Technical Customer Success Manager

At Encord, we're building the AI infrastructure of the future. Today, the biggest challenge companies face in getting an AI product to market is actually not half as glamorous as it may seem: it's all about data quality. In fact, the success of any AI application today relies on the quality of a model's training data — and for 95% of teams, this essential step is both the most costly and the most time-consuming.

As ex-computer scientists, physicists, and quants, we felt first-hand how the lack of tools to prepare quality training data was impeding the progress of building AI. AI today is what the early days of computing or the internet were like, where the potential of the technology is clear, but the tools and processes surrounding it are still primitive, preventing the next generation of applications. This is why we started Encord.

We’re a team of 60 working at the cutting edge of computer vision and deep learning, backed by top investors, including CRV and Y Combinator, leading industry executives like Luc Vincent, former VP of AI at Meta, and other prominent leaders in AI. We are one the fastest growing companies in our space, and consistently rated as the best tool in the market by our customers.

About Us

Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production.

Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more. We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator.

The Role

As part of Encord's continued rapid growth, we are looking for an experienced Customer Success Manager to work with our largest and most complex customers to help them successfully embed Encord into the innovative and world-leading AI applications that they are building.

Working closely with the Sales, Growth and Engineering teams, and reporting directly to the co-founders, the ideal candidate is someone with a track record of making customers successful with complex technology while consistently over achieving on Gross and Net Revenue Retention targets.

About you

• You have 2-5 years of customer facing experience, especially with Enterprise customers
• You are an exceptional communicator across different channels; you are comfortable leading a meeting with the CTO of a Fortune 500 company, the founder of a fast-growing startup, or the lead ML engineer of a research institution
• You are structured and organized, and instinctively know how to prioritize your time
• You have a track record of successful internal collaboration, with both technical and commercial teams
• You are highly consultative, skilled with discovery questioning and uncovering new use cases
• You have a track record of strengthening existing customer relationships and proactively developing new ones; you make sure you're always multi-threaded at different levels
• You're ambitious and you want to rise faster than would be possible in any 'normal' career and you're happy to roll up your sleeves and do whatever is required
• You're intelligent and can dip into the technical intricacies while staying focused on achieving the outcome

What We Offer

• Competitive salary, commission, and meaningful equity in a high-growth start-up
• Clear, accelerated growth opportunities as the company scales rapidly
• Strong in-person culture: 3–5 days/week in our newly launched North Beach loft office
• Flexible PTO to fully recharge
• 18 paid vacation days in the U.S. plus federal holidays
• Annual learning & development budget
• Comprehensive health, dental, and vision coverage
• Frequent travel opportunities across the U.S., London, and Europe
• Bi-annual company offsites, twice-weekly team lunches, and monthly socials

The role will be exposed to a broad tech stack (e.g. ReactJS, Python, REST & GraphQL, OpenCV, PyTorch, GCP, AWS & CUDA, Kubernetes) and the cutting edge of computer vision and deep learning.