Nexer Czech Republic is a division within DANIR AB – an IT consulting and R&D company. We work with world‑renowned brands from Scandinavia, the USA, the UK, and Western Europe. Our focus is on growing strong in competencies rather than in numbers.
If you like what we do, see our offer — maybe it is you we will have the pleasure to meet! :)
Our client is an AI‑driven Pharma-Tech American company focused on discovering, designing, and developing better medicines using advanced data science. The team is building scalable and maintainable enterprise‑grade solutions used in precision cancer medicine.
Nexer is seeking a Machine Learning Infrastructure Engineer to join the ML-Driven Predictive Chemistry team for a 9-month maternity cover. This team is at the heart of our drug design platform, leveraging massive internal datasets and high-performance computing to accelerate the discovery of new medicines.
In this role, you will bridge the gap between high-level chemistry modeling and robust engineering. You will be responsible for ensuring our predictive models are not just “research-grade,” but production-ready, scalable, and deeply integrated into our automated drug design workflows.
Key Responsibilities
1. Infrastructure & Platform Engineering
Productionize Models: Build and scale the infrastructure required to deploy state-of-the-art models (GNNs, Transformers) for chemistry and structural biology.
Data Architecture: Manage and optimize Cloud-Native Data Warehouses (BigQuery/Snowflake) to handle petabyte-scale molecular data, ensuring high-concurrency access for analytical workflows.
Pipeline Orchestration: Design and maintain robust data and ML pipelines using Argo or Prefect to ensure reproducible model training and inference.
2. Model Deployment & ML Ops
Active Contribution: Develop, train, and deploy cutting-edge predictive models within our specialized chemistry modeling platform.
Scalable Compute: Utilize Anyscale/Ray to manage distributed training and inference workloads across massive GPU clusters.
Agentic Systems: Architect and maintain LLM-based agentic systems to automate complex drug design reasoning and decision-making tasks.
3. Collaboration & Innovation
Cross-Functional Partnership: Work closely with the core ML Infrastructure team to ensure chemistry-specific tools align with broader company engineering standards.
Programming: Mastery of Python and its scientific ecosystem (PyTorch, NumPy, Pandas).
AI Coding Agents: Modern software development practices scaffolded by AI-coding agents for production purposes, including documentation, unit testing, formatting, etc.
ML Ops & Infra: Proven experience building and maintaining production-grade Machine Learning infrastructure (Model registries, feature stores, and deployment patterns).
Workflow Orchestration: Experience with container-native orchestration tools like Argo Workflows or Prefect.
Cloud Data Warehousing: Hands-on experience with BigQuery or Snowflake, optimizing complex SQL queries and managing large-scale analytical tables.
Cloud Ecosystem: Familiarity with modern cloud environments (GCP preferred) and Kubernetes.
Soft Skills: Exceptional cross-functional communication; the ability to explain infrastructure trade-offs to research scientists.
Located in Czechia, Slovakia or Hungary
Fluency in English (spoken and written)
Domain Expertise: Background in cheminformatics (RDKit, molecular featurization) or biological modeling.
Distributed Systems: Experience with Anyscale/Ray for distributed computing.
Agentic Frameworks: Experience building or deploying autonomous agents using LLMs
Start date: ASP 2026
Workload: full-time
Location: Czechia, Slovakia or Hugary (remote within these countries only - please note, that candidates outside of these countries will be not considered)
B2B contract
Client: global US‑based life‑science company
Screening Call
A 30‑minute introductory Teams video call with our recruiter, Nikola Otáhalová.
Technical Interview
A 60‑minute Teams video call with one of our software developers, focused on technical questions related to the required tech stack.
Final Interview with the Client
A 60‑minute Teams video call with the client.
Submit your application online in one easy step! Apply now!
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