At a Glance
- Tasks: Build and manage data pipelines for robotics and AI datasets, ensuring quality and readiness.
- Company: Join Miraxis, a pioneering company in robotics data solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative remote team with a focus on innovation and quality.
- Why this job: Make a real impact in the robotics field with cutting-edge technology.
- Qualifications: Experience with robotics datasets and strong Python skills required.
The predicted salary is between 60000 - 80000 £ per year.
Miraxis is building the rights-cleared data factory for robotics and physical AI. A key differentiator is turning messy, heterogeneous real-world robotics data into training-ready datasets with verifiable quality. As Robotics Data Pipeline Engineer, you will own the multimodal pipeline layer: ingestion, transformation, validation, QA gates, and delivery packaging. You should be able to talk shop with vendors/partners/clients on best practices (formats, sync, calibration metadata, labeling, eval outputs) and also build the tooling to manipulate and audit datasets directly.
What you’ll do (Responsibilities)
- Build and operate multimodal pipelines for robotics/physical AI datasets: ingestion, transformation, validation, and delivery packaging.
- Define “training-ready” as enforceable checks: alignment validation, integrity checks, schema enforcement, and reproducibility standards.
- Build tooling to inspect, transform, and audit datasets (large files, long-running jobs, real-world edge cases).
- Collaborate with Ops/Delivery and Hardware to turn external constraints into concrete pipeline requirements.
- Maintain clear documentation (schemas, runbooks, data contracts) so a remote team can operate consistently.
What we’re looking for
- Hands-on experience with robotics/physical AI datasets (multimodal: video + sensors + proprioception) and their failure modes.
- Strong Python and data engineering instincts: validation, reproducibility, and careful handling of messy real-world data.
- Comfort working at the intersection of software and domain: can reason about timing/sync, calibration metadata, and the practicalities of capture pipelines.
- Able to communicate clearly with both engineers and external stakeholders; converts ambiguity into executable specs.
Nice to have
- Experience with ROS/ROS2 data formats (bags) or other robotics logging systems.
- Familiarity with simulation/teleoperation datasets, annotation/labeling workflows, and evaluation harnesses.
- Experience building QA frameworks that surface issues early (before downstream training).
Robotics Data Pipeline Engineer employer: Miraxis AI
At Miraxis, we pride ourselves on being an innovative employer that fosters a collaborative and dynamic work culture, perfect for those passionate about robotics and AI. Our commitment to employee growth is evident through continuous learning opportunities and the chance to work on cutting-edge technology in a supportive environment. Located in a vibrant tech hub, we offer unique advantages such as networking with industry leaders and access to state-of-the-art resources, making us an excellent choice for professionals seeking meaningful and rewarding careers.
StudySmarter Expert Advice🤫
We think this is how you could land Robotics Data Pipeline Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the robotics and AI space on LinkedIn or at industry events. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your work with multimodal pipelines or any relevant projects. We love seeing practical examples of how you’ve tackled real-world data challenges.
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of data validation and QA frameworks. We want to see how you can turn complex problems into clear solutions, so practice explaining your thought process.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always on the lookout for passionate candidates who are ready to dive into the world of robotics and AI.
We think you need these skills to ace Robotics Data Pipeline Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Robotics Data Pipeline Engineer. Highlight your hands-on experience with robotics datasets and any relevant projects that showcase your skills in Python and data engineering.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about robotics and physical AI. Share specific examples of how you've tackled messy data or built pipelines, and don’t forget to mention your ability to communicate with both engineers and stakeholders.
Showcase Your Technical Skills:In your application, be sure to highlight your technical expertise, especially in areas like validation, schema enforcement, and QA frameworks. We want to see how you can turn real-world challenges into executable solutions!
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates directly from us. Plus, it’s super easy!
How to prepare for a job interview at Miraxis AI
✨Know Your Data Inside Out
Make sure you’re well-versed in the types of robotics and physical AI datasets. Be ready to discuss specific examples of ingestion, transformation, and validation processes you've worked on. This will show that you can handle messy data and understand its failure modes.
✨Show Off Your Python Skills
Brush up on your Python programming skills, especially in relation to data engineering. Be prepared to talk about how you've used Python for validation and reproducibility in past projects. If you can share code snippets or examples, even better!
✨Communicate Like a Pro
Since you'll be collaborating with both engineers and external stakeholders, practice explaining complex concepts in simple terms. Think about how you would convert technical jargon into actionable specs that everyone can understand.
✨Prepare for Real-World Scenarios
Expect questions about real-world edge cases and how you would handle them. Think through potential challenges in pipeline operations and have some strategies ready to discuss. This will demonstrate your practical understanding of the role.
