Member of Technical Staff

About the role

Moonlake is developing the foundational infrastructure for AI-driven world simulation to advance robotics and embodied intelligence. As a Member of Technical Staff at Embedding Vc, you will join a team focused on bridging the gap between simulated environments and physical robot deployment. This role centers on creating the critical link between virtual testing and real-world operation. You will own the core systems that ensure simulation fidelity translates into reliable robot behaviors. Your work will directly influence the capabilities of next-generation embodied intelligence platforms. You will be responsible for maintaining the robustness and scalability of the simulation infrastructure. This position requires a deep commitment to solving hard problems at the intersection of software and hardware. You will play a key role in defining the engineering standards for the Moonlake stack. The successful candidate will operate at the forefront of the robotics simulation stack, ensuring that high-fidelity virtual testing reliably produces safe and effective behaviors in the physical world.

What you'll do

• Benchmark robotic foundation models and policies within simulated environments to establish quantifiable performance baselines and identify systemic limitations.
• Create evaluation frameworks that rigorously test reasoning, navigation, and manipulation capabilities across diverse, real-world inspired scenarios.
• Train world models using multimodal data streams, integrating depth, vision, and detailed robot state telemetry to predict future states and optimize actions.
• Build robust, scalable pipelines for sim-to-real transfer that systematically close the reality gap and improve model performance on physical hardware.
• Manage the full lifecycle of integration, maintenance, and deployment of complex physical robotic systems, from initial setup through continuous iteration.
• Debug intricate, cross-domain issues that span sensor hardware layers, real-time control software, and adaptive machine learning models.
• Instrument simulation environments to capture high-fidelity telemetry for long-term analysis, enabling data-driven iteration and model refinement.
• Optimize simulation execution pipelines to achieve maximum throughput and determinism, ensuring stability for large-scale training and evaluation runs.
• Collaborate closely with research and hardware teams to translate theoretical concepts into executable engineering specifications and validation tests.
• Implement comprehensive monitoring and logging solutions that provide deep visibility into the interaction between software control loops and physical hardware states.
• Design and maintain extensible interfaces that allow for the seamless integration of new sensors, robotic platforms, and third-party software tools.
• Ensure the stability, security, and reproducibility of development and production environments used for continuous testing and deployment.
• Lead the development and enforcement of best practices for data management, versioning, and lineage tracking across massive simulation datasets.
• Drive the adoption of standardized tooling and workflows to improve consistency, efficiency, and knowledge sharing across the engineering organization.
• Instrument the simulation stack to measure and report on key performance indicators relevant to robotics deployment, including latency, determinism, and fidelity metrics.
• Partner with research scientists to prototype novel algorithms and translate academic insights into production-grade simulation components.
• Validate hardware-in-the-loop configurations to confirm that simulated behaviors align with physical constraints and sensor noise profiles.

Requirements

• Background in robotics, machine learning, or embodied AI is mandatory for this position, with a demonstrated ability to apply theory to practical systems.
• Practical, hands-on experience working with physical robotic hardware is a strict requirement for eligibility, including sensor calibration and actuator control.
• Proficiency in Python and standard robotics software tools must be demonstrated through prior work on complex, real-world projects.
• Experience with simulation platforms like NVIDIA Isaac Sim, MuJoCo, Meta Habitat, or Gazebo is essential for modeling realistic dynamics and sensor noise.
• Ability to troubleshoot across the full software and hardware stack - from low-level sensor drivers to high-level control policies - is non-negotiable.
• Strong understanding of software engineering principles, including version control (e.g., Git), testing frameworks, and code review processes, is required.
• Capacity to work effectively in a fully on-site role within the San Francisco Bay Area, collaborating daily with cross-functional engineering teams.
• Clear communication skills to articulate technical trade-offs and collaborate with stakeholders across engineering, research, and product functions.
• Willingness to engage with cutting-edge tooling and contribute to the definition of internal standards for simulation, validation, and deployment.
• Commitment to rigorous experimentation and analysis, ensuring that simulation results are actionable and reliably inform hardware behavior.
• Demonstrated ownership of complex technical tasks, including root cause analysis, performance optimization, and long-term system maintainability.
• Alignment with the company mission to advance robotics and embodied intelligence through high-fidelity simulation and real-world deployment.

Practical notes

This position is strictly on-site in San Francisco. The company has secured 28 million dollars in seed funding from investors including NVIDIA Ventures, Threshold Ventures, AIX Ventures, Naval Ravikant, and Jeff Dean.