Responsibilities
- Create robust, secure user interfaces and application programming interfaces that support AI-integrated workflows.
- Design and manage data schemas across relational, non-relational, and vector databases to ensure efficiency and growth readiness.
- Lead development of front-end and back-end services with an emphasis on speed, stability, and long-term maintainability.
- Identify and eliminate performance constraints while maintaining low latency and high availability under heavy workloads.
- Utilize AWS, Kubernetes, and DevOps methodologies to deploy and manage scalable production systems.
- Collaborate with machine learning researchers and scientists to embed data systems and APIs into research processes.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a closely related discipline.
- Minimum of 6 to 8 years of hands-on experience delivering large-scale software systems to production.
- Deep knowledge in at least one core area—front-end development, back-end engineering, or data architecture—with the ability to contribute across the full stack.
- Proficiency in React and TypeScript is mandatory; practical experience with Python is highly desirable.
- Extensive background in SQL, NoSQL, and next-generation databases including vector stores; demonstrated skill in indexing, query tuning, and schema design.
- Proven track record designing and scaling reliable, high-performance APIs using RESTful or GraphQL standards.
- Direct experience using AI-powered coding tools to accelerate development workflows and improve code quality.
- Prior work in research-driven or data-heavy domains such as life sciences or materials science.
- Excellent communication skills with a history of effective collaboration across technical and non-technical teams; capable of translating complex concepts for varied audiences.
- Demonstrated problem-solving ability, with experience leading solutions that balance performance, scalability, and system longevity.
Nice to Have
- Familiarity with cloud platforms like AWS, GCP, or Azure, and tools for container orchestration, infrastructure as code, and CI/CD, such as Kubernetes, Terraform, and GitHub Actions.
- Experience with workflow orchestration systems including Airflow, Prefect, Flyte, or Temporal.
- Background in developing systems for lab environments, scientific workflows, LIMS, ELN, or machine learning platforms.
- Experience building systems that support audit trails, data lineage, reproducibility, or compliance with regulatory standards.
Team
The Scientific System of Record Team creates the foundational memory layer for company operations, linking experimental intent with real-world outcomes. Their systems enable traceability and close the Design-Build-Test-Learn cycle by integrating with data and automation infrastructure.
What You'll Be Building
- User Interfaces and APIs: Design and build high-performance, secure, and well-documented UIs and APIs that integrate with AI-driven applications.
- Database Architecture and Scaling: Develop schemas and manage diverse data systems, including SQL, NoSQL, vector databases, and other emerging technologies, for performance and scalability.
- Application Development: Drive implementation of front-end and backend services with a focus on performance, maintainability, and reliability.
- Performance and Reliability: Diagnose and resolve system bottlenecks while ensuring high availability and low-latency performance across large-scale workloads.
- Cloud and Infrastructure: Leverage AWS services, Kubernetes, and modern DevOps practices to build and deploy production-grade systems at scale.
- Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows.
What You’ll Need to Succeed
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
- 6–8+ years of engineering experience building and deploying large-scale systems in production.
- Strong expertise in at least one of the following areas, with the ability to work across the stack: front-end engineering, backend engineering, or data modeling and system design.
- TypeScript, React, and Python: Strong experience with React and TypeScript is required; Python experience is strongly preferred.
- Databases: Strong experience with SQL, NoSQL, and emerging database technologies such as vector databases; proven track record in schema design, indexing, and query optimization.
- API Development: Proven ability to design and scale RESTful or GraphQL APIs with a focus on reliability and performance.
- Hands-on experience using AI coding assistants to improve engineering productivity.
- Scientific or Data-Intensive Domains: Experience working in life sciences, materials science, or other research-heavy or data-intensive fields.
- Communication and Collaboration: Strong listening skills and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences.
- Problem Solving: Proven ability to take ownership of complex technical challenges while balancing trade-offs between scalability, performance, and maintainability.
Bonus Points For
- Cloud and DevOps: Hands-on experience with AWS, GCP, or Azure; strong understanding of Kubernetes, containerization, infrastructure as code such as Terraform or CloudFormation, and CI/CD pipelines such as GitHub Actions.
- Orchestration Systems: Experience with orchestration tools such as Flyte, Temporal, Airflow, Prefect, or similar systems.
- Experience building laboratory, scientific workflow, LIMS, ELN, data platform, or ML platform products.
- Experience designing systems that support auditability, traceability, reproducibility, data provenance, or regulated workflows.
