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Governed, private, secure data access for ML and analytics
Forward-Deployed ML Engineer
Location
Worldwide
Posted
82 days ago
Salary
0
Seniority
Mid Level
No structured requirement data.
Job Description
Forward-Deployed ML Engineer
Apheris
Role Description At Apheris, we power federated data networks in life sciences to address the data bottleneck in training highly performant ML models. Our product enables biopharma organizations to collaboratively train higher quality models on their combined data. The Apheris product is a set of drug discovery applications enriched with the proprietary data of network participants. Our federated computing infrastructure with built-in governance and privacy controls ensures that the data IP and ownership always stays with the data custodians. We are looking for a Senior ML Engineer to drive the technical execution for our structural biology models. This is a hands-on, high-impact role focused on: - Advancing the state of the art in applying foundational models to structural biology problems. - Working closely with our leadership team as the technical authority on ML modelling, architecture, and experimentation. What you will do - 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. - 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. What we expect from you - By month 3: Develop a deep technical understanding of the Apheris product and contribute to delivery of at least one customer-driven cofolding project. - By month 6: Build a customer-ready package for results analysis and drive adoption of Apheris-generated models with customers. - By month 12: Take ownership of a customer-driven cofolding model development stream and drive product requirements. You should apply if - You have deep experience building and training contemporary models in production, at scale (e.g., AlphaFold, OpenFold, Boltz) and are familiar with modern MLOps tooling. - You have experience applying ML to real-world protein structure or drug discovery problems. - You are comfortable working in a fast-paced startup environment and enjoy customer-driven projects. - You understand the technical challenges of structural biology and can design scalable data preprocessing, training, and evaluation workflows. Nice to have - You have experience in federated learning, privacy-preserving ML, or privacy-preserving model training. - You’ve published in ML or biology journals/conferences (e.g., NeurIPS, ICML, Nature Methods, Bioinformatics). Benefits - 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. - 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. Logistics Our interview process is split into three phases: - Initial Screening: If your application matches our requirements, we invite you to an initial video call to explore the fit. - Deep Dive: A domain expert will assess your skills and knowledge required for the role. - Final Interview: Up to three hours of targeted sessions with our founders, discussing our culture and meeting future co-workers.
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