Full-Stack Engineer, AI Data Platform

Your Impact

• Own Large Surface: Design, build, and ship workflows spanning frontend UI, APIs, backend services, databases, and production infrastructure across a variety of features.
• Enable Human-in-the-Loop AI Training: Build systems that allow humans to efficiently create, review, and curate high-quality AI training and evaluation data sets.
• Support RLHF and Preference Data Workflows: Design and implement tooling that supports RLHF-style pipelines, including task generation, human review, scoring, aggregation, and dataset versioning.
• Leverage LLMs in the Review Loop: Build systems that use LLMs to assist human reviewers, such as automated checks, critiques, ranking suggestions, or quality signals.
• Advance AI Evaluation: Design and implement evaluation frameworks and interactive tools for LLMs and AI agents across multiple data modalities (text, images, audio, video).
• Create Intuitive, Reviewer-Focused Interfaces: Build thoughtful, efficient user interfaces optimized for high-throughput human review, quality control, and operational workflows.
• Architect Scalable Data & Service Layers: Design APIs, backend services, and data schemas that support large-scale data creation, review, and iteration with strong guarantees around correctness and traceability.
• Solve Ambiguous, Real-World Problems: Translate loosely defined operational and research needs into practical, scalable, end-to-end systems.
• Ensure System Reliability: Participate in on-call rotations to monitor, troubleshoot, and resolve issues across the stack.
• Elevate the Team: Re-imagine engineering practices, development processes, and documentation. Share knowledge through technical writing and design discussions.

What You Bring

• Bachelor’s degree in Computer Science, Data Engineering, or a related field.
• 3+ years of experience in a software or machine learning engineering role.
• A proactive, product-focused mindset and a high degree of ownership, with a passion for building solutions that empower users.
• Experience using frontend frameworks like React/Redux and backend systems and technologies like Python, Java, GraphQL; familiarity with NodeJS and NestJS is a plus.
• Knowledge of designing and managing scalable database systems, including relational databases (e.g., PostgreSQL, MySQL), NoSQL stores (e.g., MongoDB, Cassandra), and cloud-native solutions (e.g., Google Spanner, AWS DynamoDB).
• Working knowledge of cloud infrastructure like GCP (GCS, PubSub) and containerization (Kubernetes).
• Excellent communication and collaboration skills.
• High proficiency in leveraging AI tools for daily development (e.g., Cursor, GitHub Copilot).
• Comfort and enthusiasm for working in a fast-paced, agile environment where rapid problem-solving is key.
• A focus on writing clean, well-tested code and delivering your work on time.

Bonus Points

• Experience building tools for AI/ML applications, particularly for data annotation, monitoring, or agent evaluation.
• Familiarity with data infrastructure components such as data pipelines, streaming systems, and storage architectures (e.g., Cloud Buckets, Key-Value Stores).
• Previous experience with search engines (e.g., ElasticSearch).
• Experience in optimizing databases for performance (e.g., schema design, indexing, query tuning) and integrating them with broader data workflows.

Our Technology Stack

• Frontend: React.js with Redux, TypeScript
• Backend: Node.js, TypeScript, Python, some Java & Kotlin
• APIs: GraphQL
• Cloud & Infrastructure: Google Cloud Platform (GCP), Kubernetes
• Databases: MySQL, Spanner, PostgreSQL
• Queueing / Streaming: Kafka, PubSub