Design and maintain ML-Ops platforms, enhancing observability and scalability of ML applications while ensuring high code quality and collaboration with engineering teams.
Our Mission
Our mission is to solve the most important and fundamental challenges in AI and Robotics to enable future generations of intelligent machines that will help us all live better lives.
Machine Learning Operations (ML-Ops) Engineers build infrastructure that supports the entire lifecycle of Machine Learning (ML) projects from development to scaling and to deployment. If you have a passion for building the foundation that enables robotics research and engineering, you will want to join us!
What You Will Do
- Design, develop, and maintain company-wide platforms and tooling that utilize Kubernetes infrastructure to enable machine learning and data processing applications
- Enable self-service access to ML-compute for our on-prem and cloud compute clusters, including support for job scheduling, workload scalability and workload fault tolerance
- Enhance observability across ML applications through integrations with tools and services such as FluentD, Prometheus, Grafana and DataDog
- Integrate ML applications with experiment tracking and management services like Weights and Biases
- Elevate code quality and champion best practices in our engineering processes
- Collaborate with Machine Learning Engineers, Data Engineers, DEVOPs engineers and researchers to build scalable solutions that improve engineering and research velocity.
What You Will Bring
- BS or MS in Computer Science, Engineering, or equivalent
- 3+ years of experience in an MLOPs, DevOps, ML Engineering or software engineering role
- Strong hands-on experience deploying and managing applications running on Kubernetes
- Experience developing MLOPS platforms to manage the lifecycle of ML experiments; including one or more of data and artifact management, reproducibility, fault-tolerance, experiment tracking and model serving
- Experience with Docker and Python environment management tools such as pip, poetry, uv or similar
- Proficient in software practices such as version control (Git), CI/CD (Github Actions, ArgoCD), Infrastructure as Code(Terraform).
Extra Skills We Value
- Experience with Kueue, or similar job scheduling mechanisms
- Experience with workflow orchestration tools such as Airflow, Metaflow, Argo Workflows or similar
- Hands-on experience deploying and managing cloud infra on platforms like GCP and AWS
- Experience with hybrid-cloud compute and data environments
- Experience with Ray, Pytorch Lightning or similar scalable AI/ML platforms
- Experience with application and system, logging with tools and services like FluentD, Prometheus, Grafana and DataDog or similar
- Experience with Bazel build tool or similar
- Experience with ML model serving frameworks such as Torchserve, ONNX runtime or similar
- Experience working with research teams in an academic or industrial environment.
We provide equal employment opportunities to all employees and applicants for employment and prohibit discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Top Skills
Airflow
Argocd
AWS
Bazel
Ci/Cd
Datadog
Docker
Fluentd
GCP
Git
Grafana
Kubernetes
Metaflow
Onnx Runtime
Prometheus
Python
Pytorch Lightning
Ray
Terraform
Torchserve
Weights And Biases
RAI Institute Cambridge, Massachusetts, USA Office
145 Broadway, Cambridge, MA, United States, 02142
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