Principal Scientist Computational Chemistry

Festanstellung, Vollzeit · Remote (UTC +/- 2 hrs)

About the role

We are seeking an accomplished and self-motivated principal scientist with exceptional interpersonal and problem-solving skills to join our team.

At Apheris, we power federated data networks 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.

We currently focus on two key platforms: one centered around protein structure and binding predictions (https://www.apheris.com/join-a-network/aisb), and one structured around ADMET predictions (https://www.apheris.com/join-a-network/admet). We are looking for a Principal Scientist to lead the scientific direction and execution of our ADMET network. This is a high-impact role that aims to transform how pharmaceutical companies work collectively to improve their modeling capabilities. The ultimate goal is to accelerate drug discovery research globally and to improve patient lives at scale.

You will lead the scientific interactions with the pharmaceutical partners in the network and will work closely with our leadership team on scientific strategy. This is also a hands-on role, developing, applying and advancing state of the art deep learning models. In the consortium, you function as the scientific authority on data preparation pipelines, data harmonization, and experimental design. While this is not a people management role, you will also guide and mentor other engineers and researchers on a content level. You have a deep understanding of the full drug discovery process, and you bring in deep scientific ADMET, pharmacokinetics, and toxicity expertise. Fluently communicating complex concepts with external scientific stakeholders comes naturally for you.

While this role currently focuses on the ADMET network, Apheris is preparing for additional networks in drug discovery and we expect you will make scientific impact here as well. If you want to be part of a mission-driven team building cutting-edge AI systems for drug discovery and life sciences – and you know what it takes to create impact – this role is for you.

What you will do

Drive the scientific approach for development and benchmarking of state-of-the-art ADMET models on distributed, unseen external data.
Influence strategic decisions on data strategy, experimentation design and benchmarking.
Scientific lead for our ADMET network of pharmaceutical companies to define data preprocessing, selection, harmonization, and benchmarking strategies for novel training tasks involving ADMET data. This also includes mentor internal team members on scientific content level.
Contribute to publications or open-source contributions where relevant.
Design appropriate experimental methodologies for benchmarking novel ADMET prediction models.
Develop a deep technical understanding of the Apheris product and how it maps to the current ADMET use-cases we are working on. Take ownership of an ADMET experimentation methodology. Develop a roadmap and experiment plan for preparing data and adapting models to one high-value use case.
Lead multiple data preparation efforts in ADMET and demonstrate measurable progress in model performance and real-world impact.

You should apply if

You successfully employed cutting edge machine learning models or tools to drive drug discovery programs.
You have expert-level knowledge of all state-of-the art models in computational drug discovery, including MD, FEP, ADMET models, multitask learning, graph neural network approaches, …
You are highly collaborative and a strong communicator.
You are organized with effective time management and planning skills.
You are driven and conscientious: proactively identify opportunities to contribute scientifically and organizationally to promote a culture of innovation and inclusion.
You hold a PhD in computational chemistry, cheminformatics, computational biology or bioinformatics, at least 5 years of professional expertise in drug discovery.
You have experience in leading strategic scientific initiatives in drug discovery and in driving execution on ambitious modeling plans.
You have expert-level knowledge on ADMET models, understand how they fit in the drug discovery lifecycle, and how to engage with stakeholders to create impact.
You have deep experience with ADMET data, including assay protocols and data harmonization.
You have expert skills in machine learning, scientific computing, data analysis, and cloud computing.

Bonus points if

You built/trained ADMET machine learning models using contemporary architectures (e.g. Graph Neural Networks, Transformers, …).
You bring experience in federated learning, privacy-preserving ML, or secure model training.
You published in top-tier ML or biology journals/conferences (e.g., NeurIPS, ICML, Nature Methods, Bioinformatics).
You have experience with working in a consortium and aligning with multiple external stakeholders.
You contributed to open-source ML or cheminformatics tooling.

What we offer you

Industry-competitive compensation, incl. 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

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 high-quality 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 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.
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 co-workers on the ground.

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