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Nimble Robotics

AI-powered robotic fulfillment for eCommerce logistics

Senior Robotics Software Engineer - System Identification & Modeling

Robotics & Autonomous SystemsConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USAIn-person

About the Job

Requirements

  • A bachelor's, master's, or PhD degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, or a related field.
  • At least 5 years of software engineering experience building production robotics systems.
  • A strong foundation in dynamics, controls, and system identification, including least-squares regression, parameter estimation, state-space and transfer-function modeling, and feedforward/feedback controller design.
  • Hands-on experience characterizing real electromechanical systems such as motors, gearboxes, linear actuators, or articulated arms.
  • Strong proficiency in at least one of Rust, Python, or C++.
  • Ability to design, implement, and maintain complex, high-reliability software systems.
  • Experience integrating software with sensors, actuators, and embedded systems.
  • Familiarity with software best practices, including testing, documentation, code reviews, and robust architectural design.
  • Willingness to support production operations through an on-call rotation.

Responsibilities

  • Design and execute system identification experiments for actuators, mechanisms, and full robot subsystems, fitting dynamic models via regression and curve-fitting to derive accurate feedforward and feedback controllers.
  • Own kinematic calibration workflows, including DH parameter identification, wheel radius estimation, and tool-center-point calibration to improve arm accuracy and mobile base odometry.
  • Build automated calibration and system-identification tooling that runs on production hardware, enabling rapid re-characterization after mechanical changes, wear, or new platform deployments.
  • Lead the design and implementation of robot behaviors and task-level intelligence across the full stack, integrating perception, planning, and control into reliable end-to-end execution across nominal and edge-case scenarios.
  • Drive measurable improvements in autonomy quality and arm accuracy using data, operational metrics, and model validation, including diagnostic plots, residual analysis, and statistical benchmarks.
  • Collaborate with hardware engineering on software-hardware integration for new platforms and upgrades, and triage and resolve production robotics issues.
  • Lead technical design reviews, drive architecture decisions for core subsystems, and mentor engineers and technicians on reliability, testing, and operational excellence.

Desired Qualifications

  • Graduate-level coursework or research in system identification, optimal estimation, or adaptive control.
  • Experience with nonlinear system identification techniques such as extended Kalman filters, particle filters, or neural-network-based model learning.
  • Familiarity with physics simulators such as MuJoCo, Drake, or Isaac Sim and sim-to-real transfer, including using identified parameters to calibrate simulation environments.
  • Experience with real-time embedded system development, real-time operating systems, or bare-metal firmware.
  • Exposure to automation environments such as warehousing, manufacturing, or logistics.
  • Experience with safety-critical systems.
  • Familiarity with ROS/ROS 2 and standard robotics middleware.
  • Experience building data pipelines for collecting, storing, and analyzing robot telemetry at scale.

About the Company

Nimble Robotics

AI-powered robotic fulfillment for eCommerce logistics

Nimble Robotics uses artificial intelligence to optimize eCommerce logistics and supply chains, coordinating fulfillment and inventory planning to speed deliveries and improve accuracy. Its system includes robotic fulfillment hardware that handles items that fit in a bin, with oversized items processed manually, and works via plug‑and‑play integrations with Shopify, Netsuite, and Skubana on a pay‑as‑you‑go, no-upfront-capex model plus pilot options. It differentiates itself by combining AI‑driven optimization with robotic fulfillment and a hands‑off support model backed by engineers and leaders from robotics and logistics, plus flexible pilots. The goal is to help brands—from startups to Fortune 500s—scale fulfillment efficiently, reduce delivery times, and boost sales and customer satisfaction without upfront costs.

Company size
201-500
Company stage
Series C
Total funding
$221M
Headquarters
San Francisco, CA
Founded
2017

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