Forward-deployed Scientist – Computational & Medicinal Chemistry

Apheris; Berlin; Remote; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

A leading company in drug discovery is looking for an expert in structure-based drug discovery to drive the adoption of ApherisFold within pharma R&D organizations. This hands-on role requires a PhD in a relevant field and the ability to engage users effectively. The ideal candidate will work closely with users to ensure successful integration of AI in their drug discovery processes. Flexible remote working options and comprehensive benefits are offered.

Leistungen

Remote-first working environment

Industry-competitive compensation

Wellbeing budget

Qualifikationen

• PhD in computational chemistry, cheminformatics, or related field required.
• Proactive user-first mindset is essential.
• Background as a power user in structure-based workflows is a must.

Aufgaben

• Act as a forward-deployed scientist to drive adoption and measurable value.
• Work with pharma users guiding benchmarking and application to discovery programs.
• Design and run structured adoption programs with success criteria.

Kenntnisse

Proactive user-first mindset

Computational chemistry

Molecular visualization tools

Clear communication skills

Basic scripting skills

Ausbildung

PhD in computational chemistry, cheminformatics, or related field

Tools

PyMOL

RDKit

Jobbeschreibung

About Apheris

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. We address this by hosting networks where pharma 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 ensures that the data IP and ownership always stay with the data custodians.

We currently host two flagship networks : the AISB Network focused on protein co-folding and binding affinity prediction and the ADMET Network focused on small-molecule property addition we have just launched ApherisFold which enables pharma teams to deploy models like OpenFold3 and Boltz-2 directly in their own environments with more capabilities to follow.

About the role

We are seeking a scientifically grounded expert in structure-based drug discovery with a strong user-first proactive mindset.

This role is central to driving adoption and boosting usage of ApherisFold within leading pharma R&D organizations. You will act as a power user of Apheris products yourself and will guide computational and medicinal chemists in benchmarking testing and applying our models to real programs. Your focus will be on anticipating user needs engaging the right stakeholders and making sure pharma scientists and users not only try the application but rely on it in their day-to-day work.

You will collaborate closely with pharma users internal ML scientists the AI Applications Team and the Product team to embed ApherisFold into existing R&D environments. If you want to accelerate the use of AI in drug discovery and you thrive on working directly with users to ensure adoption translates into measurable value this role is for you.

What you will do

• Act as a forward-deployed scientist with a commercial mandate to drive adoption and measurable value
• Work hands-on with pharma users of ApherisFold guiding benchmarking testing and application to real discovery programs
• Identify and engage the right stakeholders across pharma R&D from computational and medicinal chemists to program leaders and ensure alignment across levels of seniority
• Design and run structured adoption programs with clear success criteria and ongoing check-ins
• Build internal champions and scale usage across organizations through enablement sessions and playbooks
• Give workshops (both in person and remotely) to train users showcase best practices and accelerate adoption across research teams
• Develop case studies that demonstrate impact and can be replicated across accounts
• Capture and translate field insights into actionable product improvements with Product and the AI Applications Team
• Contribute scientific content such as example datasets best practices and evaluation protocols

What we expect from you

• You have a PhD in computational chemistry cheminformatics (bio)chemistry or a related field
• You are proactive user-first mindset with the ability to operate in a forward-deployed role that bridges science and product adoption and use
• You have a hands‑on background as a power user in structure‑based workflows (e.g. co‑folding docking protein‑ligand modeling) as part of drug discovery programs
• You are familiar with molecular visualization and cheminformatics tools (e.g. PyMOL Mol RDKit )
• You have credibility with both computational and medicinal chemists and the ability to bridge their needs
• You have clear and adaptable communication skills areable to work effectively with bench scientists project leads and decision-makers
• You have basic scripting skills to run and explain notebooks and analyses (not a software development role)
• You are ownership-driven comfortable navigating ambiguity and moving work forward without full specifications
• You are a clear and structured thinker who can translate complex problems across technical and non-technical stakeholders
• You are a fast learner with a bias to action motivated to deliver solutions that scientists and users will use

Nice to have

• You have an understanding of pharma deployment environments and integrations with common R&D platforms (e.g. LiveDesign Pipeline Pilot Benchling )
• You have e xperience in a customer-facing role ( e.g. application scientist solutions consultant field scientist)
• You have experience navigating complex pharma R&D organizations and engaging the right stakeholders at different levels of seniority
• You have experience working directly with external partners c ustomers and users in fast-moving high-stakes projects

What we offer you

• Industry-competitive compensation incl uding 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
• Office Days at our Berlin HQ or a different European location (3x a year)
• 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

Interested

Please reach out to José-Tomás (JT) Prieto ( ) Director of AI Programs directly including a current version of your CV and a short description why you would be a great fit for this role.

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 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.

Key Skills

Laboratory Experience,Immunoassays,Machine Learning,Biochemistry,Assays,Research Experience,Spectroscopy,Research & Development,cGMP,Cell Culture,Molecular Biology,Data Analysis Skills

Employment Type : Full-Time

Experience : years

Vacancy : 1