Field Engineer - California

📋 Description

• • As a Field Engineer based in the vibrant San Francisco or broader California area, you will be instrumental in ensuring the technical success and operational excellence of cutting-edge wearable data collection hardware. This role is a critical bridge between our advanced technical development team and the real-world deployment of our physical AI models, focusing on gathering high-fidelity, multimodal data from human activities in diverse environments such as factories, kitchens, and warehouses.
• • Your primary responsibility will be to rigorously test and validate our wearable devices, ensuring their robustness and reliability across a spectrum of challenging conditions. This involves conducting comprehensive stress tests on multiple devices simultaneously, exposing them to varying degrees of speed, temperature, humidity, dust, and lighting to identify potential failure points and performance limitations.
• • A significant part of your day-to-day will involve hands-on, on-site debugging. You will be tasked with troubleshooting issues related to data capture, device connectivity, ergonomic fit, battery life, and software stability in real-time. Your ability to minimize downtime and ensure continuous data collection will be paramount to the project's success.
• • You will play a key role in the iterative design and optimization of our Standard Operating Procedures (SOPs). This includes collaborating with the operational team to engineer the most efficient workflows for charging, syncing, and deploying multiple wearables. Your insights will directly contribute to streamlining field operations and maximizing productivity.
• • Feedback collection and product iteration are central to this role. You will gather detailed user feedback from human demonstrators regarding the comfort, weight, potential interference, and heat dissipation of the wearables during extended use. This qualitative data, combined with your technical observations, will be crucial for informing hardware and software improvements.
• • You will be responsible for creating structured, detailed testing reports. These reports will serve as the foundation for prioritizing key fixes, feature requests, and design modifications, ensuring that the engineering team has clear, actionable insights to drive product development.
• • Acting as an early adopter, you will validate new software features and hardware modifications before they are deployed to the wider operator fleet. This proactive testing ensures that new releases are stable, functional, and meet the demanding requirements of real-world use.
• • Monitoring and analyzing incoming data streams for quality issues will be another vital task. You will share your findings and insights with the technical team, helping them to identify and resolve data quality problems, thereby enhancing the overall integrity of the collected datasets.
• • Furthermore, you will contribute to the training and enablement of non-technical operators. This involves developing clear, concise technical training manuals and conducting hands-on sessions to educate operators on proper hardware handling, maintenance, and basic troubleshooting.
• • This role offers a unique opportunity to work at the forefront of physical AI development, contributing directly to the creation of diverse datasets that power advanced AI models. You will gain invaluable experience in hardware-software integration, field operations, and product development within a dynamic startup environment.
• • The position requires a commitment of a minimum of 20 hours per week, with the potential to scale up to 40 hours per week, offering flexibility to suit your schedule. The project duration is estimated at 2+ months, with the possibility of extension based on performance and project needs.
• • This is a hands-on role for someone who thrives in dynamic environments, enjoys problem-solving, and is passionate about the intersection of hardware, software, and real-world data collection. Your contributions will directly impact the quality and quantity of data used to train sophisticated AI models.