ML Engineer

ML Engineer

Vacancy expired!

Role: ML Engineer
Location: 100% remote

Job Description:
In the role of ML Operations (MLOps), you will work closely with the Infrastructure team and the ML Engineering team to build tools and infrastructure that increase the speed of model development and deployment, while improving our capacity to deploy, scale, and support additional models as we grow. This is a long-term role to be an early employee in a startup with seed funding from a DOD contract, and the company's leadership team is actively raising a larger round to continue growing the business.
To be successful in this role, you must have experience deploying and scaling infrastructure using Docker containerization and Kubernetes orchestration in a production environment. Top candidates will have experience deploying large-scale machine learning models.

Qualifications:

• This role requires that the applicant is a skilled python developer with solid Kubernetes experience
• BS/MS in Computer Science or related field, or equivalent industry experience (preferred). In our
• environment, equivalent experience is prioritized over formal education
• Ability to demonstrate CI/CD, Continuous Training/Serving/Monitoring ML pipeline process
• Knowledge of building machine learning and NLP models
• Experience with creating ML models to run in batch as well as machine learning deployment and
• maintenance
• Strong understanding and experience working with tools like:

• Cloud Provider: AWS or Google Cloud
• ML: TensorFlow, PyTorch, Transformers
• ML Tooling: GPU Drivers, CUDA Runtimes, Multi-GPU Training
• ML Optimization: Model Inference, Tensorflow Serving, Triton, Onnx, TensorRT
• Databases: Postgresql, RDS, Redis
• Deployment: Containerization (docker, containerd)
• Pipeline orchestration: KubeFlow, Argo Workflow, ArgoCD, Drone CI
• Other tools: GitHub, Jira, Confluence, Prometheus, REST API

• Understanding of microservices and multi-tier architecture
• Ability to adapt and test new technologies based on requirements
• Ability to quickly learn new skills related to the role
• Strong problem-solving skills

• Deploy machine learning models into production and work in collaboration with the infrastructure and Engineering teams
• This position will support all projects that uses ML methods to enable scalable and repeatable model deployments
• Develop and maintain infrastructure to support ML model development, training and inference
• Monitoring and debugging any issues that pertains to the ML infrastructure
• Create tools, process or workflows that help the team operate more efficiently
• Refactor code to improve the speed and performance of models