We are looking for a Senior Full-Stack Engineer (AI-Native) with proven experience in AI-driven application development, someone who can take a product from idea to production release using modern AI tools for coding and iteration, and who thrives in the emerging “vibe coding” paradigm.
This role is not about simply using AI assistants; it requires an engineer who understands how to orchestrate AI as part of the engineering workflow, accelerating development, validating ideas rapidly, and transforming experimental prototypes into reliable, production-grade systems.
You will join a US-based bioinformatics startup building a platform for genetic medicine. The company uses Antisense Oligonucleotide (ASO) technology and AI to identify therapeutic targets and design treatments much faster, with the goal of treating genetic diseases with the urgency patients, especially children, need.
Important When Applying: If you are interested in this position, please include a short description of your commercial experience building products or MVPs using AI tools from scratch through delivery, specifying: the problem you solved and the AI tools and models you used.
Key Qualifications: ● 6+ years of commercial experience as a Full-Stack Software Engineer. ● Demonstrated experience building products or MVPs end-to-end using AI-assisted development tools. ● Proven track record as an AI-native engineer who effectively leverages modern LLM ecosystems (e.g., Google AI Studio, Gemini, Cursor, Copilot, or similar) for: rapid implementation and iteration, code generation and refinement, testing, debugging, and documentation acceleration, workflow automation. ● Hands-on experience working in a vibe coding environment, including: turning ambiguous ideas into working prototypes quickly, making pragmatic engineering trade-offs, and evolving experimental code into maintainable production systems. ● Ability to work independently without a dedicated supervising architect, owning technical decisions and execution. ● Familiarity with cloud-native architectures. ● Experience with Python for data processing and AI integrations. ● Excellent English (spoken and written) — mandatory.
Буде плюсом
Preferred Qualifications: ● Degree or formal background in Bioinformatics, Computational Biology, or a related field. ● Understanding of molecular biology concepts such as: genetics, DNA/RNA, protein structures, RNA/protein folding. ● Experience working with bioinformatics datasets (genomics, transcriptomics, etc.). ● Experience building data pipelines for scientific or medical applications.
Пропонуємо
We offer: ● А place with friendly environment where you can reach your full potential and grow your career. ● Flexible work schedules. ● Work from home. ● Social package: paid sick leave and vacation. ● English courses, medical insurance, legal support, etc.
Обов’язки
As a Full-stack AI-Native Engineer you will: ● Transform existing “vibe-coded” scripts and early-stage tools into robust, deployable applications capable of handling complex biological data. ● Take experimental solutions and operationalize them in production environments. ● Build and maintain applications using Google AI Studio (Gemini API) and Google Cloud Platform. ● Work with bioinformatics data (genomics, transcriptomics, ASO targeting) to power internal tools. ● Design and implement UI/UX, backend services, and supporting cloud infrastructure. ● AI-assisted development workflow: actively use tools like GitHub Copilot to accelerate implementation, refactoring, and testing while maintaining high engineering standards. ● Introduce the right level of architecture, observability, and CI/CD without over-engineering. ● Work directly with leadership and scientists to translate biological needs into functional digital products.
Про проєкт
The company is building a next-generation platform for designing genetic therapies by combining AI with ASO technology to dramatically shorten the path from biological insight to viable treatment design. You will operate in an environment where: early research prototypes already exist, rapid iteration is essential, engineering plays a critical role in converting experimental work into scalable internal platforms, speed and scientific impact matter more than rigid process.
