Title:
Senior Software Engineer - ML/Data Infrastructure
About UnitX:
UnitX is building the world’s best robotics product to accelerate human productivity in manufacturing. UnitX is a fast-moving startup with a team from Stanford and Google. Since inception, UnitX has shipped 1000+ mission-critical systems across 100+ of the world's leading manufacturers' production lines. Every year, $2.8B dollar worth of products (think EV batteries) go through UnitX AI inspection system to ensure quality.If you are a customer-obsessed, first-principle problem solver who loves getting into the technical nitty gritty to deliver compelling, quantifiable, and scalable value to our customer within the 24/7 mission-critical manufacturing world, there is truly no better place to do it than UnitX. We have assembled a world-class team of problem solvers in Silicon Valley and would love to talk to you.
Role Overview:
We’re looking for a Senior Software Engineer – ML/Data Infrastructure to lead the design and development of scalable systems that support our ML workflows and infrastructure, including data collection, annotation, model training, deployment, and observability. You will own critical backend components and collaborate closely with ML engineers, 2d/3d vision engineers, and application developers to create a rock-solid foundation for production AI.Sample projects:
- Introduce TensorRT into our edge inference system to speed up inference performance and repeatability
- Optimize Generative AI pipeline reliability and scalability on our cloud-based Generative AI product
- Integrate open-source AI data cleaning pipeline with our internal data and ML experiment system
What You’ll Do:
- Design and build scalable pipelines for data ingestion, preprocessing, and annotation from 2D/3D vision systems
- Develop infrastructure for distributed model training, versioning, and experimentation tracking
- Build robust APIs and tools for ML model deployment, monitoring, and rollback
- Integrate with robotic and edge devices to support real-time ML inference in production
- Ensure data quality, reproducibility, and system observability across the ML lifecycle
- Collaborate across teams to define and implement best practices for ML Ops and Data Engineering
- Mentor junior engineers and contribute to the evolution of a high-performance engineering culture
Our Tech Stack:
- Technical Domains: Deep learning, Computer vision, 3D imaging, Hardware, Edge computing, Cloud computing, Realtime systems, Industrial systems
- Frontend: React, Vue 3, TypeScript
- Backend: Python, Java, PostgreSQL, gRPC, PyTorch, OpenCV
What We're Looking For:
- Bachelor's degree in Computer Science, Computer Engineering, or a relevant technical field
- 5+ years of experience building backend or infra systems in data- or ML-intensive environments
- Deep understanding of distributed systems, data engineering, and ML lifecycle
- Proficiency with Python and backend architecture for data/ML systems
- Experience with edge deployment, hardware integration, or real-time systems
- Experience with containerization, orchestration, and cloud-native deployments
- Strong grasp of ML Ops best practices and experience supporting teams of ML practitioners
- Ownership mentality, excellent communication, and startup-ready agility
Benefits:
- Competitive salary & equity
- Unlimited PTO
- Full Medical, Dental, Vision, 401k
- Daily meals provided with your own choice
