SeniorPrincipal ML Engineer Structural Biology
SeniorPrincipal ML Engineer Structural Biology
Posted on :
16-04-2025
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1 Vacancy
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Job Description
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 highquality 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 builtin 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 handson highimpact role focused on advancing the state of the art in applying foundational models to structural biology problems. Youll 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 transformerbased 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 missiondriven team building cuttingedge AI systems for life sciences and you know what it takes to move from foundational models to domainspecific 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 handson highimpact role focused on advancing the state of the art in applying foundational models to structural biology problems. Youll 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 transformerbased 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 missiondriven team building cuttingedge AI systems for life sciences and you know what it takes to move from foundational models to domainspecific impact this role is for you.
What you will do
- Drive the technicalapproachfor ML applications in structural biology particularly around finetuning and extending foundational models likeOpenFoldandESMFold.
- Design and implement model extensions for specific tasks such asprotein complex andbinding affinity prediction including data distillation benchmarking and evaluation pipelines.
- Work with our customers and academic partners to define data preprocessingselection and benchmarking strategies for novel training tasks involving protein structures complexes and multimodal biological data.
- Design build andmaintainscalable machine learning models and the pipelines needed for training inference and deployment in production.
- Collaborate crossfunctionally to ensure models address realworld drug discovery needs.
- Mentor and guidepeers on a content level supporting the planning and breakdown of complex structural biology modeling projects.
- Make strategic decisions on model architecture data infrastructure and model deployment.
- Contribute to publications or opensource contributions where relevant.
What we expect from you
- By month 3:Develop a deep technical understanding of theApherisproduct andhow it maps to thecurrentStructural Biology usecaseswe are working on. Take ownership of a structural biology modeling stream.Build relationships with product and engineering leadership. Start aroadmap and experiment plan for adaptinga pretrained structural biology modelto one highvalue use case.
- By month 6:Deliver the first working model extension e.g.binding affinity head) with a documented benchmarking framework and reproducible pipeline.Work with our customers and collaborating partners to understand theirdata landscape thendeliverand document reproducible data pipelinesto enable their data on the model.
- By month 12:Lead multiple ML efforts in structural biology anddemonstratemeasurable progress in model performance and realworld impact. Mentor colleagues and set strategic direction for the domain.
You should apply if
- You have deep experience building and training transformerbased models in production atscalee.g.AlphaFoldESMFoldOpenFold and are familiar with modernMLOpstooling.
- You have experience applying ML to realworld protein structure or drug discovery problems.
- You understand the technical challenges of structural biology and can design scalable data preprocessing training and evaluation workflows.
- You are comfortable setting technical direction in a startup environment and enjoy working directly with customersto understand their requirements.
Bonus points if
- You have experience in federated learning privacypreserving ML or secure model training.
- Youvepublished in toptier ML or biology journals/conferences (e.g.NeurIPS ICML Nature Methods Bioinformatics).
What we offer you
- Industrycompetitive compensation incl. earlystagevirtual share options
- Remotefirst working work where you work best whether from home or a coworking space near you
- Great suite of benefitsincluding a wellbeing budget mental health benefits a workfromhome budget a coworking stipend and a learning and development budget
- Regular team lunches and social events
- Generous holiday allowance
- Quarterly All Hands meetup at our Berlin HQor a different European location
- A fundiverse team of missiondriven 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
About Apheris
Apheris powers federated life sciences data networks addressing the critical challenge of accessing proprietary data locked in silos due to IP and privacy concerns. Publicly available datasets are insufficient to train highquality ML models that meet industry requirements. Our product addresses this by enabling life sciences organizations to collaboratively train higher quality models on complementary data from multiple parties. We are now doubling down on two key areas of interest: structural biology and ADMET.
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. In this 3045 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.
- 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.
- Final Interview: Finally we invite you for up to three hours of targeted sessions with our founders talking about our culture and meeting future coworkers on the ground.
Required Experience:
Staff IC
Employment Type
Full-Time
Key Skills
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