Forward-Deployed ML Engineer

🕒 July 7

🇩🇪 Germany – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 34%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.Why this score:

• Open for 128 days.
• Jobs at this company usually fill in 52 days.
• No salary listed.

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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 and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold. • Design and implement model extensions for specific tasks such as protein complex and binding affinity prediction, including data distillation, benchmarking, and evaluation pipelines. • Work with customers and academic partners to define data preprocessing, selection, and benchmarking strategies for novel training tasks involving protein structures, complexes, and multimodal biological data. • Carry out case-studies associated with the above, providing scientific and technical expertise to customers. • Involved in the full project pipeline, from scoping through to results delivery and dissemination. • Design, build, and maintain scalable machine learning models and the pipelines needed for training, inference, and deployment in production. • Collaborate cross-functionally to ensure models address real-world drug discovery needs. • Contribute to publications or open-source contributions where relevant.

🎯 Requirements

• Deep experience building and training contemporary models in production, at scale (e.g. AlphaFold, OpenFold, Boltz) • Experience applying ML to real-world protein structure or drug discovery problems • Comfortable working in a fast-paced startup environment and enjoy on customer-driven projects • Understanding the technical challenges of structural biology and can design scalable data preprocessing, training, and evaluation workflows • Nice to have: experience in federated learning, privacy-preserving ML, or privacy-preserving model training • Nice to have: published in ML or biology journals/conferences (e.g., NeurIPS, ICML, Nature Methods, Bioinformatics)

🏖️ Benefits

• Industry-competitive compensation, incl. 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 • Regular team lunches and social events • Generous holiday allowance • Quarterly All Hands meet-up at our Berlin HQ or a different European location • A fun, diverse team of mission-driven individuals with a drive to see AI and ML used for good • Plenty of room to grow personally and professionally and shape your own role

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