Staff Machine Learning Engineer

Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to inject these systems with the right degree of human supervision and automation. Whether they are building AI products by using LLMs that require human fine-tuning, or applying AI to reduce the time associated with manually-intensive tasks like data labeling or finding business insights, Labelbox enables teams to do so effectively and quickly.

Current Labelbox customers are transforming industries within insurance, retail, manufacturing/robotics, healthcare, and beyond. Our platform is used by Fortune 500 enterprises including Walmart, Procter & Gamble, Genentech, and Adobe, as well as hundreds of leading AI teams. We are backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures (Google's AI-focused fund), Databricks Ventures, Snowpoint Ventures and Kleiner Perkins.

About the Role

As a Staff Machine Learning Engineer at Labelbox, you will be a technical leader for the 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 in terms of roadmap definition, architecture decisions and execution, delivering products 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.
• Conduct feasibility studies and prototype development for new applications leveraging foundation models.
• Research design, and incorporate 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. 
• Provide guidance to other engineering teams on best practices for leveraging machine learning, specifically using Labelbox’s AI engine as a PaaS.
• Mentor and guide less experienced engineers while driving initiatives towards completion.
• Guide customers and the broader Labelbox community with best practices in AI using Foundation Models, through meetings, 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.
• Engage with stakeholders, including customers, to understand their needs, gather requirements, and provide expert advice on AI-driven solutions.
• Stay abreast of industry trends, emerging technologies, and advancements in foundation models and their applications. Analyze, assess and incorporate technologies coming out of various AI research labs.
• Contribute to technical documentation, research publications, blog posts, and presentations at conferences and forums.

About You

• Bachelor’s degree in computer science or related field. Advanced degree preferred.
• 5+ years of work experience in a software company in the domain of distributed systems, ML engineering, AI/ML infrastructure or platforms.
• Extensive software design and architecture skills in large-scale systems and AI/ML systems design.
• Proven experience in developing and implementing large-scale systems that integrate with Foundation Models for real-world applications.
• Experience with various types of foundation models and multi-modal models.
• Experience with machine learning algorithms, natural language processing, and deep learning frameworks.
• Experience working on Generative AI, including model fine-tuning, experimentation, metrics for model evaluation, monitoring and quality-control.
• Strong understanding of AI agents architecture, RLHF, building and/or using ML pipelines for training and inference.
• An understanding of transformers and LLM architecture.
• Good grasp of the overall Data + AI ecosystem, including data processing technologies.
• Proficiency in programming languages such as Python, Typescript, or Java.
• Demonstrated ability to keep up with industry trends and research in the AI/ML landscape.
• Excellent communication and collaboration skills.