Deployment Engineer

HackerTrail; Singapore; On-site; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Equity participation

Equipment stipend

Travel opportunities

Learning and skills development budget

Generous leave and parental leave

AI infrastructure credits

Job summary

HackerTrail is seeking a deployment-focused robotics engineer to own end-to-end field deployments. You will work directly with robotic systems in commercial environments, translating field friction into research questions, and shaping reusable capabilities across perception, navigation, manipulation, and teleoperation.

The role involves building data collection, model adaptation, and evaluation designs, collaborating with research and platform teams to scale deployments in real-world use cases.

Qualifications

• Deep expertise in at least one of: perception, navigation, manipulation, or teleoperation.
• Experience deploying AI, robotics, autonomous, or embodied systems in real environments.
• Ability to work across disciplines when deployment challenges require it.
• Experience collecting and curating real-world deployment data for model or system improvement.
• Experience designing evaluations or benchmarks for systems in real-world settings.

Responsibilities

• Own real deployment problems end to end in variable field conditions.
• Translate deployment friction into research questions and stack improvements.
• Build and adapt systems across the deployment loop: data collection, model adaptation, evaluation design.
• Collaborate with core research and platform teams to make field learnings reusable.
• Contribute expertise across perception, navigation, manipulation, or teleoperation.
• Help shape how humanoid robots are deployed, improved, and scaled across real-world use cases.

Job description

About the role

What You Will Do Own real deployment problems end to end, from understanding workflows and operating constraints to diagnosing failures in the field. Work directly with robotic systems in commercial and industrial environments where conditions are variable and imperfect. Translate deployment friction into clear research and product questions, including missing capabilities, data needs, evaluation gaps, and stack improvements. Build and adapt systems across the deployment loop, including data collection, task-specific model adaptation or fine-tuning, and evaluation design. Work closely with core research and platform teams so field learnings become reusable capabilities rather than one-off fixes. Contribute technical expertise across areas such as perception, navigation, manipulation, or teleoperation. Help shape how humanoid robots are deployed, improved, and scaled across real-world use cases.

What you'll bring

What They Are Looking For Required skills and experience include: Deep expertise in at least one of the following areas: perception, navigation, manipulation, or teleoperation. Experience deploying AI, robotics, autonomous, or embodied systems in real environments, not only controlled laboratory settings. Ability to work beyond a single specialization when deployment challenges require it. Experience collecting, cleaning, or curating real-world deployment data for model or system improvement. Experience designing evaluations, benchmarks, or testing approaches for systems interacting with real-world environments. Strong communication skills, with the ability to explain complex technical ideas to both technical and non-technical stakeholders. High ownership and comfort working through ambiguity in fast-moving technical environments. Useful Additional Experience Experience moving between software engineering, research, operations, and user or customer conversations. Previous work in a forward-deployed, field robotics, customer-facing engineering, or similarly hands-on technical role. Experience building trust with users while maintaining a strong technical point of view. Curiosity around AI tools and a willingness to use AI as part of everyday engineering and problem-solving workflows.

Benefits and perks

Benefits Competitive compensation and equity participation. Equipment stipend to support an effective working setup. Travel opportunities connected to the work. Dedicated learning and skills development budget. Generous leave and parental leave. Support for software subscriptions and AI infrastructure or credits. Budget for language and presentation skills development.