Senior Backend Engineer (AI Drug Discovery Platform)
Engineering Python AI/Artificial Intelligence Backend
年収
Location
Benefits
13ヶ月目の給与
その他の福利厚生
Job Overview And Responsibility
We are looking for a Senior Backend Engineer to help build and scale the backend engine of our drug-discovery Super Intelligence. You will: - Design and build the APIs, services, and data flows that power AI agents, models, and lab workflows. - Work closely with AI researchers, data scientists, and biologists to turn ideas into running systems. - Help shape how our PD and CNS foundation models are served, observed, and improved over time. - This is a hands-on role with high ownership. You will write code, make architecture decisions. KEY RESPONSIBILITIES Build the core backend for our Drug Discovery SI - Design and implement backend services and APIs (mainly Python + FastAPI / Django). - Build asynchronous pipelines for tasks like molecule generation, docking, ADMET prediction, and literature search. - Integrate with AI services, research running on NVIDIA GPUs (foundation models, docking surrogates, RAG systems). Make the system reliable, observable, and safe - Design database schemas in PostgreSQL (including JSONB and vector embeddings). - Implement logging, tracing, and metrics so we can see what each experiment and agent is doing. - Ensure security and access control for sensitive health and scientific data. Work as a core member of the discovery team - Collaborate with AI engineers, lab scientists, and clinicians to understand needs and translate them into APIs and tools. - Review code and help set engineering standards. - Take part in system design discussions and help plan the next versions of our platform.
Required Skills and Experience
- 5+ years working as a backend or platform engineer, ideally with experience owning key services in production. - Strong in Python and at least one backend framework (FastAPI or Django). - Solid understanding of relational databases (PostgreSQL), transactions, indexing, and query performance. - Experience with asynchronous task queues (Celery, RQ, etc.). - Comfortable with Docker, containers, and CI/CD. - Able to design systems that are clear, maintainable, and scalable, not just “make it work”. - Curious about AI and science, even if you are not a biologist yet.
Why Candidate should apply this position
- Ownership of core intelligence: You’ll lead the modeling direction for Parkinson’s Disease-focused foundation models and scientific predictors that sit at the heart of our end-to-end discovery engine—not side experiments. - High-impact mission: Your work directly affects how quickly we can generate and prioritize better Parkinson’s Disease drug candidates, with real-world downstream validation. - GPU-first build environment: Access to modern GPU infrastructure and an engineering stack designed for training + high-throughput inference, with strong MLOps support for deployment. - End-to-end shipping culture: You’ll take models from idea → experiment → evaluation → production. We value measurable impact, reproducibility, and decision- grade reliability. - Annual leave - SHUI and Health Insurance - Bonuses and 13th-month salary. - Other benefits are considered on a case-by-case basis
Preferred skills and experiences
- Experience with cloud platforms (GCP / AWS / Azure) and container orchestration (Kubernetes, ECS). - Worked with AI / GPU workloads, model serving, or MLOps. - Experience with RAG system (e.g., pgvector, Pinecone, Weaviate). - Built or integrated AI experiment pipelines (tracking prompts, versions, metrics, A/B tests). - Any exposure to drug discovery or biology (SMILES, protein structures, omics data) is a plus, but not required.
Report to
Management team
Interview process
Phone screen -> Assignment -> Technical round
