Data Operations Engineer, Labelbox
$70-90k
+ Equity Packages
Data training platform for ML applications
Job no longer available
Data training platform for ML applications
101-200 employees
Job no longer available
$70-90k
+ Equity Packages
101-200 employees
Company mission
To build the best products for humans to advance artificial intelligence.
Role
Who you are
- 2+ years of working experience in a Technical role
- Bachelor’s Degree in Engineering, Computer Science, Data Science, or a technical field
- Proficiency in Python scripting and experience with automation of operational tasks
- Proficiency in SQL
- Experience with Labelbox or similar data annotation platforms
- Strong analytical and problem-solving skills with a demonstrated ability to optimize processes
- Experience with data pipelines and data workflow management
- Familiarity with cloud platforms such as AWS, GCP, or Azure
- English fluency
Desirable
- Prior experience in a production or process engineering role, especially in data operations or similar environments
- Knowledge of machine learning workflows and the data requirements for AI training
- Understanding of project management methodologies and the ability to work collaboratively across teams
What the job involves
- We are seeking a skilled and detail-oriented Data Operations Engineer to support our data annotation production processes
- In this role, you will play a critical part in optimizing, maintaining, and scaling our data labeling workflows, primarily using Labelbox
- You will ensure that labelers are able to efficiently and accurately generate data by building tools, automating tasks, and troubleshooting complex issues within the production pipeline
- Your ability to script in Python and apply engineering principles to data operations will be key to improving both efficiency and quality across our projects
- Build, deploy, and maintain Python scripts and other tools to streamline the data annotation process, automate repetitive tasks, and reduce manual effort
- Identify bottlenecks in the data labeling pipeline and implement solutions to enhance throughput, accuracy, and scalability of labeling operations
- Work closely with the quality assurance team to ensure that data labeling meets accuracy standards and troubleshoot any issues related to data quality
- Integrate and manage third-party tools with Labelbox, ensuring seamless operation and data flow across platforms
- Provide ongoing technical support to the project managers and labelers, assisting with technical challenges in Labelbox and associated tools
- Set up monitoring tools to track the performance of data annotation operations, reporting key metrics and areas for improvement to leadership
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
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 values
- Make Customers Win - We find great pleasure in creating solutions for our customers. By working with us, our customers are at the forefront of industrial machine learning
- Craftsmanship - Craftsmanship is the fine balance between perfection and a solution to the given constraints. To be a craftsman is to be consumed in a problem and to create your best expression of work as a result
- Seek To Understand - The machine learning industry is growing and evolving rapidly as it begins to transform every industry. We must stay humble and embrace that we have so much more to learn. We seek to understand first so that we build the most impactful solutions
Company HQ
Mission District, San Francisco, CA
Articles
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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