Junior Computer Vision & Robotics Engineer

We build realworld systems that survive dust, heat, rain, and deadlines. We dont chase demos; we ship machines that last. Today we enable remote QCsensor fusion to precise 3D so changes can be reviewed from anywhere. Next we go handson at a distance: telepresence robotics with ROS2, bulletproof power, and live telemetry. If you want the full pipelinehardware firmware perception decision actuationthis is the playground, and its production.

Explore what weve built:

• Robotics solutions: skymul.com/robotics#solutions
• Demo video: YouTube

• Builders first and in person: passionate, curious, and relentlessly handsonyou prototype, break, measure, and rebuild at the bench and in the field.
• Not cloning tech for a local market: we build for the world and take on problems not solved elsewhere.
• Failures are data, no rulebook: undefined hard problems, learnings shared openly, failures turned into progress.
• Learn at lightning speed: selfteach new tools, read papers, ship working systems in daysnot months.
• No pedigree gating: degrees and years dont decide; evidence of hard builds, clear thinking, and character do.
• Benevolent teammates only: we push hard and help harderzero tolerance for ego or toxicity.

• Build CV/3D pipelines endtoend: calibration, feature extraction, multiview geometry, reconstruction, reprojection checks.
• Translate math into code: implement geometric algorithms from first principlesSVD, least squares, RANSAC, triangulation, PnP, bundle adjustment seedswithout an LLM doing the thinking for you.
• Wire perception into the robot: pull camera/IMU/LiDAR streams together, manage TF2 frames and timesync, debug transform trees.
• Prototype hard, measure harder: instrument experiments, log everything, write short technical notes the rest of the team can read.
• Read papers and ship: turn ideas from FPCV / CS231A / 16822 into working scripts within days.

• Excellent linear algebra intuition: not memorized formulasyou can explain SVD geometrically, derive least squares, manipulate rotations in SO(3)/SE(3), and recognize when a problem is rankdeficient.
• Handson grasp of the three CV courseware (lectures + assignments worked through):
• First Principles of Computer Vision (Columbia, Shree Nayar) fpcv.cs.columbia.edu and YouTube.
• Stanford CS231A course notes + public problem sets.
• CMU 16822 Geometrybased Methods in Vision geometric3d.github.io.
• Heavy coding muscle, low AI dependence: you can implement a fundamental matrix estimator, calibrate a camera, or write a small SLAM loop without copypasting from an LLM. AI tools are welcome to acceleratenot to replace understanding. We will probe this in interview.
• Speed then rigor: prototype quickly to learn, then harden to fieldgradeown the path from scrappy v0 to reliable v1+.
• Linux, Git, Python fluency; basic C++ comfortable enough to read and modify ROS2 nodes.
• Methodical debugging: structured experiments, ablations, scopes, logs, reproducible results.

• Robotics work any prior build: robot controls, manipulators, drones, autonomous systems, ROVs, even a serious hobby project.
• ROS2 fluency: nodes, launch, TF2, bag handling, diagnostics.
• SLAM/VO, depth fusion, NeRF/3DGS, or onedge inference (Jetson/NPU).
• Numerical optimization (Ceres, g2o, GTSAM) and sensor calibration tooling.
• Multisensor calibration and timesync experience.

• Reproducible perception components with clear math, clear interfaces, clear tests.
• Clean ROS2 integration of your CV outputsno surprise frames, no silent NaNs, defensible metrics.
• Short technical writeups that explain decisions and tradeoffs in plain language.

Computer Vision (do all three; FPCV first for video lectures)

• First Principles of Computer Vision (Columbia / Shree Nayar) primary lectures. Free videos + free monograph PDFs. Watch the 3D Reconstruction I & II courses endtoend. fpcv.cs.columbia.edu YouTube
• Stanford CS231A best free written problem sets. Use the public course notes and ps1/ps2/ps3 PDFs as your homework. course notes course site
• CMU 16822 Geometrybased Methods in Vision best free coding assignments. Work through the multiview recon problem sets. geometric3d.github.io

Linear Algebra (level: upperundergrad, with SVD nonnegotiable)

• 3Blue1Brown Essence of Linear Algebra visual intuition pass; do this first if rusty. YouTube series
• MIT 18.06 (Gilbert Strang) canonical depth, full lectures, exams, and assignments. MIT OCW
• ROB 101 Computational Linear Algebra (Michigan Robotics) codingfirst, roboticsflavored, Jupyter notebooks. GitHub
• Required comfort: vector spaces, rank/nullspace, SVD, eigendecomposition, orthogonal projections, least squares, rotation matrices, SO(3)/SE(3), numerical conditioning. Should be able to derive and implement, not just recognize.

• Cochin, India; inperson.

• Competitive, high ownership, and rapid growth across the full stack.
• Equity/stock options at an earlystage startup, performancebased grants and refreshers.

Send your resume plus links (portfolio/GitHub/videos/photos/papers) and 510 lines on your toughest buildproblem, constraints, key decisions, outcome. Links preferred. If youve worked through any of the three CV courses or the linear algebra resources above, share your code/notesthat goes a long way.

• We evaluate builds and characternot degrees or years.
• "Junior" doesnt mean anything here. The word in the title is a deliberate filter. If "Junior" stings or youre here to optimize for the next title bump, this isnt your seat. If youre here to ship hard things and let the work speak, we dont care what we call the role.