Machine Learning Support Engineer

San Francisco /
Customer Success /
Labelbox is building software infrastructure for industrial data science teams to do data labeling for the training of neural networks. When we build software we take for granted the existence of collaborative tools to write and debug code. The machine learning workflow has no standard tooling for labeling data, storing it, debugging models and then continually improving model accuracy. Enter Labelbox. Labelbox's vision is to become the default software for data scientists to manage data and train neural networks in the same way that GitHub or text editors are defaults for software engineers.

Current Labelbox customers include American Family Insurance, Lytx, Airbus, Genius Sports, Keeptruckin and more. Labelbox is venture backed by Andreessen Horowitz, Gradient Ventures (Google’s AI-focused venture fund), Kleiner Perkins and First Round Capital and has been featured in Tech Crunch, Web Summit and Forbes.

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    • Create great experiences for our customers when they need help. Build trust and advisory relationships with customers to help them better use the product.
    • Act as the frontline of customer love, utilizing live chat and internal resources as your secret weapons to proactively resolve product issues and provide proactive guidance to deepen relationships and product usage.
    • Learn Labelbox's product at a deep level and help customers do the same. Learn what’s happening next with the product, and help customers prepare for the use of new features.
    • Go beyond the question being asked; understand how our customers define their own success with the product and help them work toward that success.
    • Proactively propose creative solutions to address customers’ business problems and goals.
    • Be a voice for our customers during internal discussions and projects at Labelbox. Represent their needs and struggles to help drive our products in a strong direction.
    • Monitor and identify trends in customer experiences. Work within the team and with other teams at Labelbox give customers the information and tools they need to more effectively and efficiently support themselves in the use of the product.

Key Attributes

    • Proficiency in Python Experience in a business setting is great, but new grads who have used/learned/taught themselves Python will definitely be considered.
    • An ability to navigate and advise on efforts related to complex customer requests or projects, gathering additional human resources for assistance if needed
    • An ability to learn quickly to understand and articulate new technologies and corresponding value propositions
    • A strong motivation to work closely with customers to create the best possible experiences with Labelbox
    • Assertive, positive and effective communication skills in English – both written and oral – with considerable attention to detail and the ability to present and influence
    • Demonstrated problem-solving ability, particularly in complex technical situations
    • Ability to thrive in a dynamic, fast paced startup environment
    • Four year university BA/BS degree (or equivalent)
    • A couple years of business experience (preferably in a SaaS customer success or data analysis role) is nice to have, but not required
    • Must be authorized to work in the US without visa sponsorship from an employer


    • BA/BS in business, engineering or a related field; Computer Science/Engineering degree is a plus
    • Experience using Python is a big plus
We believe that AI has the power to transform every aspect of our lives -- from healthcare to agriculture. The exponential impact of artificial intelligence will mean mammograms can happen quickly and cheaply irrespective of the limited number of radiologists there are in the world and growers will know the instant that disease hits their farm without even being there.

At Labelbox, we’re building a platform to accelerate the development of this future. Rather than requiring companies to create their own expensive and incomplete homegrown tools, we’ve created a training data platform that acts as a central hub for humans to interface with AI. When humans have better ways to input and manage data, machines have better ways to learn.