Senior Software Engineer – Drug Discovery Workflow
- Role
- Backend
- Experience
- Senior
- Employment
- Full-time
- Company size
- Startup
Open to DE only. Set where you work from to check your eligibility.
No BS summary
Senior backend engineer with 5+ years of experience in Python and Docker. Must have a degree in CS/Engineering or equivalent experience, strong ownership, and excellent communication skills in English. Experience with drug discovery workflows, cloud providers, and Kubernetes is a plus.
Core skills
Required skills
Optional skills
Required languages
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 into further drug modalities.
- Antibody developability Network:Pharma partners collaborate to federate historical and purpose-built antibody developability data sets for secure ML training, without data leaving each partner’s environment.
About the role
We are looking for a hands-on Senior Software Engineer to design and implement the drug discovery workflows at the core of Apheris' platform.
In this role, you will be a strong individual contributor with end-to-end ownership of meaningful technical scope - designing, building, and operating workflows used in production by leading pharmaceutical companies. While this role does not include people management, it requires technical leadership through example, strong ownership, and active collaboration across teams.
You will work closely with drug discovery scientists, the data team, machine learning and software engineers to translate scientific problems into reliable, scalable, automated workflows. This role is ideal for someone who enjoys shipping high-quality software in a fast-moving startup environment, takes pride in execution, and is motivated to continuously improve both the product and the way we build it.
About you
What you will do
- Design and implement drug discovery workflows primarily in Python to solve real drug discovery problems and automate the drug design process.
- Contribute to system design and architectural discussions, helping evolve platform components as the product and customer base scale.
- Collaborate cross-functionally across engineering, data, machine learning, and drug discovery science to deliver cohesive, end-to-end functionality.
- Use AI coding tools efficiently, exercising good judgment on the balance between agentically built proof of concept and robust, production-grade code.
- Write high-quality, maintainable, and well-tested code, and actively participate in code reviews to uphold strong engineering standards.
What we expect from you
- A degree in computer science, engineering, computational chemistry/biology, or equivalent hands-on development experience in a related domain.
- 5+ years of professional experience as a developer, with a strong focus on backend systems.
- Strong programming skills in Python.
- Solid experience working with Docker and containerized applications in production environments.
- Strong understanding of software engineering best practices, including testing strategies.
- A strong sense of ownership and accountability for technical outcomes, including reliability and long-term maintainability.
- Comfortable working in a fast-paced, iterative startup environment where priorities evolve, and trade-offs are part of daily decision-making.
- Excellent communication skills and fluency in English (verbal and written).
Nice to have
- Have experience running drug discovery workflows using engines like Nextflow, Prefect and Argo Workflow.
- Experience working with AWS or another major cloud provider, ideally in hybrid environments that include on-premise deployments.
- Practical experience with Kubernetes, including deploying, operating, and debugging cloud-native workloads.
- Experience supporting customers in production environments, including debugging issues and communicating technical context clearly and pragmatically.
- Experience with CI/CD pipelines and infrastructure-as-code.
- Prior exposure to life sciences, machine learning platforms, or data-intensive products (not required).
- A strong interest in combining software engineering skills with life sciences.
What we offer you
- Industry-competitive compensation, including 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
- 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
Logistics
Our mission statement
What you'll do
- Design and implement drug discovery workflows primarily in Python to solve real drug discovery problems and automate the drug design process.
- Contribute to system design and architectural discussions, helping evolve platform components as the product and customer base scale.
- Collaborate cross-functionally across engineering, data, machine learning, and drug discovery science to deliver cohesive, end-to-end functionality.
- Use AI coding tools efficiently, exercising good judgment on the balance between agentically built proof of concept and robust, production-grade code.
- Write high-quality, maintainable, and well-tested code, and actively participate in code reviews to uphold strong engineering standards.
What they require
- A degree in computer science, engineering, computational chemistry/biology, or equivalent hands-on development experience in a related domain.
- 5+ years of professional experience as a developer, with a strong focus on backend systems.
- Solid experience working with Docker and containerized applications in production environments.
- Strong understanding of software engineering best practices, including testing strategies.
- A strong sense of ownership and accountability for technical outcomes, including reliability and long-term maintainability.
- Comfortable working in a fast-paced, iterative startup environment where priorities evolve, and trade-offs are part of daily decision-making.
- Excellent communication skills and fluency in English (verbal and written).
Benefits
- Industry-competitive compensation, including 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
- 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
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 into further drug modalities.Antibody developability Network:Pharma partners collaborate to federate historical and purpose-built antibody developability data sets for secure ML training, without data leaving each partner’s environment.