Senior Backend Engineer, Labelbox
Data Platform
$170-215k
Plus equity
Data training platform for ML applications
Job no longer available
Data training platform for ML applications
201-500 employees
Job no longer available
$170-215k
Plus equity
201-500 employees
Company mission
To build the best products for humans to advance artificial intelligence.
Role
Who you are
- 8+ years of experience as either a full-stack or backend software engineer
- Experience in a modern programming language (Typescript, Python, Java)
- Expertise in multiple storage systems (Relational Databases, Key-Value Stores, Cloud Buckets, etc.)
- Experience with Google Cloud or AWS
- Familiarity with DevOps technologies (ArgoCD, CodeFresh, Kubernetes, etc.)
- Exposure to Google Spanner and ElasticSearch
Desirable
- Building ETL pipelines
- Kotlin
- Asynchronous Messaging systems (e.g. Kafka, Google pub/sub, AWS SQS, etc.)
- Microservices architecture
What the job involves
- As a Senior Software Engineer on our Data Platform team you will be building highly available storage platforms that are operationally simple, scale to billions of objects, and have predictable read and write performance
- Our Data Platform team maintains backend infrastructure that does data curation of large data sets in order to search, visualize, explore, and analyze all labeled and unlabeled data, metadata, and model inferences
- Design, build, deliver and maintain complex backend systems and integrations that store billions of records, offer consistent query performance, and reasonable transactional guarantees at hundreds of thousands of queries per second
- Take the lead on critical escalations, drive them to resolution, and see them as opportunities to improve
- Empower other engineering and product teams to identify their data needs and deliver APIs for them to efficiently access it
Our take
Labelbox offers a web service and API helping companies better manage the process of annotating and labelling data used for machine learning (ML). This reduces costly and labour-intensive human data labelling, a major stumbling block for the increasing number of larger companies looking to deploy ML at scale.
Labelbox offers automated labelling 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 labelling. The company emphasises 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
(Co-Founder)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
(Co-Founder & 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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