We are living in a world where machines are becoming smarter every day. They are taking over mundane yet crucial tasks (such as driving) and letting humans focus on living a more creative, fruitful life. Training these machines to perceive the physical environment and make mission-critical decisions is a complex task that heavily relies on using massive amounts of perception data. The large volume, high variety, and lack of structure in the perception data pose a crippling data operation problem for teams that build these smart machines. We want to solve this problem.
SceneBox is committed to simplifying machine learning (ML) workflow management for perception-heavy applications such as autonomous vehicles, automated drones, robotics, and video surveillance. By focusing on finding the most valuable training data, SceneBox platform helps perception teams:
- build better training datasets faster
- focus on core ML tasks rather than data operations
- reduce ML product time to market
Our ideal candidate is mission-driven, passionate, meticulous, and takes ownership in building world-class products.
As a team, we will make sure you learn every hour of every day, support you to take risks and make bold decisions, and enable you to have access to the best technical and scientific resources through our top-notch investors, and advisors in Silicon Valley and Canada.
Location: Canada or US - Remote
The Data Engineer will be responsible for developing data pipelines and data processing modules for our SceneBox platform, implementing DevOps best practices, and ensuring the platform availability and uptime.
We are hiring a Data Engineer with enterprise-level software design, architecture, and development experience and with expertise in building cloud platforms and deploying cloud-based microservices applications. The engineer will:
- Design and implementation of Caliber’s flagship product
- Implement cost-effective and scalable infrastructure for ML/AI workflows for large-scale perception applications.
- Help in maintaining our core data platform.
- Masters in computer engineering, computer science, or a related field
- 5+ years of solid hands-on software development experience with a focus on continuous delivery and deployment, enterprise application development, cloud automation, and building a container-hosting platform
- Software programming experience in one or more programming languages: Python (must have), Java/Scala (good to have)
- Experience with Docker, Kubernetes, or similar solutions
- Experience in streaming applications such as Kafka, Apache Samza, Spark Streaming, etc.
- Experience building cloud-based application using microservices and deploying in containerized environments
- Excellent knowledge of building server-side RESTful applications, APIs and automation tools
- Database experience: Redis, Elasticsearch, SQL, NoSQL
- Understanding of Software design patterns, SDLC, Test Driven Development (TDD), Continuous Integration and Continuous Delivery
- Experience working in an Agile development environment