Production ML Engineer — Scalable Deep Learning Pipelines

Hive; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Deploy is building an intelligence infrastructure platform to give developers and enterprises access to AI models. We’re hiring ML engineers to prototype, train, and deploy state-of-the-art neural networks at scale, handling terabyte-scale datasets and integrating with data labeling services and DevOps.

The role involves delivering production-ready models, collaborating across teams, and staying current with deep learning research to improve accuracy and speed.

Qualifications

• Requires undergraduate or graduate degree in computer science or a similar technical field with significant math/statistics coursework.
• 1–2 years industry ML experience.
• Proven experience training and deploying a deep learning model into production with measurable performance gains.
• Strong experience with ML frameworks TensorFlow, Caffe, or Torch.
• Proficient in Python; capable of fast scripting and data pipeline prototyping using Python/Node/bash/Linux.
• Working knowledge of additional technologies (C++, Scala/Spark, SQL, Cassandra, Docker).

Responsibilities

• Apply an ML model to production, including design, data gathering, training, deployment at scale, and performance monitoring.
• Collaborate with Backend and DevOps teams and data labeling services.
• Use OWASP Top 10 techniques to secure code from vulnerabilities.
• Maintain awareness of industry best practices for data handling and security.
• Adhere to information security policies and report breaches as needed.

Skills

Machine learning experience

Python programming

Team player

Communication skills

Data pipelines

Prototyping

Education

Bachelor's or Master's in Computer Science or related

Tools

TensorFlow

Caffe

Torch

Docker

Node.js

Bash

Linux CLI

C++

Scala/Spark

SQL

Cassandra

Job description

Deploy is building an intelligence infrastructure platform to give developers and enterprises access to AI models. We’re hiring ML engineers to prototype, train, and deploy state-of-the-art neural networks at scale, handling terabyte-scale datasets and integrating with data labeling services and DevOps.

The role involves delivering production-ready models, collaborating across teams, and staying current with deep learning research to improve accuracy and speed.