At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.
About Labelbox
We're the only company offering three integrated solutions for frontier AI development:
- Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale
- Frontier Data Labeling Service: Specialized data labeling through Aligner, leveraging subject matter experts for next-generation AI models
- Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling
Why Join Us
- High-Impact Environment: We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions.
- Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.
- Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.
- Continuous Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.
- Clear Ownership: You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.
Role Overview
As a Senior Software Engineer, AI Platform, Data Systems at Labelbox, you will lead the design and development of our core data infrastructure, powering the seamless flow, storage, and processing of data for our AI platform. Your expertise will drive the evolution of scalable systems—anchored by high-performance databases—to support large-scale workflows, high-throughput data I/O, and streaming capabilities. You’ll enable Labelbox customers to efficiently manage and stream data for training next-generation AI models. Owning critical components of our data infrastructure, including database architecture, you’ll work end-to-end on projects from design to deployment. Collaborating cross-functionally with Product, Design, and other stakeholders, you’ll transform ideas into robust, scalable solutions that enhance platform adoption and customer success.
Your Impact
- Design and build scalable data infrastructure, integrating high-performance databases (relational, NoSQL, cloud-native) with distributed systems for data processing, storage, and streaming.
- Optimize database systems for performance, reliability, and scalability, ensuring efficient data retrieval, indexing, and querying to support AI workflows.
- Develop and maintain data pipelines using distributed queues, message brokers, and job management mechanisms to enable high-throughput import/export operations.
- Collaborate with team members and stakeholders to align data infrastructure with platform goals and customer needs.
- Participate in Sprint Planning, Standups, and related activities to drive data-focused initiatives forward.
- Mentor and guide less experienced engineers, sharing expertise in data infrastructure and database optimization.
- Support the team’s area of ownership by working with the Support organization to resolve customer-facing data issues.
- Stay abreast of industry trends in data infrastructure and database technologies, incorporating relevant innovations into our systems.
- Contribute to technical documentation, research publications, blog posts, and presentations at conferences and forums.
- Innovation in AI: Enhance data infrastructure capabilities for an AI platform used by leading AI labs to develop powerful multi-modal large language models (LLMs).
What You Bring
- Bachelor’s degree in Computer Science, Data Engineering, or a related field. Advanced degree preferred.
- 5+ years of work experience in a software or data-focused company, with significant expertise in data infrastructure and backend engineering.
- Deep 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).
- Strong experience with data infrastructure components such as data pipelines, streaming systems, and storage architectures (e.g., Cloud Buckets, Key-Value Stores).
- Proficiency in optimizing databases for performance (e.g., schema design, indexing, query tuning) and integrating them with broader data workflows.
- Previous experience with distributed systems tools (e.g., queues, message brokers like Kafka or RabbitMQ, job orchestration frameworks) for real-time data processing and other use cases..
- Previous experience with search engines (e.g., ElasticSearch).
- Knowledge of backend development using languages like Python, Java, or TypeScript; familiarity with NodeJS and NestJS is a plus.
- Proficient in data structures, algorithms, and system design for large-scale data management.
- Demonstrated ability to keep up with trends in data infrastructure and database technologies.
- Excellent communication and collaboration skills.
- Strong sense of ownership and ability to thrive in a fast-paced environment.
- Comfortable with ambiguity, breaking down high-level requirements into actionable data infrastructure tasks methodically.
- Resourceful problem-solver with attention to detail, eager to take initiative and deliver results.
- High proficiency in leveraging AI tools for daily development (e.g., Cursor, GitHub Copilot).
Nice to Have
- Familiarity with data warehousing solutions (e.g., Snowflake, BigQuery).
- Experience with container orchestration systems (e.g., Kubernetes) for deploying data infrastructure components.
- Experience with one or more public cloud platforms:
- Google Cloud Platform (GCP) (preferred)
- Amazon Web Services (AWS)
- Microsoft Azure
- Understanding of the Data + AI ecosystem and its relevance to large-scale AI platforms.
- Knowledge of memory management and optimization in data-intensive systems.
- Experience with DevOps tools (e.g., ArgoCD, DataDog) for monitoring and managing data infrastructure.
- Previous experience using LLM backed AI services such as from OpenAI, Anthropic, Google, etc. to develop product features.
Engineering at Labelbox
At Labelbox Engineering, we're building a comprehensive platform that powers the future of AI development. Our team combines deep technical expertise with a passion for innovation, working at the intersection of AI infrastructure, data systems, and user experience. We believe in pushing technical boundaries while maintaining high standards of code quality and system reliability. Our engineering culture emphasizes autonomous decision-making, rapid iteration, and collaborative problem-solving. We've cultivated an environment where engineers can take ownership of significant challenges, experiment with cutting-edge technologies, and see their solutions directly impact how leading AI labs and enterprises build the next generation of AI systems.
Our Technology Stack
Our engineering team works with a modern tech stack designed for scalability, performance, and developer efficiency:
- 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
Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.
Annual base salary range
$180,000—$260,000 USD
- Location: Join our dedicated tech hubs in San Francisco or Wrocław, Poland
- Work Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility
- Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making
- Growth: Career advancement opportunities directly tied to your impact
- Vision: Be part of building the foundation for humanity's most transformative technology
Our Vision
We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs.
Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs.
Your Personal Data Privacy: Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice.
Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.
Top Skills
What We Do
Labelbox is the data factory for generative AI, providing the highest quality training data for frontier and task-specific models. Labelbox’s comprehensive platform combines on-demand labeling services with the industry-leading data labeling platform. The Boost labeling service is powered by the Alignerr community of highly-educated experts, who span all major languages and a diverse range of advanced subjects. They are available on-demand to rapidly generate new data for supervised fine-tuning, RLHF, and more. Labelbox’s software-first approach delivers unmatched control and transparency into the labeling process, leading to the generation of high-quality, consistent data at scale. Customers include Fortune 500 enterprises and leading AI labs, and Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins.
Why Work With Us
Labelboxers are driven to master their craft and build the best products for AI, which is quickly becoming one of the most significant technologies of our time. Join us in supporting our customers as they create AI breakthroughs.
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