Machine Learning Engineer

Deploy•Seattle, WA

12d

About The Position

Deploy is an intelligence infrastructure platform that gives developers and enterprises access to open-source and proprietary AI models. It operates an optimized inference engine for leading open-source LLMs and offers proprietary models for content safety, AI and deepfake detection, sponsorship intelligence, and IP and publicity protection. The company has more than 250 employees across offices in San Francisco, Seattle, New York, and Delhi. Deploy is seeking machine learning engineers to prototype state-of-the-art neural network models and launch them into production. The role involves working with large datasets, learning technologies across the machine learning stack, moving quickly, and taking ownership. The ideal candidate has built a machine-learning-powered project from the ground up, contributes innovative ideas and implementations, and can plan scalable, maintainable data pipelines. The expected base salary is $120,000–$180,000. Compensation may vary based on qualifications, skills, competencies, experience, and location. Stock options may be offered in addition to base pay. Employees may participate in company-sponsored health, vision, dental, and gym membership benefits, and receive paid vacation according to company policy.

Requirements

Undergraduate or graduate degree in computer science or a similar technical field, with significant coursework in mathematics or statistics.
1–2 years of industry machine learning experience.
Successfully trained and deployed a deep learning model for image, NLP, video, or audio into production, with measurably improved performance over baseline, either in industry or as a personal project.
Strong experience with a high-level machine learning framework such as TensorFlow, Caffe, or Torch, and familiarity with the others.
Strong Python skills, particularly as applied to machine learning frameworks.
Ability to quickly code and prototype data pipelines using combinations of Python, Node, bash, and Linux command-line tools, especially for large datasets containing millions of files.
Working knowledge of C++, Scala/Spark, SQL, Cassandra, and Docker, or willingness to learn them quickly.
Up-to-date understanding of deep neural network research and architectures, including the theory and motivations behind techniques and how to implement them in a chosen machine learning framework.
Strong communication skills and ability to work with others.
Strong team-player approach and a do-whatever-it-takes attitude.

Responsibilities

Design and code neural networks; gather and refine data; train and tune models; deploy models at scale with high throughput and uptime; and analyze results in production to continuously improve model accuracy and speed.
Work closely with Backend and DevOps teams and internal data-labeling services.
Use OWASP Top 10 techniques to secure code against vulnerabilities.
Maintain awareness of industry best practices for data maintenance and handling relevant to the role.
Follow policies, guidelines, and procedures for protecting information assets.
Report actual or suspected security or policy violations or breaches to an appropriate authority.