Staff Machine Learning Engineer

Censys; Kirkland (WA); Remote; USD 190,000 - 250,000; Full time

Job summary

An innovative company is seeking a Staff Machine Learning Engineer to build and maintain a robust MLOps platform that handles petabyte-scale data. This role involves deploying containerized workloads, optimizing machine learning models, and collaborating with cross-functional teams to enhance data insights. The ideal candidate will have extensive experience in Python, Docker, and MLOps tools, contributing to impactful machine learning applications in the cybersecurity domain. Join a dynamic team dedicated to empowering customers with cutting-edge solutions and enjoy a competitive salary package with excellent benefits.

Benefits

401k Match

Health Insurance

Vision Insurance

Dental Insurance

Equity Options

Flexible Work Environment

Qualifications

• 5+ years of experience in Docker, Kubernetes, and Helm.
• Strong proficiency in Python and machine learning libraries.

Responsibilities

• Deploy and maintain containerized workloads for machine learning.
• Collaborate with teams to design data pipelines for petabyte-scale data.

Skills

Python

Docker

Kubernetes

Machine Learning Libraries (PyTorch, Transformers)

MLOps Tools (Metaflow, MLflow)

Streaming Data Processing (Kafka, Spark)

Model Optimization Techniques

Education

Bachelor's Degree in Computer Science

Equivalent Professional Experience

Tools

Helm

Argo Workflows

Grafana

Prometheus

Job description

Location

This position can beremote within the United States, but employees have the option to work from any of our office locations: Ann Arbor, MI; Los Altos, CA; Seattle, WA; or Tysons, VA.

Role Summary

Censys is looking for a Staff Machine Learning Engineer to join our team and help us derive valuable insights from internet security datasets. This role is responsible for building and maintaining a robust internal Machine Learning Operations (MLOps) platform capable of handling petabyte-scale data and delivering high-throughput, low-latency predictions. You will engineer and optimize this platform to support a diverse range of machine learning applications, including computer vision, natural language processing, and reinforcement learning, ensuring seamless deployment and scalability

We’re a highly collaborative, creative team dedicated to empowering customers with innovative solutions to improve their security posture. You’ll work closely with data scientists, engineers, and product teams to build scalable systems that enhance how customers interact with and understand our data. You will design machine learning systems that not only process and analyze data but also bring critical insights to the surface in a way that empowers decision-making.

What You’ll Do

• 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.

Qualifications

• 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.

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.

Our target salary range for this role is between $190,000 USD and $250,000 USD + bonus eligibility and equity.

In addition to our great compensation package, our benefits are effective on day one and include but are not limited to: 401k match, health, vision, dental, and more! Please see our careers page for more details.

Our innovation is fueled by the team’s global perspectives.We are open to remote employees for this role; however, we offer four hub locations that employees are welcome to use: Seattle, WA; Los Altos, CA; Tysons, VA; or Ann Arbor, MI.

#LI-Remote