Asks for

The ad says“Deep Planning Expertise: Mastery of planning algorithms (graph search, sampling-based, and optimization-based methods), route planning, and decision-making frameworks (behavior trees, FSMs, RL).”
The ad says“Theoretical Foundations: Strong background in optimization, probabilistic reasoning, or control theory. Proven ability to formulate and solve complex autonomy problems using rigorous mathematical principles.”
The ad says“Deep Planning Expertise: Mastery of planning algorithms (graph search, sampling-based, and optimization-based methods), route planning, and decision-making frameworks (behavior trees, FSMs, RL).”
The ad says“Deep Planning Expertise: Mastery of planning algorithms (graph search, sampling-based, and optimization-based methods), route planning, and decision-making frameworks (behavior trees, FSMs, RL).”
The ad says“Deep Planning Expertise: Mastery of planning algorithms (graph search, sampling-based, and optimization-based methods), route planning, and decision-making frameworks (behavior trees, FSMs, RL).”
The ad says“Theoretical Foundations: Strong background in optimization, probabilistic reasoning, or control theory. Proven ability to formulate and solve complex autonomy problems using rigorous mathematical principles.”
The ad says“Deep Planning Expertise: Mastery of planning algorithms (graph search, sampling-based, and optimization-based methods), route planning, and decision-making frameworks (behavior trees, FSMs, RL).”
The ad says“Deep Planning Expertise: Mastery of planning algorithms (graph search, sampling-based, and optimization-based methods), route planning, and decision-making frameworks (behavior trees, FSMs, RL).”
The ad says“Theoretical Foundations: Strong background in optimization, probabilistic reasoning, or control theory. Proven ability to formulate and solve complex autonomy problems using rigorous mathematical principles.”
The ad says“Deep Planning Expertise: Mastery of planning algorithms (graph search, sampling-based, and optimization-based methods), route planning, and decision-making frameworks (behavior trees, FSMs, RL).”
The ad says“Experience with Real-world Systems: Hands-on experience with ROS (Robot Operating System) and real-world deployment of autonomous systems or robots.”
The ad says“Deep Planning Expertise: Mastery of planning algorithms (graph search, sampling-based, and optimization-based methods), route planning, and decision-making frameworks (behavior trees, FSMs, RL).”
The ad says“Deep Planning Expertise: Mastery of planning algorithms (graph search, sampling-based, and optimization-based methods), route planning, and decision-making frameworks (behavior trees, FSMs, RL).”

A world empowered by autonomy. We build robotic vehicles to improve logistics safety, forge a greener Earth, and enhance human lives.

We are a closely-knit team aspiring to change the world through disruptive technology. We are innovators. We are tinkerers. We are problem-solvers. And we have a fair amount of magic dust up our sleeves. We have a plan for fleet-level deployment of autonomous vehicles, and we are looking for the best-of-the-best to join us in making this a reality.

About Venti Technologies

Based in the U.S. and Asia, Venti Technologies is the leader in safe-speed autonomous logistics systems, developing the future of goods transportation. Using rigorous mathematics, deep learning, and theoretically-grounded algorithms, Venti has a proprietary collection of autonomy technologies including a suite of powerful logistics algorithms. Venti’s proven value proposition of saving costs, increasing vehicle utilization, and improving safety is recognized by customers and driving growth. Launched in 2018, Venti brings together an unsurpassed team internationally. The company has autonomous systems deployed in Asia for industrial and logistics sites and a growing pipeline. Venti has offices in Cambridge (Massachusetts, USA), Suzhou (China), and Singapore – our Asian headquarters.

Location

Singapore

Position overview

We are seeking a Staff Planning Research Scientist to lead the technical direction and system architecture for planning within our autonomy stack. In this role, you will drive the research roadmap, guide algorithmic innovation, and define how planning interacts with perception, prediction, and control systems to achieve safe, efficient, and scalable autonomy.

You will be responsible for identifying high-impact research directions, validating novel approaches through simulation and deployment, and mentoring the team to translate advanced theory into robust, real-world systems. This is a high-influence position suited for an individual who thrives at the intersection of deep technical research and system-level autonomy design.

Role responsibilities

Technical Leadership: Define the long-term research and development roadmap for planning within the autonomy stack. Identify key challenges in decision-making, trajectory generation, and optimization for real-world logistics operations.
Algorithm Development: Design, analyze, and validate advanced route, behavior, and motion planning algorithms. Push the boundaries of autonomous planning through novel approaches in optimization, learning, and uncertainty modeling.
System Integration: Lead integration of planning algorithms with perception, prediction, localization, and control systems. Anticipate system-level trade-offs and guide architectural decisions for reliable deployment.
Research to deployment: Evaluate academic and industrial research trends, prototype promising ideas, and guide their evolution into production-ready systems. Balance long-term research innovation with near-term deployment needs.
Performance and Robustness Analysis: Establish methods to evaluate planning performance under complex and uncertain real-world conditions. Identify bottlenecks and develop strategies to enhance reliability, safety, and efficiency.
Mentorship and Team Development: Provide technical guidance to researchers and engineers, fostering a culture of innovation, ownership, and delivery.
External Awareness: Stay informed on advances in planning, decision-making, and learning for autonomy. Represent Venti in research discussions, conferences, and technical collaborations where appropriate..

Required experience

Educational Background: Ph.D. or Master’s degree in Computer Science, Robotics, Engineering, or a related field, with a focus on route planning, motion planning, or autonomous systems.
Deep Planning Expertise: Mastery of planning algorithms (graph search, sampling-based, and optimization-based methods), route planning, and decision-making frameworks (behavior trees, FSMs, RL).
Theoretical Foundations: Strong background in optimization, probabilistic reasoning, or control theory. Proven ability to formulate and solve complex autonomy problems using rigorous mathematical principles.
Experience with Real-world Systems: Hands-on experience with ROS (Robot Operating System) and real-world deployment of autonomous systems or robots.
Leadership and Mentorship: Experience leading technical projects, mentoring engineers, and contributing to organizational growth.
Collaboration & Communication: Excellent communication skills for working across multidisciplinary teams and influencing technical direction.