Embedded Systems Engineer, Humanoid Robotics
FieldAI · Boston, MA · 1 mo ago
On-siteEngineeringFull-time
We need to produce HTML fragment with sections: About the role? Actually the posting includes "About Us" and then "Embedded Systems Engineer" title. We should not repeat job title as heading. Use for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to include sections only if posting has real specific content.
We need to extract content: About the role? The posting includes "About Us" description of company. Could map to "About the role"? Probably "About the role" is appropriate for description of role. But we have "About Us" which is company description. Could treat as "About the role"? Might be better to have "About the role" containing description of role (the responsibilities). However we have separate sections for responsibilities etc. The posting includes "What You Will Get To Do" which is responsibilities. Also "What You Have" includes education, experience, programming etc. That's qualifications/requirements/skills. Also "What Will Set You Apart" is preferred qualifications. Also "Why Join Field AI?" is benefits/company culture. There's no explicit Pay or Schedule. So we should not include Pay or Schedule sections.
We need to use
for section headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to include sections only if posting has real specific content.
We need to extract content: About the role? The posting includes "About Us" description of company. Could map to "About the role"? Probably "About the role" is appropriate for description of role. But we have "About Us" which is company description. Could treat as "About the role"? Might be better to have "About the role" containing description of role (the responsibilities). However we have separate sections for responsibilities etc. The posting includes "What You Will Get To Do" which is responsibilities. Also "What You Have" includes education, experience, programming etc. That's qualifications/requirements/skills. Also "What Will Set You Apart" is preferred qualifications. Also "Why Join Field AI?" is benefits/company culture. There's no explicit Pay or Schedule. So we should not include Pay or Schedule sections.
We need to use for each section heading. Use - for any list of duties/requirements/benefits. Use
for prose.
We must preserve all substantive information, not invent or summarize away detail. Remove boilerplate like EEO, navigation, cookie, legal, etc. Also remove duplicated lines.
We need to output only HTML fragment.
Let's structure:
About the role
maybe include description: "FieldAI is transforming how robots interact with the real world. Our growing R&D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use." That's from the first paragraph and repeated "About Us". We'll combine but avoid duplication.
Then responsibilities: Responsibilities
with - items from "What You Will Get To Do" enumerated 1-4 and subpoints.
We need to preserve all details. Let's extract bullet points:
1. Backpack Compute Platform
- Compute Platform Design: Design and select the embedded compute platforms (ARM, SoC, microcontrollers) that power the humanoid payload, balancing capabilities against SWaP constraints.
- Firmware & BSP Bring-Up: Write and customize bare-metal and RTOS firmware, board support packages (BSPs), and bootloaders for the humanoid payloads computing hardware.
- Kernel-Level Development: Work at the Linux kernel level to support real-time performance and reliable operation of the backpack's compute stack.
- Testing & Diagnostics: Conduct thermal profiling, power draw analysis, and latency measurement, and implement watchdogs and health checks for the compute stack.
2. Sensor & Actuator Drivers
- Perception & State Sensor Drivers: Adapt, integrate, and where needed develop drivers for cameras, LiDAR, and IMUs that feed the backpack's compute platform with real-time perception and state-estimation data.
- Motor, Joint & Tactile Drivers: Adapt, integrate, and where needed develop drivers for motors, joint actuators, and tactile sensors, supporting low-latency control and feedback for humanoid manipulation and locomotion.
- Communication & Timing: Bring up wired (Ethernet, CAN, GMSL, SPI, I2C) and wireless interfaces with deterministic timing (PTP, PPS) across the payload.
3. Manipulation & ML Integration
- ML Team Partnership: Partner closely with the ML team building the robot's software brain to ensure the compute platform meets their latency, memory, and throughput needs.
- Manipulation Data Pipeline: Build the data pipeline connecting camera, LiDAR, IMU, and tactile input to joint manipulation commands, from raw sensor capture through to actuator control.
- Edge ML Enablement: Support accelerated inference on the backpack so ML models can interpret sensor data and issue robot commands in real time.
- ROS/DDS Middleware: Expose driver and sensor data through ROS/ROS2 and DDS interfaces so the ML team's software brain can consume it in real time.
4. Cross-Platform Generalization & Collaboration
- Platform Abstraction: Design the backpack's compute and software architecture to generalize across a diversity of humanoid robot platforms.
- Cross-Team Collaboration: Work closely with mechanical, electrical, and sensor engineers to develop a tightly integrated backpack payload.
- Technical Leadership: Lead the technical direction of backpack compute development, from architecture decisions through implementation.
- Safety & E-Stops: Implement e-stop circuitry and safety monitoring on the backpack platform, laying the groundwork for functional safety as the fleet matures.
Now qualifications/requirements: "What You Have" includes Education, Experience Level, Programming, Embedded Systems Experience, Driver Development, ML/Edge Acceleration, Real-Time Communication Protocols, ROS & Middleware, SWaP-Constrained Design.
We could put under
Requirements
or Qualifications
. The instruction says use headings like Requirements, Qualifications, Skills, Benefits. We can choose Requirements for mandatory, Qualifications for preferred? But we have both mandatory and preferred. Maybe combine into Requirements and Qualifications sections. However we need to avoid thin sections. Could combine Education, Experience, Programming, Embedded Systems Experience, Driver Development, ML/Edge Acceleration, Real-Time Communication Protocols, ROS & Middleware, SWaP-Constrained Design into Requirements (mandatory). And "What Will Set You Apart" into Qualifications (preferred). Or we could have Skills section for technical skills.
Let's decide: Use Requirements
for Education, Experience Level, Programming, Embedded Systems Experience, Driver Development, ML/Edge Acceleration, Real-Time Communication Protocols, ROS & Middleware, SWaP-Constrained Design. Use Preferred Qualifications
for "What Will Set You Apart". Use Skills
Benefits
. Also maybe include location? Not needed.
We must not include Pay or Schedule.
Now we need to ensure we don't include boilerplate like Equal Opportunity, AI tools, etc. Remove that.
Also remove duplicated lines: The "About Us" appears twice; we will include only once.
Now produce HTML.
We need to wrap content in appropriate tags: About the role
- for any list of duties/requirements/benefits. Use
for prose. We must preserve all substantive information, not invent or summarize away detail. Remove boilerplate like EEO, navigation, cookie, legal, etc. Also remove duplicated lines. We need to output only HTML fragment. Let's structure:
About the role
maybe include description: "FieldAI is transforming how robots interact with the real world. Our growing R&D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use." That's from the first paragraph and repeated "About Us". We'll combine but avoid duplication. Then responsibilities:Responsibilities
with- items from "What You Will Get To Do" enumerated 1-4 and subpoints.
We need to preserve all details. Let's extract bullet points:
1. Backpack Compute Platform
- Compute Platform Design: Design and select the embedded compute platforms (ARM, SoC, microcontrollers) that power the humanoid payload, balancing capabilities against SWaP constraints.
- Firmware & BSP Bring-Up: Write and customize bare-metal and RTOS firmware, board support packages (BSPs), and bootloaders for the humanoid payloads computing hardware.
- Kernel-Level Development: Work at the Linux kernel level to support real-time performance and reliable operation of the backpack's compute stack.
- Testing & Diagnostics: Conduct thermal profiling, power draw analysis, and latency measurement, and implement watchdogs and health checks for the compute stack.
2. Sensor & Actuator Drivers
- Perception & State Sensor Drivers: Adapt, integrate, and where needed develop drivers for cameras, LiDAR, and IMUs that feed the backpack's compute platform with real-time perception and state-estimation data.
- Motor, Joint & Tactile Drivers: Adapt, integrate, and where needed develop drivers for motors, joint actuators, and tactile sensors, supporting low-latency control and feedback for humanoid manipulation and locomotion.
- Communication & Timing: Bring up wired (Ethernet, CAN, GMSL, SPI, I2C) and wireless interfaces with deterministic timing (PTP, PPS) across the payload.
3. Manipulation & ML Integration
- ML Team Partnership: Partner closely with the ML team building the robot's software brain to ensure the compute platform meets their latency, memory, and throughput needs.
- Manipulation Data Pipeline: Build the data pipeline connecting camera, LiDAR, IMU, and tactile input to joint manipulation commands, from raw sensor capture through to actuator control.
- Edge ML Enablement: Support accelerated inference on the backpack so ML models can interpret sensor data and issue robot commands in real time.
- ROS/DDS Middleware: Expose driver and sensor data through ROS/ROS2 and DDS interfaces so the ML team's software brain can consume it in real time.
4. Cross-Platform Generalization & Collaboration
- Platform Abstraction: Design the backpack's compute and software architecture to generalize across a diversity of humanoid robot platforms.
- Cross-Team Collaboration: Work closely with mechanical, electrical, and sensor engineers to develop a tightly integrated backpack payload.
- Technical Leadership: Lead the technical direction of backpack compute development, from architecture decisions through implementation.
- Safety & E-Stops: Implement e-stop circuitry and safety monitoring on the backpack platform, laying the groundwork for functional safety as the fleet matures.
Now qualifications/requirements: "What You Have" includes Education, Experience Level, Programming, Embedded Systems Experience, Driver Development, ML/Edge Acceleration, Real-Time Communication Protocols, ROS & Middleware, SWaP-Constrained Design.
We could put under
Requirements
orQualifications
. The instruction says use headings like Requirements, Qualifications, Skills, Benefits. We can choose Requirements for mandatory, Qualifications for preferred? But we have both mandatory and preferred. Maybe combine into Requirements and Qualifications sections. However we need to avoid thin sections. Could combine Education, Experience, Programming, Embedded Systems Experience, Driver Development, ML/Edge Acceleration, Real-Time Communication Protocols, ROS & Middleware, SWaP-Constrained Design into Requirements (mandatory). And "What Will Set You Apart" into Qualifications (preferred). Or we could have Skills section for technical skills. Let's decide: UseRequirements
for Education, Experience Level, Programming, Embedded Systems Experience, Driver Development, ML/Edge Acceleration, Real-Time Communication Protocols, ROS & Middleware, SWaP-Constrained Design. UsePreferred Qualifications
for "What Will Set You Apart". UseSkills
Benefits
- items from "What You Will Get To Do" enumerated 1-4 and subpoints.
We need to preserve all details. Let's extract bullet points:
1. Backpack Compute Platform
- Compute Platform Design: Design and select the embedded compute platforms (ARM, SoC, microcontrollers) that power the humanoid payload, balancing capabilities against SWaP constraints.
- Firmware & BSP Bring-Up: Write and customize bare-metal and RTOS firmware, board support packages (BSPs), and bootloaders for the humanoid payloads computing hardware.
- Kernel-Level Development: Work at the Linux kernel level to support real-time performance and reliable operation of the backpack's compute stack.
- Testing & Diagnostics: Conduct thermal profiling, power draw analysis, and latency measurement, and implement watchdogs and health checks for the compute stack.
2. Sensor & Actuator Drivers
- Perception & State Sensor Drivers: Adapt, integrate, and where needed develop drivers for cameras, LiDAR, and IMUs that feed the backpack's compute platform with real-time perception and state-estimation data.
- Motor, Joint & Tactile Drivers: Adapt, integrate, and where needed develop drivers for motors, joint actuators, and tactile sensors, supporting low-latency control and feedback for humanoid manipulation and locomotion.
- Communication & Timing: Bring up wired (Ethernet, CAN, GMSL, SPI, I2C) and wireless interfaces with deterministic timing (PTP, PPS) across the payload.
3. Manipulation & ML Integration
- ML Team Partnership: Partner closely with the ML team building the robot's software brain to ensure the compute platform meets their latency, memory, and throughput needs.
- Manipulation Data Pipeline: Build the data pipeline connecting camera, LiDAR, IMU, and tactile input to joint manipulation commands, from raw sensor capture through to actuator control.
- Edge ML Enablement: Support accelerated inference on the backpack so ML models can interpret sensor data and issue robot commands in real time.
- ROS/DDS Middleware: Expose driver and sensor data through ROS/ROS2 and DDS interfaces so the ML team's software brain can consume it in real time.
4. Cross-Platform Generalization & Collaboration
- Platform Abstraction: Design the backpack's compute and software architecture to generalize across a diversity of humanoid robot platforms.
- Cross-Team Collaboration: Work closely with mechanical, electrical, and sensor engineers to develop a tightly integrated backpack payload.
- Technical Leadership: Lead the technical direction of backpack compute development, from architecture decisions through implementation.
- Safety & E-Stops: Implement e-stop circuitry and safety monitoring on the backpack platform, laying the groundwork for functional safety as the fleet matures.
Now qualifications/requirements: "What You Have" includes Education, Experience Level, Programming, Embedded Systems Experience, Driver Development, ML/Edge Acceleration, Real-Time Communication Protocols, ROS & Middleware, SWaP-Constrained Design.
We could put under
...
. For lists:- ...
- or just include as part of
- text. The instruction: Use
- for any list of duties/requirements/benefits. It doesn't forbid nesting. We'll keep simple: each main bullet as
- and sub-bullets as separate
- maybe with indentation? Could nest but okay.
We'll produce:
About the role
FieldAI is transforming how robots interact with the real world. Our growing R&D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use.
Responsibilities
- Backpack Compute Platform
- Compute Platform Design: Design and select the embedded compute platforms (ARM, SoC, microcontrollers) that power the humanoid payload, balancing capabilities against SWaP constraints.
- Firmware & BSP Bring-Up: Write and customize bare-metal and RTOS firmware, board support packages (BSPs), and bootloaders for the humanoid payloads computing hardware.
- Kernel-Level Development: Work at the Linux kernel level to support real-time performance and reliable operation of the backpack's compute stack.
- Testing & Diagnostics: Conduct thermal profiling, power draw analysis, and latency measurement, and implement watchdogs and health checks for the compute stack.
- Sensor & Actuator Drivers
- Perception & State Sensor Drivers: Adapt, integrate, and where needed develop drivers for cameras, LiDAR, and IMUs that feed the backpack's compute platform with real-time perception and state-estimation data.
- Motor, Joint & Tactile Drivers: Adapt, integrate, and where needed develop drivers for motors, joint actuators, and tactile sensors, supporting low-latency control and feedback for humanoid manipulation and locomotion.
- Communication & Timing: Bring up wired (Ethernet, CAN, GMSL, SPI, I2C) and wireless interfaces with deterministic timing (PTP, PPS) across the payload.
- Manipulation & ML Integration
- ML Team Partnership: Partner closely with the ML team building the robot's software brain to ensure the compute platform meets their latency, memory, and throughput needs.
- Manipulation Data Pipeline: Build the data pipeline connecting camera, LiDAR, IMU, and tactile input to joint manipulation commands, from raw sensor capture through to actuator control.
- Edge ML Enablement: Support accelerated inference on the backpack so ML models can interpret sensor data and issue robot commands in real time.
- ROS/DDS Middleware: Expose driver and sensor data through ROS/ROS2 and DDS interfaces so the ML team's software brain can consume it in real time.
- Cross-Platform Generalization & Collaboration
- Platform Abstraction: Design the backpack's compute and software architecture to generalize across a diversity of humanoid robot platforms.
- Cross-Team Collaboration: Work closely with mechanical, electrical, and sensor engineers to develop a tightly integrated backpack payload.
- Technical Leadership: Lead the technical direction of backpack compute development, from architecture decisions through implementation.
- Safety & E-Stops: Implement e-stop circuitry and safety monitoring on the backpack platform, laying the groundwork for functional safety as the fleet matures.
Requirements
- Education: B.S., M.S., or Ph.D. in Computer Engineering, Electrical Engineering, Robotics, or a related field.
- Experience Level: Minimum of 3+ years of hands-on experience with embedded systems.
- Programming: Proficient in C++ and Python for embedded and application-level development.
- Embedded Systems Experience: Experience bringing up and customizing bare-metal and RTOS firmware, Linux kernel and device drivers, and board support packages (BSPs), across platforms such as Jetson or custom SBCs.
- Driver Development: Experience developing drivers for cameras, LiDAR, IMUs, tactile sensors, or motors, connecting sensors and actuators to embedded compute.
- ML/Edge Acceleration: Familiarity with GPU, TPU, or NPU offload and frameworks such as CUDA or TensorRT for edge inference, ideally supporting manipulation
- Backpack Compute Platform