Staff ML Engineer — Production AI & Systems, Equity

Hive; Seattle (WA); Hybrid; USD 200,000 - 300,000; Full time

Benefits offered by this job

Health insurance

Vision & Dental insurance

Gym membership

Job summary

Deploy seeks a Staff Machine Learning Engineer to advance production ML models, design scalable data pipelines, and deliver robust backend services. You will mentor teammates and collaborate across teams to shape the ML platform.

You should have deep experience in distributed systems, ML frameworks like PyTorch/TensorFlow, and Python scripting, with a track record of end-to-end product development in fast-paced environments.

Qualifications

• Bachelor's degree in computer science or related field.
• 8+ years of experience building web applications and distributed systems.
• Experience delivering scalable backend APIs.
• Expertise in ML frameworks (PyTorch or TensorFlow).
• Strong communication and leadership with ability to mentor teammates.
• Ability to lead end-to-end product development of new ML-powered solutions.

Responsibilities

• Apply ML models to production use cases including design, data gathering, training, and deployment.
• Write scalable, high-performance backend code shared across platforms.
• Contribute to core backend systems and product direction.
• Improve engineering standards, tooling, and processes.
• Develop novel ML algorithms for scale and performance.
• Conduct metric-driven experiments to improve model performance.
• Mentor ML engineers and onboard new team members.
• Lead cross-functional collaboration across teams.

Skills

Distributed systems

Backend APIs

Leadership

Mentorship

Communication

Model deployment

Education

Bachelor's Degree in CS or related

Tools

PyTorch

TensorFlow

Python

Shell scripting

Job description

Deploy seeks a Staff Machine Learning Engineer to advance production ML models, design scalable data pipelines, and deliver robust backend services. You will mentor teammates and collaborate across teams to shape the ML platform.

You should have deep experience in distributed systems, ML frameworks like PyTorch/TensorFlow, and Python scripting, with a track record of end-to-end product development in fast-paced environments.