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Mecka AI is building the data infrastructure layer for robotics and embodied AI. The company designs and operates global systems for data capture, data labeling, and hardware-enabled workflows used by leading AI labs and robotics companies to train and validate humanoid and embodied AI systems.
You will lead experimental methods and scientific operations for automated laboratory work, combining hands-on laboratory experience with understanding of AI, robotics, and automation. Working directly with instruments, protocols, and experimental results alongside scientists and engineering specialists, you will establish sound scientific methods, troubleshoot practical problems, and turn promising workflows into dependable operations.
Key responsibilities include:
- Owning experimental methods: selecting and adapting protocols, designing controls, and defining scientific acceptance criteria
- Developing automated workflows by assessing and qualifying instruments, labware, and automation with engineering specialists
- Establishing scientific validity through evaluating results, investigating variability, and distinguishing execution problems from scientific failures
- Leading laboratory operations including planning staffing, equipment, materials, and operating requirements; establishing training, documentation, and safe working procedures
- Troubleshooting hands-on by diagnosing problems at the bench and across instruments, hardware, and software; coordinating technical fixes and verifying effects
- Improving operating performance by assessing reproducibility, throughput, manual effort, and cost; identifying methods ready for routine use and areas needing further development
REQUIREMENTS:
- Substantial hands-on experimental work with ownership of methods, controls, reproducibility, and interpretation
- Experience running automated laboratory operations, including instruments, resources, staffing, and quality procedures
- Practical experience with AI and robotics in laboratory automation or experimental systems
- Working knowledge of reinforcement learning and model evaluation sufficient to contribute to scientifically meaningful tasks and assessments
- Ability to work directly at the bench and with instruments, and translate scientific requirements into specifications for engineers
PREFERRED:
- Experience building RL environments, robotic benchmarks, or model-evaluation tools
- Scientific leadership in a contract research organization, research core, or shared instrumentation facility
- Track record improving experimental reliability, turnaround, or cost across different operators, instruments, or operating conditions
