About The Role
We are looking for a Senior Software Engineer with a passion for data systems to join our engineering team. You'll spend significant time architecting and building the core data infrastructure that powers our AI-driven platform. This role combines hands-on software development with deep involvement in data engineering initiatives, from designing scalable pipelines to implementing intelligent data processing systems that handle billions of documents and structured datasets.
As a member of our engineering team, you'll build robust, scalable systems that ingest, process, and serve data to our AI agents and end users. This is an opportunity to shape the technical foundation of a platform that's transforming how R&D teams discover and leverage global innovation data.
Responsibilities
- Design and build scalable data processing systems that handle ingestion and transformation of structured and unstructured data (APIs, SQL schemas, raw documents) into queryable, AI-ready formats
- Develop production-grade software with a focus on data-intensive applications, implementing robust error handling, monitoring, and performance optimization
- Architect and implement data pipelines using Apache Airflow and Python, ensuring reliability, scalability, and maintainability across distributed systems
- Build and optimize data storage solutions using both relational and document-based storage systems, with a focus on query performance and cost efficiency
- Create data access layers and APIs that enable seamless integration between data infrastructure and application services
- Own end-to-end feature development from data ingestion through to user-facing functionality, ensuring data quality and system reliability at every step
- Implement monitoring and observability for data pipelines and services, proactively identifying and resolving data quality issues
- Work directly with the CTO on establishing data engineering best practices, maintain comprehensive documentation, and mentor team members on data-related initiatives.
- Drive technical decisions around data architecture, storage mechanisms, and processing frameworks to support both current needs and future scale
Requirements
- 5-8 years of software engineering experience with significant exposure to data-intensive applications and distributed systems
- Strong programming expertise in Python with experience building production applications and data processing systems
- Hands-on experience with Google Cloud Platform (GCP) services, particularly for data engineering (BigQuery, Cloud Storage, Dataflow, Pub/Sub)
- Proficiency with Apache Airflow / Google Cloud Composer for building and orchestrating complex data workflows
- Solid understanding of data modeling principles for both SQL and NoSQL systems, with experience in PostgreSQL and Elasticsearch
- Experience with streaming and batch data processing frameworks and patterns at scale
- Familiarity with LangChain or similar frameworks for building LLM-powered applications
- Strong software engineering fundamentals: version control, code review, testing, CI/CD, and infrastructure as code
- Proven track record of owning production systems from initial design through deployment, monitoring, and iteration
- Excellent problem-solving skills with the ability to debug complex data and system issues
- Strong communication abilities and experience working effectively in cross-functional teams
- Thrives in a remote startup environment with the autonomy to drive projects forward and the collaborative spirit to build something meaningful
Nice to Have
- Experience with Java / Spring Boot or Angular for full-stack development capabilities
- Background in building data platforms that support GenAI or LLM applications, with understanding of vector databases and embedding pipelines
- Experience with real-time data processing and event-driven architectures
- Knowledge of data governance, lineage, and quality frameworks
- Familiarity with scientific/technical data sources (patents, research papers, regulatory filings)
- Experience with search and information retrieval systems at scale
- Background in R&D, scientific computing, or innovation intelligence domains
Why Join Us
- Build the backbone of a platform at the intersection of data and AI.
- Tackle complex challenges in data integration, scalability, and cost optimization.
- Work transparently and collaboratively in a team where ownership is valued.
- Be part of an early-stage startup where speed, impact, and growth with a positive attitude are the norm — not the exception.
Skills Required
- 5-8 years of software engineering experience in data-intensive applications and distributed systems
- Strong programming expertise in Python
- Hands-on experience with Google Cloud Platform services for data engineering
- Proficiency with Apache Airflow / Google Cloud Composer
- Solid understanding of data modeling principles for SQL and NoSQL systems
- Experience with streaming and batch data processing frameworks
- Excellent problem-solving skills
Cypris Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cypris and has not been reviewed or approved by Cypris.
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Fair & Transparent Compensation — Pay is considered competitive within the software industry, with clearly posted ranges for key roles in sales and engineering. Feedback suggests base salaries are a relative strength, with meaningful upside for high performers.
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Equity Value & Accessibility — Equity grants and an employee stock purchase option are highlighted across multiple roles. This provides accessible ownership potential aligned with company growth.
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Leave & Time Off Breadth — Unlimited paid time off and paid holidays are emphasized as standard. A deliverable-first work model supports flexibility in taking time away.
Cypris Insights
What We Do
Cypris is the vertical AI platform for corporate R&D and innovation teams. We centralize scientific papers, patents, market news, and company intelligence into a single platform with over 500M+ global data points. On top of this foundation, we’re building AI Agents tailored to R&D workflows, powered by the latest models from OpenAI, Anthropic, and others. With Cypris, teams can manage projects, automate research workflows, and track markets, competitors, and technologies to accelerate product development and R&D strategy. Today, leading mid-size to Fortune 100 companies in aerospace, genomics, cancer research, and autonomous systems rely on Cypris to innovate faster and with more clarity. Customers include the US Airforce, NASA, J&J, Yamaha and more.
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