
Robotics Engineer
Dallas, TX at a glance
- Rent
- #25 of 51$2,300/mo-15% vs US avg
- Weather
- #13 of 51174 mild days138 hot · 0 cold
- Income tax
- #51 of 51NoneTexas
What you need
- Taught robot manipulation task on real hardware
- Strong Python, comfortable patching research code
- Rigorous evaluation: count trials, log failures
- Hands-on with real hardware: calibration, cabling
- Collaborate with mechanical design on hand design
What you'll do
- Own robot learning end-to-end for reliable fish handling
- Build real-world evaluation, measure success rates, debug failures
- Create repeatable data collection, calibration, versioning infrastructure
- Integrate newer robot-learning approaches into production robots
- Travel 20-25% to farms for testing and deployment
Teach robots to handle fish: wet, slippery and soft. You will own robot learning end to end, until robots work reliably on real hardware, not just in a demo video.
OctaPulse builds computer vision and robotics for aquaculture. Fish farming is one of the fastest-growing food production systems on the planet, but much of its most important work is still done by hand, one fish at a time. Our vision systems already work on fish farms and processing lines. Now we are putting robots next to them.
Fish are a hard thing to handle. They are wet and slippery, they deform when you pick them up, and no two are alike. A rigid gripper and a clean tabletop demo do not get you far. We already have robots working on real hardware. The next job is making them reliable and measured.
You will be one of the first robotics hires. You will own robot learning end to end and work closely with mechanical design. This is not a research role. We care about robots that work on the hundredth trial, not the best video.
The role is on-site in Dallas (Frisco area), with up to 20 to 25% travel to farms and processing sites to see the real work and test where the robots will operate.
What you'll build
- Reliable, gentle handling. Take robot handling from "it worked on a good run" to a measured success rate. Build an honest real-world evaluation, report results the team can trust, understand why things fail, and grow the data that fixes them. Then grow the task toward the messier, faster work real plants need.
- Data collection as infrastructure. Make collecting data, calibrating, and versioning what we learn from boring and repeatable, so every new task starts faster than the last.
- Toward more capable robots. As tasks involve more choices and more variety, help bring newer robot-learning approaches into real work, move from early prototypes to robots built for food plants, and contribute to longer-horizon work on soft, gentle manipulation.
You might be
- Someone who has taught a robot a manipulation task and run it on real hardware, not only in simulation. You can name the task, the data and what broke first.
- Strong in Python, and comfortable reading and patching research code.
- Rigorous about evaluation. You count trials, log failures and do not trust a single good video.
- At home with real hardware: calibration, cabling and the things that come loose.
- Happy to work beside mechanical design. You know the hand often matters more than the software, and you can tell whether a failure belongs to the data, the learning or the hardware.
- Fine with getting wet. You will travel up to 20 to 25% of the time to farms and processing floors, which are cold and loud.
- Able to work on-site in Dallas, TX (Frisco area). This is one of our first robotics hires, reporting to our CTO. It is not a founding-engineer role.
Bonus
- You have worked with recent robot-learning approaches or contributed to open-source robotics tools.
- Experience handling soft or deformable objects, in the real world or in simulation.
- You have designed or co-designed a robot hand or end effector.
- Experience running experiments on real hardware, not only in simulation.
- Industrial robot integration and safety: safety-rated stops, guarding, working near people.
- Experience with grasping delicate objects.
- Food processing, agriculture or other messy, non-lab deployment experience, including hygienic or food-contact design.
Details
Location: Dallas, TX (Frisco area) Workplace: On-site Type: Full-time Compensation: $130k to $165k + equity
Interview Process
- We read every application ourselves.
- If there is a fit: a 30-minute call with a founder.
- A technical conversation about real work, yours and ours.
- We reply to everyone, either way.
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About OctaPulse
OctaPulse uses AI vision to automate hatchery QA for fish farms, starting with broodstock phenotyping and juvenile deformity inspection. We cut inspection time from about 5 minutes to under 30 seconds per fish, with more than 90 percent accuracy, so farms advance only high quality fish and waste less feed and labor. Our goal is to bring automation across the entire fish production lifecycle for the $300B aquaculture industry. Aquaculture is the fastest growing food sector, and has already surpassed commercial fishing for production of seafood. Yet two of the most critical QA and QC steps in fish production are still done by hand: phenotyping and deformity inspection. These processes are slow, error prone, and despised by technicians but vital for the success of the farm, so much so that farms spend >$200K a year on trained technicians and geneticists to operate. We’ve built an AI vision platform for vertically integrated finfish farms that drops into existing workflows starting in the hatchery, uses off the shelf cameras, standardizes these QA steps, and creates a proprietary multi species dataset that becomes the brain for future autonomous aquafarms. Phenotyping is the start, but the platform is designed to be a drop in solution that can easily expand into feeding, health monitoring, and processing so we can solve production problems across the lifecycle. We signed a 6-figure paid pilot with the largest trout producer in the United States, are deploying into 2 more farms early 2026, and trained models above 90 percent accuracy while cutting inspection time from 5 minutes to under 30 seconds. Each deployment adds labeled images to our dataset and improves cross species generalization.
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