Staff Machine Learning Engineer
| Company | Censys |
|---|---|
| Location | Seattle, WA, USA, Ann Arbor, MI, USA, Kirkland, WA, USA, Mountain View, CA, USA, Vienna, VA, USA |
| Salary | $190000 – $250000 |
| Type | Full-Time |
| Degrees | Bachelor’s |
| Experience Level | Senior, Expert or higher |
Requirements
- Bachelor’s degree in Computer Science, Data Science, Engineering, or other technical discipline (or equivalent professional experience)
- 5+ years of experience in docker, Kubernetes, and Helm
- Strong proficiency in Python and machine learning libraries like PyTorch, Transformers, and Timm
- Strong proficiency in MLOps tooling like Metaflow, MLflow, Argo Workflows, Torchrun, and Ray
- Experience with streaming data processing frameworks like Kafka and Spark
- Experience with applying model optimization techniques such as quantization, pruning, and distillation to improve performance
- Experience working with cloud platforms like AWS, GCP, and Azure
Responsibilities
- Deploy and maintain containerized workloads to support machine learning development, deployment, and post-deployment monitoring
- Utilize tools like Helm and Kustomize to accelerate the deployment of machine learning models and data pipelines
- Apply various optimization techniques such as compilation, quantization-aware training (QAT), and pruning to improve the latency and throughput of models
- Utilize open-source software like Metaflow, Prefect, Temporal, and Argo Workflows to facilitate data science development
- Build and optimize machine learning models to analyze security data, extract actionable insights, and identify trends, anomalies, and other relevant security signals
- Develop and maintain systems for drift detection and model monitoring to ensure continuous improvement and accuracy of insights
- Collaborate with cross-functional teams to design data pipelines that can efficiently process petabytes of raw internet security data
Preferred Qualifications
- Experience working in the cyber security domain
- Strong communication skills, including the ability to collaborate with both technical and non-technical stakeholders
- Experience with DevOps tooling like Grafana and Prometheus
- Proficiency in GoLang and Protocol Buffers