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Robotics Engineer - Singapore: Field Robotics Specialist

General Robotics · Singapore

About this role

General Robotics is seeking a Robotics Engineer with a Field Robotics specialization to join their Singapore team. This role focuses on integrating technology onto physical hardware and leading customer deployments, contributing to the end-to-end development platform (GRID). Responsibilities include designing and implementing robotics, control, and machine learning algorithms for autonomous systems, performing system integration with edge compute and sensor suites, and deploying AI models on physical hardware. The engineer will optimize systems for constraints like bandwidth, compute, and latency, diagnose technical issues in real-world scenarios, and collaborate with AI and Simulation Engineers. Additionally, the role involves acting as a technical liaison for customers and identifying opportunities for scaling value. Minimum qualifications include an undergraduate or Master’s degree in Robotics, Computer Science, or a related field, 2+ years of experience in robotics, machine learning, or control algorithms, and proficiency in ROS/ROS2, Python, and C++. Candidates must have experience with end-to-end robot learning systems and the ability to travel to customer sites. Desired qualifications include a track record of deploying controllers and algorithms on multiple platforms, experience with constrained edge compute devices like NVIDIA Jetson, and familiarity with security protocols for sensitive data. The position offers a base salary range of 82,000 SGD to 220,000 SGD based on experience.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

RoboticsMachine LearningControl AlgorithmsROSROS2PythonC++Robot Learning SystemsEdge ComputingNVIDIA JetsonData AcquisitionModel Inference

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End System Integration
The role requires independently setting up robots for real-world deployments, which involves integrating edge compute and sensor suites. Success depends on your ability to bridge the gap between high-level AI models and physical hardware constraints.
Edge Compute Optimization
You must optimize AI models and control algorithms for constrained hardware like NVIDIA Jetson. This requires balancing performance metrics such as latency, bandwidth, and compute power to ensure reliable operation in the field.
Customer-Facing Technical Liaison
As a field specialist, you act as the primary technical bridge between customers and internal research teams. You are expected to diagnose real-world issues and identify opportunities to scale value for the client.

Likely challenges to think through

Bridging Simulation to Reality
Transitioning algorithms from simulation to physical hardware often reveals discrepancies in sensor noise or environmental variables. Prepare by discussing how you validate models in controlled environments before field deployment and your process for iterative debugging.
Managing Real-World Constraints
Field environments lack the stability of a lab. You will face unpredictable hardware failures or network limitations. Prepare by highlighting your experience in remote diagnostics and your methodology for maintaining system stability under resource-constrained conditions.

Practice your answers

Technical practice

ROS2 Architecture and Middleware

What to coverExplain how you have utilized ROS2 for system integration in previous projects. Focus on your experience with node communication, lifecycle management, and how you handle real-time constraints within the ROS2 ecosystem.

Optimizing AI Models for Edge Compute

What to coverDescribe your approach to deploying machine learning models on constrained hardware like the NVIDIA Jetson. Discuss techniques for model optimization, such as quantization or pruning, to manage latency and compute limitations.

End-to-End Robot Learning Systems

What to coverDiscuss your experience in building and evaluating end-to-end robot learning pipelines. Address how you handle data acquisition, training, and the transition from simulation to physical hardware deployment.

Troubleshooting Field Deployments

What to coverProvide an example of how you diagnose complex issues when a robot fails in a real-world environment. Explain your methodology for isolating faults between hardware, sensor suites, and software algorithms.

C++ and Python for Robotics

What to coverCompare the use of C++ and Python in your robotics development workflow. Explain how you decide which language to use for performance-critical control loops versus high-level logic or data processing.

Sensor Suite Integration

What to coverDetail your process for integrating diverse sensor suites onto a robotic platform. Discuss how you handle data synchronization, calibration, and noise filtering to ensure reliable input for autonomous systems.

System Constraints and Trade-offs

What to coverWhen designing autonomous systems, how do you balance trade-offs between bandwidth, compute power, and latency? Provide an example of a design decision where you had to prioritize one constraint over another.

Security Protocols for Sensitive Data

What to coverGiven the requirement to handle sensitive data, what security practices do you implement in your robotics software? Discuss your experience with secure data transmission or storage in field-deployed systems.

Behavioural practice

Technical Liaison Experience

What to coverDescribe a situation where you acted as a technical bridge between a customer and an internal research or engineering team. How did you translate customer feedback into actionable technical requirements?

Cross-functional Collaboration

What to coverGive an example of a time you collaborated with simulation engineers to improve a real-world deployment. How did you ensure that metrics from the field were effectively communicated back to the simulation team?

Scaling Customer Value

What to coverTell me about a time you identified an opportunity to scale value for a customer beyond the initial project scope. What steps did you take to validate this new use case and implement it?

Adapting to Field Challenges

What to coverDescribe a time when a deployment did not go as planned due to unexpected environmental factors. How did you adapt your strategy on-site to ensure the project's success?

Sharpen your knowledge

Topics to prepare

Edge Computing and System Constraints
The role requires optimizing AI models for bandwidth, compute, and latency on hardware like NVIDIA Jetson. Exercise: Review your past projects to identify specific bottlenecks in edge deployment and prepare to explain how you balanced model accuracy against hardware resource limitations.
End-to-End Robot Learning Systems
The position involves deploying AI models on physical hardware and managing the full lifecycle of robot learning. Exercise: Prepare a case study detailing a project where you moved a model from simulation or development to a physical robot, focusing on the challenges of real-world data acquisition and performance evaluation.

Skills to revise

ROS/ROS2 Development
Proficiency in ROS/ROS2 is a core requirement for system integration. Exercise: Practice creating a custom ROS2 node that handles sensor data acquisition and publishes control commands, ensuring you can explain your choice of communication patterns and node architecture.
C++ and Python for Robotics
The role requires implementing algorithms in both languages. Exercise: Review your code for a robotics project and be prepared to discuss why you chose C++ versus Python for specific modules, particularly regarding performance-critical control loops versus high-level logic.
Field Deployment and Troubleshooting
The role involves leading customer deployments and diagnosing issues in real-world scenarios. Exercise: Draft a structured approach for troubleshooting a robot that fails during a field test, covering diagnostic steps for hardware, sensor, and AI model performance issues.

Your preparation checklist

Prepare a portfolio of field deployment case studies
Document specific instances where you integrated robotics hardware with edge compute devices. Focus on how you handled real-world constraints like latency, bandwidth, and compute limitations, and explain how you diagnosed and resolved technical issues during these deployments.
Demonstrate proficiency in ROS2 and C++/Python
Prepare code samples or a walkthrough of a project that utilizes ROS2 for robot control or data acquisition. Highlight your ability to write efficient, production-ready code that runs on constrained hardware like NVIDIA Jetson.
Develop a narrative for end-to-end robot learning
Be ready to discuss your experience with the full lifecycle of robot learning systems, from data collection and model training to deployment on physical hardware. Explain your methodology for evaluating model performance in the field.
Formulate questions about the GRID platform and customer integration
Prepare thoughtful questions regarding the architecture of the GRID platform, how the Singapore team collaborates with the US-based research team, and the specific security protocols required when handling sensitive customer data.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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