Junior Computer Vision & Robotics Engineer

Next we go hands‑on at a distance: telepresence robotics with ROS2 , bulletproof power, and live telemetry. If you want the full pipeline—hardware → firmware → perception → decision → actuation —this is the playground, and it’s production .\n\nExplore what we’ve built:\nRobotics solutions : skymul.com/robotics#solutions\nDemo video : YouTube\nTeam culture & how we work Builders first and in person : passionate, curious, and relentlessly hands‑on—you prototype, break, measure, and rebuild at the bench and in the field.\nNot cloning tech for a local market : we build for the world and take on problems not solved elsewhere.\nFailures are data, no rulebook : undefined hard problems, learnings shared openly, failures turned into progress.\nLearn at lightning speed : self‑teach new tools, read papers, ship working systems in days—not months.\nNo pedigree gating : degrees and years don’t decide; evidence of hard builds, clear thinking, and character do.\nBenevolent teammates only : we push hard and help harder—zero tolerance for ego or toxicity.\n\nWhat you’ll do Build CV/3D pipelines end‑to‑end : calibration, feature extraction, multi‑view geometry, reconstruction, reprojection checks.\nTranslate math into code : implement geometric algorithms from first principles—SVD, least squares, RANSAC, triangulation, PnP, bundle adjustment seeds—without an LLM doing the thinking for you.\nWire perception into the robot : pull camera/IMU/LiDAR streams together, manage TF2 frames and time‑sync, debug transform trees.\nPrototype hard, measure harder : instrument experiments, log everything, write short technical notes the rest of the team can read.\nRead papers and ship : turn ideas from FPCV / CS231A / 16‑822 into working scripts within days.\n\nMust‑have Excellent linear algebra intuition : not memorized formulas—you can explain SVD geometrically, derive least squares, manipulate rotations in SO(3)/SE(3), and recognize when a problem is rank‑deficient.\nHands‑on grasp of the three CV courseware (lectures + assignments worked through):\nFirst Principles of Computer Vision (Columbia, Shree Nayar) — fpcv.cs.columbia.edu and YouTube.\nStanford CS231A — course notes + public problem sets.\nCMU 16‑822 Geometry‑based Methods in Vision — geometric3d.github.io.\nHeavy coding muscle, low AI dependence : you can implement a fundamental matrix estimator, calibrate a camera, or write a small SLAM loop without copy‑pasting from an LLM . AI tools are welcome to accelerate—not to replace understanding.

We will probe this in interview.\nSpeed then rigor : prototype quickly to learn, then harden to field‑grade—own the path from scrappy v0 to reliable v1+.\nLinux, Git, Python fluency; basic C++ comfortable enough to read and modify ROS2 nodes.\nMethodical debugging : structured experiments, ablations, scopes, logs, reproducible results.\n\nNice‑to‑have (Robotics is a strong bonus) Robotics work — any prior build: robot controls, manipulators, drones, autonomous systems, ROVs, even a serious hobby project.\nROS2 fluency : nodes, launch, TF2, bag handling, diagnostics.\nSLAM/VO, depth fusion, NeRF/3DGS, or on‑edge inference (Jetson/NPU).\nNumerical optimization (Ceres, g2o, GTSAM) and sensor calibration tooling.\nMulti‑sensor calibration and time‑sync experience.\n\nWhat success looks like Reproducible perception components with clear math, clear interfaces, clear tests.\nClean ROS2 integration of your CV outputs—no surprise frames, no silent NaNs, defensible metrics.\nShort technical write‑ups that explain decisions and trade‑offs in plain language.\n\nRecommended prep (use this before and during onboarding) Computer Vision (do all three; FPCV first for video lectures)\nFirst Principles of Computer Vision (Columbia / Shree Nayar) — primary lectures. Free videos + free monograph PDFs. Watch the 3D Reconstruction I & II courses end‑to‑end.

→ fpcv.cs.columbia.edu · YouTube\nStanford CS231A — best free written problem sets. Use the public course notes and ps1/ps2/ps3 PDFs as your homework. → course notes · course site\nCMU 16‑822 Geometry‑based Methods in Vision — best free coding assignments.

Work through the multi‑view recon problem sets. → geometric3d.github.io\nLinear Algebra (level: upper‑undergrad, with SVD non‑negotiable)\n3Blue1Brown — Essence of Linear Algebra — visual intuition pass; do this first if rusty. → YouTube series\nMIT 18.06 (Gilbert Strang) — canonical depth, full lectures, exams, and assignments.

→ MIT OCW\nROB 101 Computational Linear Algebra (Michigan Robotics) — coding‑first, robotics‑flavored, Jupyter notebooks. → GitHub\nRequired comfort : vector spaces, rank/null‑space, SVD , eigendecomposition, orthogonal projections, least squares, rotation matrices, SO(3)/SE(3), numerical conditioning. Should be able to derive and implement, not just recognize.\n\nLocation & work mode Cochin, India; in‑person.\n\nCompensation & growth Competitive, high ownership, and rapid growth across the full stack.\nEquity/stock options at an early‑stage startup , performance‑based grants and refreshers.\n\nHow to apply Send your resume plus links (portfolio/GitHub/videos/photos/papers) and 5–10 lines on your toughest build —problem, constraints, key decisions, outcome.

Links preferred. If you’ve worked through any of the three CV courses or the linear algebra resources above, share your code/notes—that goes a long way.\n\nA note on the title We evaluate builds and character—not degrees or years.\n\"Junior\" doesn’t mean anything here. The word in the title is a deliberate filter.

If \"Junior\" stings or you’re here to optimize for the next title bump, this isn’t your seat. If you’re here to ship hard things and let the work speak, we don’t care what we call the role.