Applied Research Engineer, Labelbox
$220-300k
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
$220-300k
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
- Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or related field
- At least 3 years of experience addressing sophisticated ML problems with successful delivery to customers
- Track record of designing robust data quality measurement and refinement systems for improving model performance
- Deep understanding of frontier models (e.g., large language models, multimodal models), state-of-the-art post-training methods, and their human data requirements
- Proficiency in programming languages such as Python, and experience with deep learning frameworks (e.g., PyTorch, JAX, TensorFlow)
- Excellent research skills with a track record of publications in top-tier AI/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, etc.)
- Adept at interpreting research literature and quickly turning new ideas into prototypes
- Strong analytical and problem-solving abilities
- Excellent communication skills and ability to collaborate in a multidisciplinary team
What the job involves
- As an Applied Research Engineer at Labelbox, you will be at the forefront of developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers
- Your role will involve designing and implementing advanced systems that align human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), etc
- You will also work on innovative techniques to measure and improve human data quality, and develop AI-assisted tools to enhance the data labeling process
- Your expertise in machine learning, frontier model training, and advanced human data alignment techniques will be crucial in pushing the boundaries of AI capabilities and delivering state-of-the-art solutions to meet the evolving needs of our customers
- Conduct cutting-edge research on advanced methods for aligning human preferences with AI systems, including RLHF and other novel approaches
- Design and develop rigorous systems to measure, enhance, and leverage the quality of human-in-the-loop data for AI training
- Create AI-assisted tools that incorporate active learning and adaptive sampling techniques to increase the efficiency and effectiveness of the human data labeling process
- Investigate the impact of different types of human feedback (e.g., demonstrations, preferences, critiques) on model performance and alignment
- Develop and implement novel algorithms for learning from human preferences and for optimizing the human feedback collection process
- Collaborate with engineering and product teams to integrate research findings into Labelbox's product suite, focusing on scalable and practical applications of human-AI alignment techniques
- Engage with customers and the AI community to understand evolving human data needs for frontier models and to share insights on best practices
- Publish research findings in top-tier academic journals and present at leading AI conferences
- Stay at the forefront of advancements in AI, particularly in areas related to human data quality, human-AI collaboration, and AI alignment
- Contribute to technical documentation, blog posts, and educational content to establish Labelbox as a thought leader in human-centric AI development
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 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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