Solutions Engineer

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, Next
47 and Y Combinator.

As a Solutions Engineer at Encord, you will be the core technical expert and problem-solver for our most complex customers. You are the engineering-focused counterpart to the commercial team, partnering with Account Executives to architect and implement solutions for prospects on the cutting-edge of AI.

You will be the expert on how our platform integrates and scales, establishing technical credibility and guiding prospects through complex evaluations, especially for applications in robotics, autonomous systems, and multimodal AI. You are both a deep technical builder and an expert business value translator.

• Partner with Account Executives and the ML team to lead the technical strategy for complex, enterprise sales cycles and co‑own the technical win
• Lead deep technical discovery sessions with a wide range of stakeholders (from ML Engineers to MLOps leaders) to not only uncover business pain but to architect a complete technical solution and integration path
• Architect, build, and deliver highly‑technical, customised product demonstrations and proof‑of‑concepts (POCs) that solve specific, complex customer problems
• Own the end‑to‑end technical implementation of POCs, writing robust, production‑quality Python scripts for complex data ingestion, pre/post‑processing, dataset management, and custom platform integrations
• Act as the key technical advisor to prospects, expertly guiding them through security, architecture, and integration evaluations, particularly on complex data pipelines involving multimodal data, LiDAR, or robotics sensor data
• Translate complex technical architectures and findings into clear, persuasive value propositions for senior, non‑technical stakeholders (Directors, VPs, CTOs)
• Serve as the "voice of the customer" to our Product and Engineering teams, channelling detailed, technical feedback from complex enterprise clients to help shape the product roadmap

• Coding experience — hands‑on experience building and debugging scripts and solutions using Python or other scripting languages; experience with SDKs is desirable
• Excellent communication and presentation skills — you can confidently command a room of engineers and just as easily simplify complex concepts for senior decision‑makers
• Strong technical command of modern cloud infrastructure (GCP, AWS, Azure) and machine learning concepts
• A "hacker mindset" with a strong engineering foundation: you are a creative problem‑solver who builds robust, scalable, and efficient scripts and integration workflows to overcome technical hurdles
• A solution‑driven and customer‑obsessed mindset — while you understand commercial goals, your passion is for solving complex technical problems and ensuring customer success
• Ability to translate value between technical and commercial stakeholders, both with prospects and internally within Encord
• Bonus: hands‑on experience working with multimodal AI, sensor‑fusion, physical AI, LiDAR data, or in the robotics industry

• 1 – 5+ years of experience in a customer‑facing role within an AI business, such as Solutions Engineering, Solutions Architecture, or Technical Account Management
• Proficiency in Python, with the ability to write clean, production‑quality scripts for data ingestion, pipeline automation, API integrations, and custom tooling; experience with REST APIs and SDKs is strongly preferred
• Hands‑on experience with at least one major cloud platform (GCP, AWS, or Azure), including data storage, compute, and deployment patterns common in ML workflows
• Solid understanding of machine learning concepts and the ML development lifecycle — from data preparation and annotation through model training, evaluation, and…