About the role
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 Principal ML Engineer to lead the technical direction 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 modeling, architecture, and experimentation in this domain. While this is not a people management role, you will guide and mentor other engineers and researchers on a content level.
You should bring deep expertise in training and deploying transformer-based 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.
As we are doubling down on structural biology use cases as a focus area within our drug discovery work, we are looking for a Principal ML Engineer to lead the technical direction 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 modeling, architecture, and experimentation in this domain. While this is not a people management role, you will guide and mentor other engineers and researchers on a content level.
You should bring deep expertise in training and deploying transformer-based 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.
