Product Engineer, Labelbox
AI Platform, Distributed Systems
$130-260k
2 days a week in office
Data training platform for ML applications
Job no longer available
Data training platform for ML applications
101-200 employees
Job no longer available
$130-260k
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
- Strong technical expertise with 2+ years of experience in backend engineering, specializing in distributed systems and large-scale software architecture
- Proficiency in backend technologies, including databases (Relational, Key-Value Stores, Cloud Buckets), queues, job management, message brokers, and streaming data
- Experience building APIs using REST and GraphQL, with knowledge of backend development in Node.js (NestJS preferred)
- Fluency in programming languages such as TypeScript, Java, or Python, with a solid understanding of data structures and algorithms
- Cloud expertise, with experience in at least one major platform (GCP preferred, AWS, or Azure)
- Strong problem-solving skills, able to break down complex requirements into actionable tasks and navigate ambiguity effectively
- A proactive mindset, with a strong sense of ownership and the ability to thrive in a fast-paced, dynamic environment
- Effective collaboration and communication skills, working across teams to drive projects to completion
- Commitment to continuous learning, staying current with industry trends and leveraging AI tools like Cursor and GitHub Copilot for development efficiency
What the job involves
- As a Product Engineer, Distributed Systems at Labelbox, you will design and develop core backend systems that power large-scale AI training workflows
- You will develop and enhance systems for orchestrating large-scale workflows using distributed queues, databases, and message brokers
- A key part of your role will be improving our data infrastructure to enable seamless, high-performance import and export, allowing customers to integrate Labelbox with their platforms effortlessly
- You’ll take ownership of backend components, leading projects from design to deployment
- Working closely with Product, Design, and other teams, you’ll help transform ideas into scalable, production-ready features that enhance customer workflows and drive AI model development
- Enhance AI data workflows by building scalable infrastructure for high-throughput data I/O, orchestration, and storage, leveraging distributed queues, databases, and message brokers
- Drive engineering excellence by mentoring and guiding less experienced engineers, fostering collaboration, and ensuring high-quality development practices
- Improve system reliability and customer experience by working with the Support team to troubleshoot and resolve issues within the team’s area of ownership
- Shape the future of AI development by contributing to an AI platform used by leading research labs to train multi-modal large language models (LLMs)
- Advance innovation by staying up to date with industry trends, assessing new technologies, and incorporating relevant advancements into Labelbox’s backend systems
- Share knowledge and expertise through technical documentation, blog posts, research publications, and conference presentations, establishing Labelbox as a thought leader in AI infrastructure
- Collaborate cross-functionally with engineers, product managers, and stakeholders to drive initiatives forward and develop solutions that meet customer and business needs
- 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
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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