real drug discovery workflows

🎯 Requirements

• An MSc, PhD or equivalent experience in machine learning, computational biology, bioinformatics, physics or a related field • Strong Python and PyTorch • Hands-on experience training or fine-tuning deep learning models on biomolecular data • Hands-on experience with co-folding models or protein language models such as OpenFold, AlphaFold, Boltz, ESM or similar, beyond just running inference • Good evaluation habits and solid engineering practice, including fair benchmarks, reproducible experiments, and maintainable code • Experience with Kubernetes-based training, evaluation or deployment, or other MLOps and ML infrastructure tooling is nice to have • Experience with federated learning, privacy-preserving ML, or distributed and multi-GPU training is nice to have • Experience in pharma, biotech or other regulated or high-trust environments is nice to have • Publications in ML, computational biology or structural biology venues such as NeurIPS, ICML, ICLR or similar are nice to have

🏖️ Benefits

• Remote work (UTC +/- 2 hrs) • Full-time permanent employment