Jobs at MicroAGI

Own high-volume manufacturing recruiting across European talent pools, including factory technicians and facilities managers. Build employer brand, advise on scalable recruiting practices, develop talent pipelines, and represent microagi at industry events. The role requires deep manufacturing hiring expertise, strong agency, German and English fluency, and comfort working in a fast-paced, in-person startup environment.

Own full-cycle enterprise sales for physical AI and robotic deployments in the DACH region. Build pipeline, identify industrial customers, manage complex stakeholder groups, conduct site visits and technical deployment assessments, negotiate contracts, and close initial deals. Develop the sales playbook, pricing approach, and target market while feeding customer insights back to product, research, and deployment teams. Requires fluent German and English, Munich office presence, and regular customer-site travel.

Own the full enterprise sales cycle for physical AI deployments in the UK, from pipeline creation and discovery through technical assessments, site visits, negotiations, and signed contracts. Build relationships across plant leadership, operations, safety, unions, procurement, and executives. Develop the sales playbook, validate customer use cases, communicate product capabilities honestly, and feed customer insights back to product, research, and deployment teams. The role is based in London with regular customer-site travel.

Own firmware for sensor, motion-capture, IoT, and prototype-robot hardware. Build real-time data acquisition, drivers, synchronization, calibration, timestamping, communication, logging, diagnostics, testing, and OTA updates. Collaborate with electrical and ML engineers to deliver reliable, high-quality training data. The role requires strong C/C++, RTOS, microcontroller, peripheral-protocol, sensor-integration, and hardware-debugging expertise, plus curiosity about machine learning and comfort working independently in ambiguous environments.

Own the end-to-end hardware roadmap for wearable, sensor, camera, and prototype robotics systems. Lead electrical, firmware, and mechanical architecture; remain hands-on with schematics, board bring-up, reviews, and debugging. Build and grow the hardware team, ensure high-quality synchronized data, and take products from prototype through small-batch production, certification, and field deployment. Manage vendors and contractors while collaborating closely with machine learning and deployment teams.

Train and deploy neural robotic policies for manipulation and embodied tasks using real and simulated data. Build data, training, evaluation, and deployment pipelines; run simulation experiments; transfer policies to real robots; collect demonstrations; diagnose failures; and improve robustness, generalization, latency, and task success. Collaborate with deployment engineers at customer sites to adapt robotic systems to new environments and workflows.

Designs and integrates hardware for embodied-AI data capture, including sensors, PCBs, motion-capture rigs, IoT systems, and prototype robots. Owns embedded firmware, real-time data collection, sensor synchronization, calibration, and timestamping. Collaborates with machine-learning engineers to ensure high-quality training data, rapidly prototypes hardware, debugs electronics, and supports progression from prototypes to small-batch production.

Own enterprise outreach and first contact for humanoid robot deployments. Generate and qualify leads, manage a market or territory end to end, develop outreach strategies, improve channels and messaging, book qualified meetings, and provide context to the closing team. The role requires high agency, hands-on prospecting, fluent proficiency in two languages, and in-office work in Munich.

Designs and operates petabyte-scale cloud infrastructure for ingesting, processing, storing, and serving multimodal data. Builds pipelines that transform device uploads into training-ready datasets, improves platform cost, speed, and reliability, and partners with ML and device teams to eliminate infrastructure bottlenecks. The role requires hands-on experience with TB-to-PB data processing, distributed systems, object storage, streaming, columnar formats, orchestration, and cloud infrastructure.