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Overview

We are looking for an experienced electrical engineer who is passionate about hardware design, routing dense, complex high-speed signals, and building the lowest level of the perception and compute stack for robotics. This role is a blend of custom PCB layout design, system-level architecture, and cross-functional collaboration to build complete System-on-Modules (SoMs) with integrated perception and compute.

Requirements

• Experience with custom PCB layout and hardware design for complex devices.
• System-level understanding of hardware architecture, signal integrity, and power distribution.
• Experience designing with cameras, image sensors, and SoMs.
• Ability to perform initial hardware bring-up and debug complex system-level issues.
• Hands-on experience with hardware testing and benchmarking using standard lab equipment.
• Deep understanding of Design for Manufacturing (DFM) and Design for Testing (DFT) principles to ensure designs can be mass-produced.

Responsibilities

• Designing hardware architectures and routing complex PCB layouts for our next-generation perception devices.
• Integrating SoCs, image sensors, IMUs, and Wi-Fi/BT modules into tightly constrained boards.
• Leading board bring-up, validating power sequencing, and verifying high-speed interfaces.
• Testing and benchmarking hardware performance using equipment such as oscilloscopes, logic analyzers, spectrum analyzers, multimeters, and electronic loads.
• Ensuring ultra-low latency, precise hardware synchronization, and minimal jitter across the system.
• Applying Design for Manufacturing (DFM) practices and collaborating with contract manufacturers (CMs) to ensure designs scale efficiently and yield reliable units.

Overview

We are looking for an experienced mechanical engineer who is passionate about physical product design, solving complex packaging constraints, and building robust hardware that can survive in the real world. This role blends industrial design, thermal management, and hands-on integration for devices deployed on highly dynamic robots like drones and manipulators.

Requirements

• Experience with mechanical engineering and advanced 3D CAD modeling.
• System-level understanding of physical product design, structural integrity, and complex packaging.
• Strong understanding of thermal dissipation, weight optimization, and material selection.
• Deep understanding of Design for Manufacturing (DFM) and Design for Assembly (DFA) principles to ensure enclosures can be mass-produced efficiently.
• Hands-on experience with mechanical testing and validation using standard lab equipment (e.g., thermal chambers, vibration tables, load testing).

Responsibilities

• Own the industrial design and mechanical packaging for our hardware modules.
• Manage the complex thermal and weight trade-offs required for integration into high-performance platforms like drones, humanoids, and robotic arms.
• Design custom subcomponents and mounting architectures tailored for diverse robot form factors.
• Assist customers directly with the physical integration of our devices into their proprietary robotic platforms.
• Lead rapid prototyping, testing, and iteration of mechanical enclosures using tools like 3D printing and CNC machining.
• Conduct physical benchmarking and validation (thermal, vibration, drop, and structural testing) to ensure real-world reliability.
• Apply DFM practices and collaborate closely with contract manufacturers (CMs) to transition designs from prototype to mass production.

Nice to Have

• Experience working directly with contract manufacturers (CMs).
• Background in aerospace, drones, or ruggedized electronics.
• Familiarity with vibration isolation, dynamic load testing, and IP-rated sealing (water/dust resistance).

Overview

We are seeking an experienced Camera Systems Engineer focused on optical architecture, sensor integration, and low-level driver development for custom robotic perception modules. In this role, you will own the end-to-end visual pipeline—from optical component selection and hardware bring-up to the digital data stream—delivering highly reliable, low-latency machine vision for dynamic robotic platforms.

Requirements

• Experience with camera hardware, image sensors, and optical design.
• System-level understanding of the complete camera pipeline, from photon capture to memory transfer.
• Hands-on experience with sensor bring-up, I2C/SPI configuration, and writing MIPI CSI-2 drivers.
• Ability to write clean, maintainable low-level code (C/C++).
• Hands-on experience with optical testing and benchmarking using standard lab equipment (e.g., oscilloscopes, logic analyzers, integrating spheres, tunable light sources, and standardized test charts).
• Deep understanding of Design for Manufacturing (DFM) principles as they apply to optical assemblies, active alignment, and sensor packaging.

Responsibilities

• Select lenses, optics, and image sensors tailored specifically for our edge compute and perception hardware.
• Lead the initial bring-up of new camera hardware, validating sensor capabilities and resolving low-level hardware/software bugs.
• Write and debug MIPI drivers and low-level sensor configurations to ensure perfect, minimal-jitter data transmission.
• Test and benchmark optical performance (e.g., MTF, distortion, stray light, SNR) across varied, real-world lighting conditions.
• Own the precision optical calibration processes for stereo and multi-camera arrays.
• Apply DFM practices to optical modules, collaborating closely with lens vendors and contract manufacturers (CMs) to ensure active alignment and assembly processes yield high reliability at scale.
• Collaborate with the electrical and mechanical teams to optimize hardware layouts for signal integrity, vibration resistance, and thermal stability.

Nice to Have

• Background in computational photography or Image Signal Processing (ISP) pipeline tuning.

Overview

We are seeking an experienced Image Quality (IQ) Engineer focused on tuning Image Signal Processor (ISP) pipelines and optimizing raw sensor data for high-performance machine vision. This role blends computational imaging, rigorous sensor tuning, and software optimization to ensure our perception systems perform flawlessly across dynamic, real-world lighting conditions.

Requirements

• Experience with Image Signal Processors (ISP) and comprehensive image tuning.
• System-level understanding of the imaging pipeline, from raw Bayer data to the final processed output.
• A strong focus on delivery, with a track record of deploying robust imaging solutions in production environments.
• Deep understanding of camera artifacts, dynamic range, exposure, and noise profiles.
• Hands-on experience developing metrics and testing protocols to objectively evaluate image quality.
• Ability to script and automate tuning workflows (Python, MATLAB, or C++).

Responsibilities

• Tune raw image sensors to maximize performance for downstream computer vision algorithms (VIO, VSLAM, object detection).
• Optimize the ISP pipeline for challenging edge cases, including extreme low-light, high glare, and rapid motion blur.
• Develop robust, objective metrics and automated testing protocols to continuously evaluate and benchmark image quality.
• Work closely with Computer Vision engineers to iteratively adjust imaging parameters based on neural network and algorithmic performance.
• Diagnose and resolve complex visual artifacts, isolating root causes at both the hardware and software levels.

Nice to Have

• Experience tuning specifically for robotics, automotive, or industrial computer vision applications.
• Understanding of modern AI-based image enhancement techniques.
• Familiarity with color science, photometry, and standardized IQ testing charts (e.g., Imatest).

Overview

We are seeking an experienced Computer Vision Engineer focused on spatial AI, algorithm optimization for edge hardware, and building robust 3D perception systems. This role blends VIO/VSLAM development, model optimization, and edge compute deployment to enable real-time robotic vision. Your work will center on ensuring complex spatial models run efficiently on dedicated NPUs, delivering high-performance, low-latency perception directly on the device.

Requirements

• Experience with 3D computer vision, Visual Inertial Odometry (VIO), or VSLAM systems.
• System-level understanding of perception pipelines, from raw sensor input to real-time spatial mapping.
• A strong focus on delivery, with a track record of deploying robust algorithms to edge environments.
• Strong C/C++ and Python programming skills.
• Familiarity with deploying and accelerating machine learning models on edge devices and dedicated Neural Processing Units (NPUs).

Responsibilities

• Develop and deploy robust VIO/VSLAM and 3D perception algorithms to run directly on-device.
• Optimize vision pipelines to maximize inference speed and efficiently leverage embedded compute resources.
• Collaborate with infrastructure and hardware teams to minimize end-to-end latency across the entire perception stack.
• Test, benchmark, and validate perception systems on physical lab robots in real-world environments.
• Diagnose and aggressively resolve tracking drops, algorithmic failure modes, and edge cases to ensure highly reliable spatial awareness.

Nice to Have

• Experience with model distillation, quantization, and edge AI optimization techniques.
• Familiarity with modern Vision Foundation Models and their deployment.
• Background working with stereo depth estimation or multi-sensor fusion (e.g., tightly coupled Camera + IMU systems).

Overview

We are seeking an experienced Controls Engineer focused on physical motion, robust driver development, and building control systems that bridge the gap between AI perception and the physical world. This role blends low-level IMU integration, robotics controls, and system architecture to ensure robotic platforms operate smoothly and safely, even under constrained network conditions.

Requirements

• Experience with control theory and applied robotics engineering.
• System-level understanding of hardware/software integration and sensor data pipelines.
• A strong focus on delivery, with a track record of deploying robust control systems on physical hardware.
• Experience writing low-level drivers for IMUs and handling high-frequency sensor data streams.
• Ability to design control architectures that account for wireless system constraints, including latency spikes and packet loss.

Responsibilities

• Develop low-level IMU drivers and implement advanced filtering algorithms for precise state estimation.
• Design and deploy control systems that interface perfectly with our edge perception devices.
• Build robust fallbacks, safety constraints, and predictive models to handle network jitter during remote teleoperation.
• Tune control loops directly on physical hardware, including highly dynamic platforms like drones and manipulators.
• Diagnose complex hardware/software timing issues and drive them to rapid resolution.

Nice to Have

• Experience with ROS/ROS2 or other modern robotics middleware.
• Background in shared autonomy or teleoperation systems.
• Familiarity with rigid body dynamics and kinematics.

Overview

We are seeking an experienced Robotics Software Engineer focused on embodied AI and bridging the gap between perception models and physical robotic hardware. This role blends teleoperation pipelines, on- and off-device inference, and hands-on lab experimentation to prove out our technology.

Requirements

• Experience writing software for physical robotic systems.
• System-level understanding of hardware-software integration and robotic kinematics.
• A strong focus on delivery, with a track record of deploying working solutions on physical robots.
• Strong C/C++ and Python development skills.
• Comfortable working hands-on with hardware in a lab environment.

Responsibilities

• Build and maintain the software stack for our internal fleet of test robots.
• Deploy and test model inference pipelines directly on the edge hardware.
• Integrate teleoperation workflows and optimize end-to-end control latency.
• Collaborate with the hardware team to mount, wire, and test new device iterations.
• Debug complex system-level failures to ensure reliable and safe robotic behavior.

Nice to Have

• Experience with VR/AR interfaces for teleoperation.
• Familiarity with simulation environments (Isaac Sim, Gazebo).
• Knowledge of standard robotic actuators and motor controllers.

Overview

We are seeking a Robotics Research Engineer focused on advancing teleoperation and model inference across a perception and compute stack built entirely from the ground up—from custom hardware through edge compute to cloud inference. This role blends embodied AI research, real-robot experimentation, and end-to-end system design to push what is possible when data collection, teleoperation, and foundation model inference share a single, tightly integrated infrastructure.

Requirements

• Experience conducting research or advanced development on physical robotic systems.
• System-level understanding of perception pipelines, teleoperation, and model inference across edge and cloud.
• Strong Python and C/C++ skills, with comfort prototyping and iterating quickly on real hardware.
• Familiarity with modern robotic foundation models, policy learning, or vision-language-action (VLA) systems.
• Track record of translating research ideas into working demonstrations on physical platforms.

Responsibilities

• Develop and evaluate teleoperation and inference architectures that leverage Efference's full-stack perception and compute platform.
• Design experiments that stress-test end-to-end latency, data quality, and policy performance across edge and cloud inference paths.
• Build prototypes integrating custom perception hardware, wireless teleoperation, and remote model inference into cohesive robotic workflows.
• Collaborate across hardware, perception, controls, and infrastructure teams to identify and resolve system-level bottlenecks.
• Publish internal benchmarks and technical findings that inform product direction and future research priorities.

Nice to Have

• Experience with large-scale robot learning, imitation learning, or RL on physical systems.
• Background in dataset design for robotics (multimodal, synchronized sensor streams).
• Familiarity with cloud GPU inference clusters and edge-to-cloud model deployment.
• Publications or open-source contributions in robotics, computer vision, or ML systems.

Overview

We are seeking an experienced Embedded Systems Engineer focused on orchestrating complex data flows and building the embedded Linux foundation that makes real-time AI possible. This role blends Linux internals, driver coordination, and system-level optimization to keep our multi-sensor platforms running perfectly in sync.

Requirements

• Solid Embedded Linux knowledge (userspace, system internals, custom builds).
• System-level understanding of computing architecture and hardware-software integration.
• Experience coordinating complex systems across multiple discrete sensors.
• Ability to write clean, maintainable code (C, C++, Bash).

Responsibilities

• Own the embedded Linux architecture for our edge perception devices.
• Ensure highly synchronized, low-latency data pipelines between the OS, cameras, and IMU.
• Debug hardware-software integration issues alongside the EE and optical teams.
• Optimize boot times, power consumption, and thermal limits at the OS level.
• Maintain build systems (Yocto/Buildroot) and manage over-the-air (OTA) update flows.

Nice to Have

• Kernel development exposure (drivers, patches, device trees).
• Experience with hardware-accelerated media pipelines (GStreamer).
• Familiarity with real-time operating systems (RTOS).

Overview

We are seeking an experienced Wireless Systems Engineer focused on network protocols, low-level radio optimization, and building the high-speed connectivity backbone for teleoperation and off-device model inference. This role blends protocol tuning, RF system optimization, and backend networking to achieve ultra-low latency and minimal jitter.

Requirements

• Experience with wireless networking, RF systems, or real-time protocols.
• System-level understanding of data transmission over constrained and dynamic networks.
• Deep understanding of network layers, latency optimization, and jitter reduction.
• Ability to code network-level software and tuning scripts.

Responsibilities

• Architect ultra-low latency wireless pipelines for real-time robotic teleoperation.
• Optimize networks end-to-end, from low-level Wi-Fi/5G drivers to backend servers.
• Implement custom transport protocols or tune WebRTC/UDP for harsh wireless environments.
• Collaborate with controls engineers to build systems that gracefully handle network degradation.
• Test and validate wireless performance in real-world robotic deployments.

Nice to Have

• Experience with real-time video streaming codecs (H.264, H.265, AV1).
• Background in private 5G networks or custom RF hardware.
• Familiarity with NAT traversal and modern cloud networking.

Overview

We are seeking an experienced Machine Learning Infrastructure Engineer focused on scaling cloud architectures and building the backend engines that power modern AI. This role blends data collection infrastructure, teleoperation routing, and model inference pipelines for next-generation robotic fleets.

Requirements

• Experience building backend infrastructure, data pipelines, or MLOps systems.
• System-level understanding of cloud architecture, networking, and large-scale data flow.
• Strong backend engineering skills (Python, Go, or similar).
• Familiarity with cloud providers (AWS, GCP) and container orchestration (Kubernetes).

Responsibilities

• Set up and scale the backend architecture for massive, multimodal data collection.
• Build the high-availability infrastructure required to route teleoperation video and control feeds.
• Create automated pipelines for model training, evaluation, and deployment back to the edge.
• Manage cloud storage and compute resources for large-scale computer vision datasets.
• Diagnose and resolve pipeline failures quickly to ensure continuous data flow.

Nice to Have

• Experience managing clusters of GPUs for deep learning workloads.
• Familiarity with synthetic data generation pipelines.
• Knowledge of modern edge-to-cloud synchronization techniques.