Senior Software Engineer, Labelbox
AI Data Platform
$180-260k
2+ days a week in office
Data training platform for ML applications
Be an early applicant
Data training platform for ML applications
101-200 employees
Be an early applicant
$180-260k
2+ days a week in office
101-200 employees
Company mission
To build the best products for humans to advance artificial intelligence.
Role
Who you are
- 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)
Desirable
- 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
What the job involves
- As a Senior Software Engineer, AI Platform - Data Infrastructure 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
- 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)
- 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
Our take
Labelbox offers a web service and API helping companies better manage the process of annotating and labeling data used for machine learning (ML). This reduces costly and labor-intensive human data labeling, a major stumbling block for the increasing number of larger companies looking to deploy ML at scale.
Labelbox offers automated labeling as a means of boosting productivity, which puts it in direct competition with Scale AI, a company valued at over $7 billion. Founded in 2018, the relatively young Labelbox will have to find a compelling way of distinguishing itself, such as by addressing the ethical issues faced in data labeling, The company emphasizes that its product addresses the problem of bias by working as a sort of auditable record, and offers quality assurance systems to help identify it.
Labelbox has remained focused on streamlining its capabilities to raise its value in line with the rapidly growing market. The company's global expansion efforts have significantly widened its potential client base, the company has also collaborated with Google to make access to its platform easier.
Freddie
Company Specialist at Welcome to the Jungle
Insights
46% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Jan 2022
$110m
SERIES D
Feb 2021
$40m
SERIES C
Total funding: $188.9m
Company benefits
- Flexible vacation policy (we don't count PTO Days)
- 401k Program
- College savings account
- HSA
- Daily lunches paid for by the company (especially convenient while working from home)
- Virtual wellness and guided meditation programs
- Dog-friendly office
- Regular company social events (happy hours, off-sites)
- Professional development benefits and resources
- Remote friendly (we hire in-office and remote employees)
Company HQ
Mission District, San Francisco, CA
Leadership
Brian Rieger
(COO)Began as an Engineering Intern at Hudson Technologies. Co-founded Infinity Aerospace. Was an Aerodynamics Engineer at Boeing. Worked in Data Science for World Fuel Services.
Manu Sharma
(CEO)Masters in Astronautical Engineering from Stanford. Founder & CEO of Nuovo Wind. Founder & CEO of Ardulab/Infinity Aerospace. Worked at DroneDeploy for over 3 years, from Drone Engineer to Product Manager. Was a Product Manager at Planet.
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