Forward-Deployed ML Engineer – Cofolding

​​​​​​​​Apheris powers federated life sciences data networks, addressing the critical challenge of accessing proprietary data locked in silos due to IP and privacy concerns. 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 develop ability. 

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 Develop ability Network: Pharma partners collaborate to federate historical and purpose-built antibody develop ability data sets for secure ML training, without data leaving each partner’s environment.

At Apheris, we power federated data network in life sciences to address the data bottleneck in training highly performant ML models. Publicly available, molecular datasets are insufficient to train high-quality ML models that meet industry requirements. Our product addresses this by hosting networks where biopharma organizations 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 ensure that the data IP and ownership always stays with the data custodians.

As we are doubling down on structural biology use cases as a focus area within our drug discovery work, 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. You’ll work closely with our leadership team and will serve as the technical authority on ML modelling, architecture, and experimentation in this domain.

You should bring deep expertise in training and deploying contemporary models for protein structure prediction and related tasks. You must also understand the application of these models in drug discovery workflows and have a track record of setting strategy, breaking down complex technical problems, and delivering impactful ML systems. 

If you want to be part of a mission-driven team building cutting-edge AI systems for life sciences – and you know what it takes to move from foundational models to domain-specific impact – this role is for you.

Our interview process is split into three phases:

1. Initial Screening: If your application matches our requirements, we invite you to an initial video call to explore the fit. In this 30-45 minutes interview, you will get to know us and the role. The interviewer will be interested in your relevant experiences and skills, as well as answer any question on the company and the role itself that you may have.
2. Deep Dive: In this phase, a domain expert from our team will assess your skills and knowledge required for the role by asking you about meaningful experiences or your solutions for specific scenarios in line with the role we are staffing.
3. Final Interview: Finally, we invite you for up to three hours of targeted sessions with our founders, talking about our culture and meeting future co-workers on the ground.