Robotics Engineer
DexSent Robotics — Chennai
₹4–6 LPA (HireSetu estimate) · Entry level · Freshers eligible · Full-time · remote
HireSetu listing reference de19cf — confirm requirements below, then apply on the employer site.
Quick answer: A Chennai role building kinematics, estimation, planning and control for industrial manipulators, with the algorithms validated on physical robots. Hands-on filter tuning on real hardware is the requirement that decides who fits.
From DexSent Robotics's job posting
Robotics Engineer
Chennai, India | Full-time | On-site
About the Role
Develop core robotics algorithms for advanced industrial manipulation systems.
Work across kinematics, motion planning, state estimation, sensor fusion, dynamics and robot control.
Build algorithms that move beyond simulation and perform reliably on physical robots.
Solve real-world problems involving accuracy, timing, sensing, interaction and system behaviour.
What You'll Work On
You will work close to the robot itself, developing and validating algorithms that determine how the robot moves, estimates its state and interacts with the physical environment.
Robot Kinematics: Forward/inverse kinematics, rigid-body transforms, Jacobians, singularities, redundancy and Cartesian motion.
Motion Planning: Collision-aware joint-space and Cartesian planning, path generation, interpolation, smoothing and trajectory execution.
Trajectory Generation: Velocity, acceleration, jerk and timing constraints for smooth and reliable robot motion.
State Estimation: Develop and tune KF, EKF, UKF or related estimation methods for noisy and asynchronous sensor data.
Sensor Fusion: Fuse joint, force/torque, IMU and external sensor information with appropriate filtering, timing and uncertainty handling.
Robot Control: Position, velocity and torque control across joint-space and Cartesian-space behaviours.
Physical Interaction: Develop force, impedance, admittance and hybrid position-force behaviours for contact-rich manipulation.
Robot Dynamics: Work with rigid-body dynamics, inverse dynamics, compensation, observers and model-based control.
Real-Robot Validation: Deploy, tune and debug algorithms on physical manipulators under real operating constraints.
What We're Looking For
We are looking for engineers with strong robotics and mathematical fundamentals who can connect sensing, estimation, planning and control as one complete system. You should be comfortable moving from derivation and simulation to implementation, testing and real-robot deployment.
Robotics Fundamentals: Strong understanding of transforms, FK/IK, Jacobians, dynamics, motion planning and feedback control.
State Estimation: Hands-on experience with Kalman-filter-based estimation and sensor fusion on robotic or mechatronic systems.
Control: PID, feed-forward, state-space control, stability and practical controller tuning.
Force Control: Understanding of force, impedance, admittance and hybrid position-force control.
Motion & Planning: Configuration space, collision checking, trajectory generation, singularities and motion constraints.
Dynamics: Rigid-body dynamics, inverse dynamics, gravity compensation and wrench relationships.
Python & C++: Implement numerical robotics algorithms, analyse data, debug behaviour and integrate with larger systems.
Experimental Thinking: Ability to isolate failures across calibration, estimation, sensing, planning, control, timing and mechanics.
System Mindset: Understand how sensing, algorithms, robot interfaces and physical behaviour interact as one system.
Technologies & Engineering Environment
Languages: Python, C++
Mathematics: Linear algebra, rigid-body transformations, optimisation and numerical methods
Kinematics: FK/IK, Jacobians, singularities, redundancy and coordinate frames
Planning: Joint-space and Cartesian planning, collision checking and trajectory generation
Estimation: KF, EKF, UKF, filtering, sensor fusion and uncertainty modelling
Control: PID, state-space, feed-forward, force, impedance and admittance control
Dynamics: Rigid-body dynamics, inverse dynamics, observers and model-based methods
Robot Interfaces: Industrial robot SDKs, high-rate state/control interfaces and sensor APIs
Sensors: Encoders, force/torque sensors, IMUs, torque/current signals and external sensing
Simulation & Validation: Robot simulation, numerical testing, logging and controlled experiments
Development: Git, debugging, profiling, testing and benchmarking
Deployment: Integration and validation on physical industrial manipulators
Strong robotics fundamentals matter more than familiarity with any specific framework or library.
Real-world robot tuning and debugging
If you want to build robots that truly touch and transform the real world, you’ll feel at home at DexSent.
The employer’s company overview, benefits and equal-opportunity statement are left out here; they are on the original posting.
Text from DexSent Robotics's official job posting, formatted by HireSetu. The employer's own page is the final word on details.
HireSetu's note
The bar is estimation on real hardware
Most of the technology list is context, not a checklist. The filter is the hands-on Kalman work: tuning KF, EKF or UKF estimates against noisy, out-of-order sensor data on a real robotic or mechatronic system. C++ and Python both appear because the maths has to end up as running code.
LQR, MPC, operational-space control, torque sensing and collision-aware planning sit in the useful-experience pile. Missing one of those is normal. Never having tuned an estimator against hardware data is a harder gap to close.
Chennai, with the arm in the room
The role is on-site, working close to the manipulators, and that shapes the week. Some of the iteration happens at the machine: chasing joint limits, a force sensor that drifts, a gain that behaves differently at speed. Industrial arms carry enough force that safety setup is part of the job, not paperwork around it.
Ask who owns the arms, how many people share a cell, whether you get scheduled hardware time, and how fast a code change reaches a robot.
The layer under the integration work
Lots of jobs with this title are integration: perception models wired into a stack, nodes, navigation tuning. This one is the layer beneath, frames and Jacobians, rigid-body dynamics, trajectory timing, filters, torque and impedance control. If your projects are computer vision or a SLAM demo, the daily work will feel like applied mathematics with a robot attached. Engineers from drones, servo tuning, mechatronics test rigs or academic estimation groups will recognise their own work here.
Simulation goes only so far
Simulation covers less than it appears to. The distance between a working sim and a working arm is calibration error, sensor delay, friction and gains that fall apart under a payload at full speed. If your robotics experience is coursework or a Gazebo model, describe it honestly and put every real hardware detail on your CV: which arm, which sensors, what rates, what you fixed when it misbehaved. A bench rig you built yourself says more than another simulation paper.
Drafted with AI from DexSent Robotics's job posting and checked by Prashanth Jakkula before publishing. See our Editorial Policy.
How to apply
- Open DexSent Robotics's careers site with the Apply button.
- Follow the application steps on the site and upload your resume.
- Keep the confirmation email or application number for follow-up.
Apply on DexSent Robotics's site
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