The VinRobotics Talent Program is a hands-on, project driven journey for ambitious talents who want to build real systems and solve real problems. The program offers a personalized learning path, guided by top experts in a worldclass engineering environment, and is continuously updated to reflect the latest technologies and real market needs. 

Progress is defined by impact—what you can design, build, and deploy. Through high challenge, real-world projects, participants rapidly grow technical mastery, problem-solving mindset, and execution capability, forming a strong pipeline of elite engineers ready to shape the future of robotics with VinRobotics. In accordance with internal policies, the program offers competitive allowances, strong long-term career prospects, and opportunities to engage in exclusive internal workshops and events. 

Description 

• Work–learn model: 50% hands-on execution on real company projects, 50% deep, structured training aligned with Individual Development Plan (IDP)  

• Over 2-year-fulltime residency, depending on program track, with a long-term commitment upon completion  

• Direct 1on1 supervision by CTO / Technical Director, working closely with the company’s core leadership and key technical teams  

• Project based training aligned with VinRobotics’ strategic and mission critical initiatives, including both research and engineering tracks 

• Deep specialization training by domain, combined with cross functional exposure based on IDP  

• Tech Leadership Program for high potential candidates, focusing on technical leadership and system ownership  

• Paper submission & presentation to top-tier conferences 

Benefits 

• Priority pathway toward Senior Engineer or Senior Management positions 

• Compensation & benefits:  

  • Supervised by experienced mentors having PhDs/MSc at top universities in US/EU/JP/KR/SG/Aus/UK, or worked for big tech corps. 

  • Full benefits applied in line with VinRobotics’ official employee policy framework 

  • Competitive starting salary 

  • Performance based bonus scheme

Required Qualifications 

• Outstanding early career talents with a clear orientation toward deep technical specialization and long-term development  

• Bachelor’s or Engineer’s degree with High Distinction or above, or Master’s degree (research track preferred) in Robotics, AI, Electrical/Electronics, Mechanical Engineering, Computer Science, or related fields 

• Proven experience in serious research or advanced technical projects, with technical reports, papers, or preprints; solid understanding of research methodology and evaluation 

• Strong advantage for candidates with publications, posters, patents, or high impact opensource contributions at leading journals or conferences 

• Letter of recommendation from a reputable faculty member, lab lead, or technical lead 

Preferred Qualifications 

• Award winners at national or international competitions in Robotics, AI, or Engineering (e.g., RoboCup, VEX, FPT AI, ICPC, IEEE competitions)  

• Background in IMO / IPhO / IOI is a significant advantage 

Development Plan 

• Reasearch track: Over 2 years, each resident is expected to complete 2–3 major works, with expected outputs including: 2–3 paper submissions to top-tier conferences and/or 1–2 high-impact patents. The first 6–9 months should focus on foundation building and the first major work; the second year should emphasize deeper ownership, stronger technical independence, and higher-impact outputs 

• Engineering track: Over 2 years, each resident is expected to complete 2–3 major engineering deliverables, such as validated core modules, system integration milestones, deployment-ready features, or reliability/performance improvement packages with clear product or deployment impact. The first 6–9 months should focus on technical ramp-up and one major deliverable; the second year should emphasize end-to-end ownership, cross-functional integration, and larger system-level impact. 

A detailed roadmap will be provided at a later stage. 

Scopes 

• Robotic perception: Pose estimation, object detection, scene understanding, visual SLAM support, grasp prediction learning, sensor fusion, foundation models for perception. 

• SLAM / Navigation: Localization, mapping, semantic mapping, navigation planning, robustness in dynamic environments. 

• Teleoperation & simulation: Teleoperation systems, motion retargeting, demonstration collection, simulation tooling, digital twins/real2sim, sim-to-real support. 

• Robotic foundation models: VLA/VLM, embodied agents, Agentic AI, long-horizon reasoning, edge deployment, and optimization. 

• RL / Imitation Learning / LfD: Behavior cloning, reinforcement learning, offline RL, reward design, policy evaluation, benchmark development. 

• Robotic SW / Control / MPC: Robot software stack, whole-body control, trajectory generation, runtime integration, MPC, real-time reliability. 

• Hardware / EE: New hardware design, tactile hands and gloves, miniaturized actuators, embedded interfaces, sensor and subsystem prototyping.