Role overview

Qualifications

PhD, MSc, or equivalent with 5+ years applying ML to complex scientific or biological problems, ideally in structural biology, protein modeling, co-folding, or binding prediction
Hands-on experience with modern ML systems (Python, PyTorch) and with large-scale models such as OpenFold, AlphaFold, Boltz, or ESMFold; plus MLOps/ML infrastructure experience (Kubernetes-based training, evaluation, deployment)
Proven track record delivering complex ML projects: setting technical direction, managing risks and dependencies, driving high-quality releases; comfortable as a player-coach mentoring engineers and ML scientists while contributing directly to modeling or architecture
Ability to translate ambiguous scientific/technical goals into executable plans and work effectively with product, research, leadership, customers, and scientific stakeholders; surface risks and trade-offs early

Responsibilities

Lead the teams building and delivering federated co-folding models, staying hands-on across modeling, architecture, evaluation, and engineering execution
Build and implement ML applications in structural biology, focusing on fine-tuning and extending foundational models like OpenFold, Boltz-2, and ESMFold
Own delivery against committed milestones and ensure high-quality model releases ship on time; translate ambiguous goals into clear plans and guide evaluation decisions; package results for external stakeholders
Surface risks, blockers, timeline changes, and technical trade-offs early with clear recommendations; align consortium members on objectives, data requirements, timelines, and delivery expectations; collaborate across product, engineering, research, and leadership to shape the model roadmap

Key facts

Remote from: Europe
Full time
Senior (5-10 years)
English

Hard skills

Python (Programming Language) Release Builds Controlled Experiments Model Based Systems Engineering Model Validation Machine Learning PyTorch (Machine Learning Library) System Programming Language Risk Mitigation Project Management Stakeholder Communications Mentoring Youth Technical Leadership

Other skills

Decision Making
Communication
Teamwork

About the company

Apheris

Artificial Intelligence & Machine Learning Services

Apheris is on a mission to collaboratively solve the largest problems our planet is facing by enabling organizations to securely build and deploy data applications and AI across boundaries. Our product enables governed, private, secure data access for ML and analytics. As model architectures become increasingly commoditized, data becomes an organization’s key differentiator. However, businesses need to safeguard their data assets and IP while leveraging it for ML. The Apheris Compute Gateway ensures only approved computations can be launched on that data and enables federation across organizational boundaries, allowing ML-powered insights with no need to centralize data, while ensuring compliance with data privacy, security, and governance obligations. Founded in 2019, Apheris is backed by top investors and tech industry innovators, including Octopus Ventures, Heal Capital, LocalGlobe, another.vc, MuleSoft founder Ross Mason (Dig Ventures), and Twitter board chairman and former CFO of Google, Patrick Pichette.

Company details

Company typeStartup

IndustryArtificial Intelligence & Machine Learning Services

Company size11 - 50

Links

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Job description

About Apheris

At Apheris, we are building the future of how AI is applied in pharmaceutical R&D.

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 developability.

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 to further drug modalities.
Antibody Developability Network: Pharma partners collaborate to federate historical and purpose-built antibody developability datasets for secure ML training, without data leaving each partner’s environment.

About the role

We are looking for a technical lead to own delivery of our AI Structural Biology model programs.

This is a hands-on leadership role at the intersection of foundation models, structural biology, and federated learning. You will turn ambitious scientific goals into reliable model systems that can be evaluated, released, and used in real drug discovery workflows.

You will set technical direction, drive execution, challenge modeling decisions, and turn ambiguity into executable plans, while managing risks and dependencies, mentoring senior engineers and ML scientists, and getting into technical depth when needed.

We are looking for someone who has led demanding ML delivery before and knows how to move from research-led or open-source prototypes to robust model systems.

What you will do

Lead the teams building and deliveringfederated co-foldingmodels, staying hands-on acrossmodeling, architecture, evaluation, and engineering execution.
Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2 and ESMFold.Own deliveryof theseagainst committed milestones and ensure high-quality model releases ship on time.
Translate ambiguous scientific and technical goals into clear plans, priorities, workstreams, and decisions.Guide evaluation decisionsand build on them to deliverresultspackages to external stakeholders.
Surface risks, blockers, bugs, timeline changes, and technical trade-offs early, with clear recommendations.
Align consortium members onobjectives, evaluation criteria, data requirements, timelines, and delivery expectations.
Work with product, engineering, research, and leadership to ensure application requirements shape the model roadmap.

What we expect from you

You should apply if:

You have a PhD, MSc, or equivalent experience in a relevant field, plus 5+ years applying ML to complex scientific or biological problems, ideally in structural biology, proteinmodeling, co-folding, or binding prediction.
You have hands-on experience with modern ML systems in Python andPyTorch, and have worked with or extended large-scale models such asOpenFold, AlphaFold, Boltz, ESM, or similar.
You haveMLOpsor ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows.
You can define success criteria,validatemodel quality, and ensure ML releases are robust enough for real-world use.
You have led delivery of complex ML projects, including setting technical direction, managing risks and dependencies, and driving teams toward high-quality releases.
You are comfortable operating as a player-coach: mentoring engineers and ML scientists while contributing directly tomodeling, experimentation, or architecture when needed.
You can work effectively with product, research, leadership, customers, and scientific stakeholders to turn ambiguous requirements into clear technical plans.

Bonus points if:

You have experience with federated learning, privacy-preserving ML, distributed training, or other multi-party training environments.
You have experience with Go or other systems programming languages.
You have worked on production-grade model delivery in regulated, enterprise, pharmaceutical, biotech, or other high-trust environments.
You have a publication record in top-tier ML, computational biology, or structural biology venues such asNeurIPS, ICML, ICLR, ISMB, RECOMB, or similar.

What we offer you

Industry-competitive compensation, including early-stage virtual share options
Remote-first working – work where you work best
Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget
Generous holiday allowance
Office Days at our Berlin HQ or a different European location (3x per year)
A high-calibre, execution-focused team with experience from leading organizations