Strong mathematical skills, with a focus on algorithms and geometric calculations.
Proficiency in Python, including use of libraries like NumPy and Pandas.
Experience with cloud platforms (AWS or GCP) for resource management.
Independent problem-solver with effective communication skills.
Bachelor's degree in Computer Science or a related field.
At least 5 years of software industry experience.
Familiarity with relational and NoSQL databases such as PostgreSQL and Redis.
Responsibilities
Develop and implement algorithms for processing video and sensor data.
Utilize ML models to extract signals from data effectively.
Build and maintain robust data pipelines and workflows.
Collaborate with cross-functional teams to identify data needs.
Enhance machine learning models with high-quality processed data.
Contribute to backend systems and APIs as required.
Leverage cloud computing for scalable optimization.
Benefits
Opportunity to work on cutting-edge robotic technologies.
Collaborative work environment with industry experts.
Engagement in challenging engineering problems.
Potential for professional growth in a pioneering company.
Access to advanced tools and technologies for software development.
Full Job Description
The Role
As a Software Engineer, Data Infra you are the architect of the "Laboratory" where Dyna's robotic intelligence is refined. You won't just move data; you will build the interactive systems that bridge the gap between raw multimodal sensor streams and production-ready ML models.
This is a high-impact, hands-on role where you will design the infrastructure to visualize model performance, automate data labeling, and manage the "human-in-the-loop" workflows - such as teleoperation and skill capture - that are critical to our scaling. If you enjoy solving complex geometric problems and building the low-friction tools that empower ML researchers, you'll thrive here.
What You'll Do
Interactive Data Systems: Architect the engines and interfaces that unify raw robot logs, video, and 3D sensor data. You will enable seamless "human-in-the-loop" workflows, from episode annotation to analyzing manual interventions.
Signal Extraction & Geometry: Design and implement algorithms to extract structured signals (trajectories, events, 3D poses) from raw captures. Strong mathematical intuition is required to turn pixels and point clouds into ground-truth insights.
Evaluation & Benchmarking: Build high-performance tools to compare model-driven motion against human-captured data, helping the team quantify model progress across diverse tasks.
Scalable ML Pipelines: Build and operate distributed data pipelines (using Python, GCP/AWS, and Kubernetes) for the ingestion, transformation, and validation of terabytes of multimodal data.
Observability & Debugging: Develop visualization tools that make complex model behaviors and sensor data easy to interpret, reducing the time from "data collected" to "model trained."
Startup Fluidity: Collaborate across ML, Robotics, and Product teams. As an early member of the data team, you will help define the roadmap where no blueprint yet exists.
What You'll Bring
The Experience: 5+ years of professional software experience, ideally with a focus on data-intensive or "human-in-the-loop" platforms.
Technical Stack: Proficiency in Python (NumPy, Pandas) and a solid understanding of modern backend architectures. Experience with React/TypeScript is a major plus for building internal observability tools.
Mathematical Core: Strong skills in algorithms and geometric calculations (e.g., coordinate transformations, 3D spatial reasoning).
Data Mastery: Hands-on experience with relational and NoSQL databases (PostgreSQL, Redis) and cloud-native infrastructure (GCP/AWS).
Problem-Solving: The ability to debug real-world data issues - from sensor drift to pipeline bottlenecks - independently.
Bonus Points For:
Experience with multimodal data (video, LiDAR, time-series) in robotics or autonomous systems.
Familiarity with Airflow, Kubeflow, or similar distributed batch processing systems.
Experience with experiment tracking frameworks like Weights & Biases or MLFlow.
Experience as an early hire in a fast-paced startup environment.
