Solutions Engineer

Responsibilities

• Partner with Account Executives and the ML team to lead technical strategy for complex enterprise sales cycles and co-own the technical win.
• Lead technical discovery sessions with ML Engineers, MLOps leaders, and other stakeholders to uncover business pain and architect complete technical solutions and integration paths.
• Architect, build, and deliver customized product demonstrations and proof-of-concepts (POCs) for complex customer problems.
• Own end-to-end POC implementation, writing production-quality Python scripts for data ingestion, preprocessing and postprocessing, dataset management, and custom platform integrations.
• Advise prospects through security, architecture, and integration evaluations, including complex pipelines involving multimodal data, LiDAR, or robotics sensor data.
• Translate technical architectures and findings into persuasive value propositions for Directors, VPs, CTOs, and other senior stakeholders.
• Share detailed technical customer feedback with Product and Engineering teams to help shape the product roadmap.

Requirements

• 1–5+ years of experience in a customer-facing role within an AI business, such as Solutions Engineering, Solutions Architecture, or Technical Account Management.
• Hands-on coding experience building and debugging scripts and solutions using Python or other scripting languages.
• Ability to write clean, production-quality Python scripts for data ingestion, pipeline automation, API integrations, and custom tooling.
• Strong communication and presentation skills with technical and senior business audiences.
• Strong technical command of modern cloud infrastructure, including GCP, AWS, or Azure.
• Understanding of machine learning concepts and the ML development lifecycle, from data preparation and annotation through model training, evaluation, and deployment.
• Ability to lead technical discovery, architect integration solutions, and own end-to-end POC delivery in complex enterprise sales environments.
• Track record of earning trust with ML engineers, MLOps professionals, and data engineers while communicating value clearly to senior business stakeholders.
• Experience navigating enterprise security, compliance, and architecture reviews during technical sales or implementation processes.
• A creative, engineering-focused problem-solving approach and a customer-obsessed mindset.

Nice to Have

• Experience with SDKs, REST APIs, and custom integrations.
• Hands-on experience with multimodal AI, sensor fusion, physical AI, LiDAR data, robotics sensor data, or robotics applications.
• Experience at a high-growth startup or scale-up.

Compensation and Benefits

• Competitive salary, commission, and meaningful equity.
• Clear growth opportunities as the company scales.
• Flexible paid time off.
• Annual learning and development budget.
• Comprehensive health, dental, and vision coverage.
• Frequent travel opportunities across the U.S., London, and Europe.
• Company offsites, team lunches, and monthly socials.