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Manufacturing Reliability Engineer (Austin, TX; 50% travel to Milwaukee, WI)
Diligent Robotics · Austin, Texas, United States
$95k–165k
34 days ago
♡
Technical Support Specialist - Robotics
Diligent Robotics · Austin, Texas, United States
$35k–130k
39 days ago
♡
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Manufacturing Reliability Engineer (Austin, TX; 50% travel to Milwaukee, WI)
Diligent Robotics · Austin, Texas, United States
Pay
$95k–165k
Setting
On-site
Austin, Texas, United States
Listed by Diligent Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.
Technical Support Specialist - Robotics
Diligent Robotics · Austin, Texas, United States
Pay
$35k–130k
Setting
On-site
Back to jobs
Technical Support Specialist - Robotics
Austin, Texas, United States
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What we’re doing isn’t easy. But nothing worth doing ever is.
We envision a future powered by robots that work seamlessly with human teams. We build the artificial intelligence that enables service robots to collaborate with people and adapt to dynamic human environments. Join our mission-driven, venture-backed team as we build out our customer-facing operations arm.
As part of Diligent Robotics’ operations team, you will uphold the highest standards for safe clinical use of robots in real-world hospital environments. Through excellent communication, critical thinking, and documentation, you will work alongside your teammates to bring automation to healthcare. This position will work effectively as a team to ensure exceptional results to our hospital partners
This position is located on-site in Austin, TX and you must be local, willing to relocate, or a current Diligent Robotics employee, to be considered. This role will require flexibility due to us being a 24/7 operation and a shift schedule will be put in place that will require working days, nights and weekends.
Your responsibilities will include:
Monitoring a fleet of mobile service robots and providing live support and in the moment troubleshooting in a hospital environment using problem solving skills
Detailed ticketing of issues and situations unable to resolve at first touch to escalate internally to other departments
Prioritizing the customer experience by communicating with them when needed, critical thinking for unplanned scenarios, and ensuring their expectations are always met or exceeded
Collecting data in a variety of real-world situations, providing detailed and accurate product feedback
Being an ambassador for the company
A good candidate would be, at minimum:
You have a good computing background, and can quickly learn to use new software
An excellent written and verbal communicator: you convey information to internal stakeholders in an organized and easily parsable manner
Organized: you can juggle and make progress on multiple tasks simultaneously
Self-sufficient and whip-smart: you get things done, learn what you don’t know, and can make data-driven decisions independently, as circumstances require
Flexible and willing to work different shift hours, including early mornings, nights, and weekends: you understand that our customers (i.e. hospitals) are open 24/7, that our robots must be running smoothly all the time, and that your schedule may shift as customer needs change over time
Willing to be COVID-19 and flu vaccinated and complete a HIPAA training course
The ideal candidate would be:
Responsive and responsible: you commit to deadlines and err on the side of over-communication
Results-oriented: you're happy to observe and take notes on end-users interacting with the robot all day if that's what it takes
Vigilant about the details: you notice when a checklist item is left unchecked and it gnaws at you until it's done
Passionate about healthcare and technology coming together to help people
Willing to work holidays: you understand that our customers (i.e. hospitals) don't shut down during the holidays and neither can our robots
A budding techy: you are the go-to among friends for fixing their technical problems.
Formerly employed or shadowed in the healthcare space: you have an insider understanding of the vernacular, organizational structure, and operational processes of hospitals
Benefits:
Experience working with some of the leading experts in robotics
Potential to radically change the future of healthcare
Be part of a team environment
Free parking
Insurance including medical, dental, and vision
401K
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
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Listed by Diligent Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.
Fleet Engineer
Diligent Robotics · Austin, Texas, United States
Pay
$95k–165k
Setting
On-site
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Fleet Engineer
Austin, Texas, United States
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What we’re doing isn’t easy, but nothing worth doing ever is.
Diligent builds helpful robots that work safely and autonomously in real world environments. We move quickly, solve messy problems, and care deeply about reliability at scale. As a Fleet Engineer, you'll own the reliability and continuous improvement of our deployed robotic fleet — leading hands-on investigations into how and why robots fail in the field, across the mobile base, charging/docking, motion and power, connectivity (modem), and sensor hardware. You'll combine remote data analysis with bench/lab failure analysis at our Austin HQ, turning field-technician reports and fleet data into clear problem statements, validated root causes, and corrective actions driven to closure with engineering, operations, manufacturing, and vendors.
This role is based in Austin, TX. It will require 15-20% travel along with close collaboration across software, hardware, operations, and product engineering teams.
Key Responsibilities
Fleet Reliability & Hands-On Debugging: Lead triage and bench/lab failure analysis across the mobile base, charging/docking, motion and power, connectivity, and sensor hardware —
getting hands-on with returned units to reproduce, instrument, and isolate the failure.
Root Cause Analysis: Diagnose failures from component level (electrical, mechanical, firmware) to system level, applying standard methodologies (5-why, fishbone, fault-tree, FMEA, 8D).
Data-Driven Investigation: Pull and analyze fleet data and logs to define problem statements, surface trends, and validate hypotheses quantitatively — accounting for confounding factors, base rates, and sample size.
Field Synthesis & On-Call: Turn field-technician reports into crisp problem statements; own escalated issues (on-call) to support the field team and minimize downtime.
Corrective Action & Cross-Functional: Drive short- and long-term fixes (hardware, software, operational, process) to closure with engineering, operations, and product — including supplier corrective actions and design feedback with vendors and manufacturing.
Tooling & Test Infrastructure: Build the fixtures, instrumentation, and bench test setups that accelerate debug workflows.
Documentation & Standards: Document debugging procedures and root-cause findings; contribute to fleet reliability standards.
Growth: Raise the team's investigative rigor, work closely with technicians, and grow into mentoring over time.
What Success Looks Like
Improved FPY and reduced rework rates across production builds.
Reduced per-unit cycle time for test/provisioning while increasing test coverage.
Stable, fully automated provisioning flow with minimal manual intervention.
Basic Qualifications
Hands-on electrical debugging — schematics, multimeter/oscilloscope, power, connector and harness fault isolation, basic instrumentation.
Hands-on mechanical debugging — mechanisms, tolerances and fits, fixturing, dimensional/force measurement, mechanical drawings; able to pinpoint what is physically wrong with a unit.
Hypothesis-driven, quantitative debugging — frame the problem, design discriminating tests, reason about confounding factors, base rates, and sample size, and update conclusions when the evidence contradicts them.
System log analysis — read raw system/robot logs to reconstruct events and isolate failures (the backbone of most investigations).
End-to-end versatility — comfortable across subsystems, running an investigation independently to conclusion.
3+ years in robotics, autonomous vehicles, or complex electro-mechanical systems — or an adjacent field (medical devices, industrial automation, semiconductor and capital-equipment field service, automotive or aerospace Maintenance/Repair/Overhaul, EV charging). 5+ years / senior scope preferred.
Bachelor's in Electrical, Mechanical, Mechatronics, Robotics, or a related engineering field (required); Master's a plus.
Preferred Qualifications
Experience with robotics stacks (ROS or equivalent) and robotic sensor calibration/test.
Experience with deployed robot or autonomous-vehicle fleets.
Networking (Ethernet, CAN bus, time-sync) and firmware familiarity.
Driving supplier corrective actions and design feedback with vendors and manufacturing.
Hardware-in-the-loop test and validation-rig design.
Compute platforms (NVIDIA Jetson/Orin, GPUs).
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Listed by Diligent Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.
Manufacturing Reliability Engineer
Diligent Robotics · Milwaukee, Wisconsin, United States
Pay
$95k–165k
Setting
On-site
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Manufacturing Reliability Engineer (Based in Milwaukee; Relocation assistance available)
Milwaukee, Wisconsin, United States
Apply
What we’re doing isn’t easy, but nothing worth doing ever is.
Diligent builds helpful robots that work safely and autonomously in real world environments. We move quickly, solve messy problems, and care deeply about reliability at scale. We’re hiring a Manufacturing Reliability Engineer to own production test for our robots at our contract manufacturer: you’ll design and run robust end-to-end test protocols, provision fleets of robots for production, and own the KPIs that define production quality.
This role is based in the Milwaukee, WI area and requires close collaboration across software, hardware, operations, and product engineering teams.
We welcome applicants from outside Milwaukee who are willing to relocate. Relocation assistance/bonus will be a part of compensation package.
Key Responsibilities
End-to-end test process ownership. Create, validate, and maintain production test protocols and gating criteria from incoming inspection through final test and shipment.
Provisioning of bots. Design and operate provisioning flows (imaging, firmware deployment, configuration, validation) and the tooling/fixtures needed to provision and handoff robots for production.
KPIs and continuous improvement. Own key production metrics — First Pass Yield (FPY), cycle time, and test coverage — and drive continuous improvements to meet throughput and quality targets.
Test automation & infrastructure. Architect, implement, and maintain automated test frameworks, harnesses, and test rigs used at the CM site. Ensure tests are stable, fast, and provide actionable failure data.
Cross-functional escalation & RCA. Lead root-cause analysis for field and production failures; coordinate corrective actions with design, firmware, and CM engineering to close quality loops.
On-site production leadership. Be the onsite technical authority at the contract manufacturer: train operators, debug failures on the line, and continuously refine processes with CM partners.
What Success Looks Like
Improved FPY and reduced rework rates across production builds.
Reduced per-unit cycle time for test/provisioning while increasing test coverage.
Stable, fully automated provisioning flow with minimal manual intervention.
Basic Qualifications
5+ years experience in production/test engineering for complex electro-mechanical products (robotics, medical devices, consumer electronics, or similar).
Proven ownership of E2E production test processes and production provisioning at a contract manufacturer (on-site experience strongly preferred).
Deep familiarity with test protocol development: test specification writing, fixture design, automated test implementation, and validation.
Strong hands-on experience with firmware provisioning and validation for sensors and compute (sensor FW + compute/host FW).
Broad technical knowledge across systems integration (ES integration), sensors (e.g., cameras, LiDAR, IMU or similar), embedded compute platforms, and networking/provisioning.
Excellent debugging skills across hardware, firmware, and system software; able to triage complex cross-domain failures.
Strong data-driven mindset: experienced with KPIs (FPY, cycle time, test coverage), statistical process control, and metrics reporting. Outstanding communication skills and experience working closely with contract manufacturers, operators, and cross-functional engineering teams.
Comfortable being onsite full-time at a contract manufacturer and traveling as needed.
Preferred Qualifications
Experience with robotics stacks (ROS or equivalent) and robotic sensor calibration/test.
Familiarity with manufacturing automation tools, CI for embedded systems, and test frameworks (Python, pytest, LabVIEW, or similar).
Experience with provisioning large fleets (device identity, secure image signing, automated configuration).
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Listed by Diligent Robotics for a position based in the United States. Employers on this board attest they are hiring domestically.
ML Engineer, Manipulation
Diligent Robotics · Anywhere in the US
Pay
$135k–270k
Setting
Remote
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ML Engineer, Manipulation
Anywhere in the US
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What we’re doing isn’t easy, but nothing worth doing ever is.
We envision a future powered by robots that work seamlessly with human teams. We build artificial intelligence that enables service robots to collaborate with people and adapt to dynamic human environments. Join our mission-driven team as we build out current and future generations of robots.
As an ML Engineer, Manipulation, you will develop and deploy learning-based manipulation systems that enable mobile robots to interact reliably with the physical world in dynamic human environments. You’ll build perception-to-action models, training datasets, evaluation tooling, and deployment pipelines that improve robustness, generalization, and safety for real-world manipulation tasks at scale. Your work will directly impact the robot’s ability to perform complex interactions consistently across real sites with minimal special-case engineering.
Responsibilities
Develop learning-based manipulation models for end to end sensor-driven interaction (e.g., reaching, motion generation, and execution in dynamic environments).
Build and maintain manipulation training pipelines: dataset creation from robot logs/teleop, action representations, augmentation, and distributed training.
Design evaluation metrics and regression tests that quantify manipulation reliability, recovery behavior, and safety in real environments.
Develop sim-to-real workflows for manipulation learning, including simulation environments, domain randomization, and failure-mode testing.
Optimize and distill models for edge deployment; benchmark latency, memory use, and stability on target hardware.
Partner with the AI platform team to integrate policies with control and safety systems, and validate end-to-end performance on robots.
Analyze field performance, identify dominant failure modes, and drive iterative improvements through data collection and targeted retraining.
Basic Qualifications
Bachelor’s or Master’s degree in Robotics, Computer Science, Electrical Engineering, or related field (PhD a plus).
3+ years of experience applying ML to robotics manipulation, visuomotor control, or sequential to sequence models.
Strong proficiency in PyTorch and experience building reliable training/evaluation pipelines.
Strong software engineering skills in Python; ability to collaborate across ML and robotics teams.
Preferred Qualifications
Experience with Vision-Language-Action (VLA) models, behavior cloning, and/or transformer/diffusion policies for robotic control.
Experience with sim-to-real training for manipulation (Isaac Sim/Mujoco or similar), including domain randomization and synthetic data.
Experience deploying ML models to edge hardware (ONNX/TensorRT, quantization, performance profiling).
Familiarity with safety-critical robotics integration and designing fallback/recovery behaviors.
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Please provide a link to your LinkedIn profile, Google Scholar and/or Github*
Describe your experience developing robotic manipulation systems, including dexterous manipulation and grasping, using classical robotics approaches and/or learned policies (e.g. imitation learning, reinforcement learning, diffusion policies, VLA-driven manipulation).*
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