Machine Learning Engineer, Labelbox
$155-190k
Data training platform for ML applications
Job no longer available
Data training platform for ML applications
201-500 employees
Job no longer available
$155-190k
201-500 employees
Company mission
To build the best products for humans to advance artificial intelligence.
Role
Who you are
- Bachelor’s degree in computer science or related field. Advanced degree preferred
- 3-4+ years of work experience in a software company in the domain of distributed systems, ML engineering, AI/ML infrastructure or platforms
- Software design and architecture skills in large-scale systems and AI/ML systems design
- Experience in developing and implementing systems that integrate with Foundation Models for real-world applications
- Knowledge of machine learning algorithms, natural language processing, and deep learning frameworks
- Bonus points for experience (including academic projects or internships) working on Generative AI, including model fine-tuning, experimentation, metrics for model evaluation, monitoring and quality-control
- Good grasp of the overall Data + AI ecosystem, including data processing technologies
- Proficiency in programming languages such as Python, Typescript, or Java
- Curious about industry trends and research in the AI/ML landscape
- Excellent communication and collaboration skills
- Thrive in a fast-paced environment with willingness and ability to dive deep
- Resourceful, creative, problem-solver with an attention to detail who will not hesitate to take initiative and get things done
What the job involves
- As a Machine Learning Engineer at Labelbox, you will be an important part of a team building a scalable AI platform that uses foundation models for real-world AI applications
- You will be responsible for prototyping and developing production grade tools for model fine tuning, evaluation, experimentation, metrics and quality control, and alignment with human or AI feedback
- You will draw on your expertise in machine learning, natural language processing, and deep learning, and how various Foundation Models, including multi-modal models, embody these technologies, to drive the success of our AI initiatives by executing and delivering on product capabilities that meet the needs of our customers
- Enhance and improve Labelbox’s core machine learning capabilities, including model registry, training and inferencing, towards making it a best-in-class AI Platform-as-a-Service. Examples include improving inference latency or optimizing training memory consumption
- Implement approaches and metrics for evaluating generated output from models, including human-preference metric, e.g. ranking and selection and other types, e.g. model performance variance with ELO scores
- Work with more experienced ML engineers on incorporating and implementing new models and latest ML techniques into the Labelbox AI engine
- Collaborate with other engineering teams on best practices for leveraging machine learning, specifically using Labelbox’s AI engine as a PaaS
- Guide customers and the broader Labelbox community with best practices in AI using Foundation Models, through PoC applications, webinars, blog posts, etc
- Oversee and define mechanisms for adaptation, hyperparameter tuning and fine-tuning of foundation models to suit specific application requirements
- Stay abreast of industry trends, emerging technologies, and advancements in foundation models and their applications
- Contribute to technical documentation, blog posts, and presentations at conferences and forums
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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