New opportunity

Robotics-AI Engineer

DSTA · Singapore

About this role

DSTA seeks a Robotics-AI Engineer in Singapore to develop, integrate and deploy AI-driven autonomy capabilities for robotic platforms operating in complex, multi-domain, unstructured environments. The role spans perception, navigation, multi-robot and swarm coordination, and learning-based robotics, with an emphasis on turning research into deployable systems.

Responsibilities

Design and develop perception, localisation, mapping, navigation and mission-autonomy capabilities.
Explore deep learning, reinforcement learning and foundation-model-based approaches to robotics problems.
Lead software and hardware integration, and test robotic systems through simulation and field trials locally and internationally.
Analyse autonomy-stack and algorithm performance to improve robustness, reliability and real-time autonomous behaviour.
Collaborate with robotics, AI and systems-engineering specialists and researchers globally.

Requirements

A degree or master's in Robotics, AI, Computer Science, Computer Engineering or a related discipline; the posting also lists a bachelor's degree or equivalent as its qualification.
0 ~ 5 years of experience, with hands-on robotics development covering software and hardware integration, robotics frameworks such as ROS 2, and simulation platforms.
Strong programming skills, preferably in Python and/or C++, along with communication, teamwork and an interest in open-ended autonomy problems.
Experience deploying algorithms on real robotic platforms is highly desirable.

The position is full-time, has standard working hours and is listed under the Digital Hub.

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

Skills for this role

RoboticsRobot perceptionLocalisationMappingAutonomous navigationMission autonomyMulti-robot coordinationSwarm roboticsDeep learningReinforcement learningFoundation modelsROS 2Robotics simulationPythonC++Software and hardware integrationAlgorithm deploymentPerformance analysisField testingCommunicationTeamwork

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

Build the autonomy stack
The role develops perception, localisation, mapping, navigation and mission-autonomy capabilities. Prepare to explain how these components work together on a robotic platform, using a concrete technical example if available.
Turn AI research into deployable robotics
The posting calls for exploring deep learning, reinforcement learning and foundation-model-based approaches while translating research into robust capabilities. Prepare to discuss how you would evaluate an approach beyond a research result.
Own integration and testing
The engineer leads software and hardware integration and tests systems in simulation and field trials, locally and internationally. Prepare to describe an integration or testing workflow and how findings would drive changes.
Improve operational performance collaboratively
The role analyses autonomy-stack performance for robustness, reliability and real-time behaviour, working with robotics, AI and systems-engineering specialists and researchers. Prepare to explain how you would communicate a performance issue across disciplines.

Likely challenges to think through

Closing the simulation-to-field gap
A likely challenge is that behaviour validated in simulation may change on physical platforms in unstructured environments. Prepare to discuss what you would measure during field trials and how you would use discrepancies to revise the system.
Balancing AI capability with dependable execution
Learning-based approaches may offer useful autonomy gains while making reliability and real-time performance harder to maintain. Prepare to explain how you would compare approaches against those deployment needs; this is a preparation hypothesis, not a stated DSTA problem.
Coordinating autonomy across robots
Because the role spans multi-robot and swarm coordination, a plausible difficulty is maintaining useful collective behaviour when conditions or information differ across platforms. Prepare to describe how you would test coordination and assess failures without assuming a specific deployed system.

Practice your answers

Technical practice

How would you design perception for a robot in an unstructured environment?

What to coverThe role includes robot perception in complex, unstructured environments. Preparation suggestion: explain how you would assess perception quality under changing conditions and handle uncertain or missing observations.

How would you evaluate localisation and mapping during autonomous operation?

What to coverLocalisation and mapping are stated areas of work. Preparation suggestion: discuss how you would detect drift or inconsistent maps, diagnose likely causes and measure whether a change improves performance.

How would you approach autonomous navigation when conditions change?

What to coverThe posting calls for autonomous navigation and robust real-time behaviour. Preparation suggestion: describe how a navigation system could respond to unexpected obstacles, balancing progress, reliability and computation time.

How would you structure mission autonomy for a robotic platform?

What to coverMission autonomy is an advertised capability. Preparation suggestion: explain how a system could choose actions, monitor whether they succeed and respond when a planned action cannot be completed.

What would you evaluate in a multi-robot coordination approach?

What to coverThe role spans multi-robot and swarm coordination. Preparation suggestion: compare coordination choices in terms of communication needs, resilience to a robot dropping out and the ability to complete a shared objective.

How would you choose among deep learning, reinforcement learning and foundation-model-based approaches?

What to coverThe posting explicitly names all three as approaches to explore for robotics problems. Preparation suggestion: discuss what evidence would justify each approach, including data or training needs, reliability and deployability on a robot.

How would you integrate autonomy software with robot hardware using a framework such as ROS 2?

What to coverThe role requires software and hardware integration and cites ROS 2 as an example framework. Preparation suggestion: walk through component interfaces, timing and data-flow checks, and how you would isolate a fault across the integration boundary.

How would you move an autonomy capability from simulation to a field trial?

What to coverThe posting calls for testing from simulation through local and international field trials. Preparation suggestion: explain what simulation can validate, what must be checked on a real platform and how you would use trial observations to revise the system.

How would you diagnose poor reliability or real-time performance in an autonomy stack?

What to coverAnalysing stack and algorithm performance is a stated responsibility. Preparation suggestion: identify useful measurements, show how you would locate a bottleneck or failure mode, and weigh an algorithmic improvement against its runtime cost.

How would you turn a research result into deployable robotics code?

What to coverThe role emphasises translating research into deployable capabilities and prefers Python and/or C++ programming skills. Preparation suggestion: describe how you would establish a baseline, implement and test the approach, and decide whether its measured gains justify integration.

Behavioural practice

Tell me about a time you worked across technical specialties to solve a problem.

What to coverThe role involves collaboration with robotics, AI and systems-engineering specialists. Preparation suggestion: use a real situation, your actions to align different perspectives, and the resulting technical or team outcome.

Describe a time an integration issue required coordinated troubleshooting.

What to coverLeading software and hardware integration is part of the advertised work. Preparation suggestion: explain the situation, how you narrowed down the issue and worked with others, and what the result showed.

Tell me about a time a test exposed a gap between expectations and real-world behaviour.

What to coverThe posting includes simulation and field testing. Preparation suggestion: describe the test context, the evidence you gathered, the change you made and the observed result; do not claim field experience you do not have.

Describe how you approached an open-ended technical problem you needed to learn about.

What to coverThe posting values curiosity and tackling open-ended autonomy problems. Preparation suggestion: give a real example of how you framed the uncertainty, learned or experimented, and reached a useful result.

Tell me about a time you communicated a technical trade-off to collaborators.

What to coverStrong communication and teamwork are stated requirements, alongside performance and deployment work. Preparation suggestion: set out the decision, how you explained the evidence and alternatives, and what happened as a result.

Sharpen your knowledge

Topics to prepare

Perception, localisation and mapping
Posting: The role develops these autonomy capabilities. Suggested revision exercise: explain how uncertain sensor observations affect a robot’s position estimate and map, then describe how you would diagnose a localisation failure.
Navigation and mission autonomy
Posting: The role covers autonomous navigation and mission autonomy in complex, unstructured environments. Suggested revision exercise: sketch how a robot would choose a route, respond to an unexpected obstacle and determine whether its mission should continue.
Multi-robot and swarm coordination
Posting: The overview identifies multi-robot and swarm coordination as areas of work. Suggested revision exercise: compare two ways to assign tasks among robots when communication is intermittent, including what happens if one robot drops out.
Learning-based robotics
Posting: The role explores deep learning, reinforcement learning and foundation-model-based approaches. Suggested revision exercise: choose a robotics problem and explain which approach you would investigate, what data or feedback it would need, and how you would check whether it works beyond a controlled setting.
Autonomy-stack robustness and real-time behaviour
Posting: The engineer analyses stack and algorithm performance to improve robustness, reliability and real-time behaviour. Suggested revision exercise: reason through a case where delayed perception updates cause poor navigation decisions; identify measurements and possible mitigations.

Skills to revise

ROS 2 robotics development
Posting: Experience with robotics frameworks such as ROS 2 is requested. Suggested revision exercise: build or revisit a small ROS 2 component that receives robot-state data and publishes a decision; verify its message flow and handling of missing updates.
Python or C++ implementation
Posting: Strong programming skills, preferably Python and/or C++, are requested. Suggested revision exercise: implement a simple navigation or perception routine in one of these languages, then test edge cases and explain its runtime trade-offs.
Software and hardware integration
Posting: Hands-on integration is requested, and deploying algorithms on real robotic platforms is highly desirable. Suggested revision exercise: map an autonomy algorithm’s inputs and outputs to a robot’s sensors and controls, then outline checks for timing, data validity and safe failure. This is preparation, not a claim that a particular platform is required.
Simulation, field testing and performance analysis
Posting: The role tests systems in simulation and field trials and analyses algorithm performance. Suggested revision exercise: draft a test plan for one navigation behaviour with simulation scenarios, field observations, performance measures and criteria for investigating discrepancies.

Your preparation checklist

Prepare a robotics project walkthrough
The role covers perception, localisation, mapping, navigation and mission autonomy. If you have a relevant project, prepare a five-minute account of the problem, your contribution, system architecture, results and limitations.
Sketch an autonomy design for an unstructured environment
As interview practice, diagram how sensing, localisation, planning and mission decisions would connect. Explain likely failure modes and how you would test them; this is a preparation exercise, not a stated interview task.
Gather integration and programming evidence
The posting asks for hands-on software and hardware integration, experience with frameworks such as ROS 2 and simulation platforms, and preferably Python and/or C++. Select code, diagrams or test notes you can explain accurately, where available.
Prepare a simulation-to-field testing example
The role includes simulation and field trials; deployment on a real robot is highly desirable. If you have such experience, show what changed between simulated and physical tests, how you diagnosed issues and what you could verify.
Explain one learning-based robotics choice
The posting mentions deep learning, reinforcement learning and foundation-model-based approaches. Choose a relevant example or design comparison and be ready to discuss why an approach fits, its data needs and its deployment constraints.
Define performance measures and employer questions
Prepare measures for reliability, robustness and real-time behaviour, which the role explicitly emphasises. Ask how the team evaluates autonomy stacks, coordinates with robotics and systems-engineering specialists, and moves research into deployable capabilities.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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