Machine Learning Engineer

Labelbox is the data factory for generative AI, providing the highest quality training data for frontier and task-specific models. Labelbox’s comprehensive platform combines on-demand labeling services with the industry-leading data labeling platform. The Boost labeling service is powered by the Alignerr community of highly-educated experts, who span all major languages and a diverse range of advanced subjects. They are available on-demand to rapidly generate new data for supervised fine-tuning, RLHF, and more. Labelbox’s software-first approach delivers unmatched control and transparency into the labeling process, leading to the generation of high-quality, consistent data at scale.

Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs.

About the Role

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.

Your Day to Day

• 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.

About You

• 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.

Engineering at Labelbox

We build a comprehensive platform and end-to-end tool suite for AI system development. We believe in providing the best user experience at scale with high quality. Our customers use our platform in production environments, daily, to build and deploy AI systems that have a real positive impact in the world.

We believe in collaborative excellence and shared responsibility with decision making autonomy wherever possible. We strive for a great developer experience with continuous fine tuning. How we work is one of the cornerstones of engineering excellence at Labelbox.

We learn by pushing boundaries, engaging in open debate to come up with creative solutions, then committing to execution. We continuously explore and exploit new technologies, creating new and perfecting existing techniques and solutions. Making customers win is our North Star.

Salary and Work Environment

Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.

Annual base salary range: $155,000 - $190,000 USD

Excel in a remote-friendly hybrid model.

We are dedicated to achieving excellence and recognize the importance of bringing our talented team together. While we continue to embrace remote work, we have transitioned to a hybrid model with a focus on nurturing collaboration and connection within our dedicated tech hubs in the San Francisco Bay Area, New York City Metro Area, and Wrocław, Poland. We encourage asynchronous communication, autonomy, and ownership of tasks, with the added convenience of hub-based gatherings.

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Due to current team needs, we are only considering candidates based in the San Francisco Bay Area. Please confirm you are based in the SF Bay Area. *