Senior Research Engineer – Localization & Planning
R&D
Europe, Estonia (Tallinn, Tartu, remote)
Full-time
The Mission Robust localization and motion planning in GPS-denied and contested environments is one of our core research challenges. We are looking for a Senior Research Engineer who will not just integrate existing solutions, but actively advance the state of the art for our specific operational context. Working as a highly autonomous individual contributor, you will bridge the gap between theoretical research and field-deployable code.
Expected Outcomes
Research Ownership:
Co-own the localization and planning research roadmap in direct collaboration with autonomy lead.
Rapid Delivery:
Deliver one proof-of-concept improvement to localization in GPS-denied conditions within your first 3 months.
Establish Standards:
Develop rigorous benchmarking methodologies to evaluate algorithmic performance in simulation and on hardware platforms.
Сore responsibilities
Algorithm Development:
Design, implement, and optimize algorithms for SLAM, state estimation, and motion planning to enable intelligent autonomous behavior.
System-Level Integration:
Develop robust, modular software systems interfacing with hardware components (sensors, embedded controllers) within ROS2 architectures.
Knowledge Contribution:
Summarize findings from academic papers, conduct original investigations, and apply rigorous mathematical modeling to support algorithmic design.
Cross-Functional Collaboration:
Work closely with hardware, embedded, and software teams to ensure seamless integration, while providing technical mentorship to junior engineers.
Qualifications & Competencies
Education:
MS or PhD in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field.
Experience:
5+ years in robotics-focused R&D with a strong track record of solving technical challenges end-to-end. Proven publications or an equivalent applied research track record.
Technical Stack:
Proficiency in C++ and/or Python within the ROS2 ecosystem.
Mathematical Rigor:
Deep theoretical understanding of Bayesian filtering, optimization, and differential geometry.
Domain Expertise:
Hands-on experience with SLAM, control systems, and motion planning.
Senior Research Engineer – Localization & Planning
R&D
Europe, Estonia (Tallinn, Tartu, remote)
Full-time
The Mission Robust localization and motion planning in GPS-denied and contested environments is one of our core research challenges. We are looking for a Senior Research Engineer who will not just integrate existing solutions, but actively advance the state of the art for our specific operational context. Working as a highly autonomous individual contributor, you will bridge the gap between theoretical research and field-deployable code.
Expected Outcomes
Research Ownership:
Co-own the localization and planning research roadmap in direct collaboration with autonomy lead.
Rapid Delivery:
Deliver one proof-of-concept improvement to localization in GPS-denied conditions within your first 3 months.
Establish Standards:
Develop rigorous benchmarking methodologies to evaluate algorithmic performance in simulation and on hardware platforms.
Сore responsibilities
Algorithm Development:
Design, implement, and optimize algorithms for SLAM, state estimation, and motion planning to enable intelligent autonomous behavior.
System-Level Integration:
Develop robust, modular software systems interfacing with hardware components (sensors, embedded controllers) within ROS2 architectures.
Knowledge Contribution:
Summarize findings from academic papers, conduct original investigations, and apply rigorous mathematical modeling to support algorithmic design.
Cross-Functional Collaboration:
Work closely with hardware, embedded, and software teams to ensure seamless integration, while providing technical mentorship to junior engineers.
Qualifications & Competencies
Education:
MS or PhD in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field.
Experience:
5+ years in robotics-focused R&D with a strong track record of solving technical challenges end-to-end. Proven publications or an equivalent applied research track record.
Technical Stack:
Proficiency in C++ and/or Python within the ROS2 ecosystem.
Mathematical Rigor:
Deep theoretical understanding of Bayesian filtering, optimization, and differential geometry.
Domain Expertise:
Hands-on experience with SLAM, control systems, and motion planning.
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