- Job Type: Full-Time
- Function: Data Science
- Industry: Teleconferencing
- Post Date: 01/13/2022
- Website: www.commonground-ai.com
- Company Address: Tel Aviv
About Common GroundCommon Ground, founded by 2 serial entrepreneurs with more than 30 years of experience in the video compression field, aims to create an immersive and 3D Telepresence application to disrupt the existing video conference ecosystem and bring people together through Telepresence.
Great opportunity to join the founding team of a well-funded startup and to design and
develop the backbone infrastructure and delivery pipeline of our first software product.
What will you do?
As our Data engineering / MLOps engineer you will be part of the data engineering team, working closely with our DevOps lead as well as software developers and researchers.
In this role, you will be a key contributor in building our ML frameworks, Design our production architecture, and allow ML models.
- Design and build MLOps systems, including data pipelines and production-level machine learning (ML) infrastructure, using tools such as Kubeflow Pipelines, Kubernetes, etc.
- Leverage your experience to drive best software development practices in ML systems.
- Bring ML research from to production. Deploy ML models under the constraints of scalability, correctness, and maintainability, with hardware acceleration techniques.
- Optimize and give feedback to research-level models to bring them to production level.
- Collaborate with cross functional agile teams of machine learning engineers, video engineers, data engineers, and others, in building machine learning infrastructure that best supports the ML needs at CommonGround.
- Passionate about Data science and Machine learning products.
- B.S. degree in Computer Science or a related technical field.
- Proficient in working with cloud platforms, dockers, and Kubernetes.
- Highly proficient in python, with the ability to build APIs.
- 2+ years of machine learning product development experience, using state-of-the-art tooling and having a deep understanding of the best practices for ML systems.
- Experience in building high performance distributed systems at scale.
- Skilled communication and a proven record of leading work across disciplines.
Good to have:
- Experience in working with Video processing and 3D.
- Familiarity with MLOps tools and experiment management platforms such as KubeFlow.
- Experience with Deep Learning Frameworks such as TensorFlow, Pytorch.
- Experience with data processing and storage frameworks.
- Ability to architect data pipelines using MLOps tools.