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Job Overview

Relativity (Chicago, IL) seeks a Senior Machine Learning Engineer to work with data scientists and product teams within the AI group to deploy machine learning solutions at scale and help build a platform to support our team and organization in deploying, validating, and monitoring machine learning models at a petabyte scale. Specific duties include: contribute throughout all service lifecycle phases from conception through development, deployment, and operation; contribute to and mature existing, automated CICD workflows; participate in code reviews for projects written by your team; work with our data scientists, product managers, and engineering teams to bring ML feature ideas to proof of concepts and feature planning for projects; prove out the use of new technology with compelling proof of concepts and demonstration; work on solutions to improve our model training pipelines, model governance, and model monitoring; interact with our Customer Support Teams regularly and reduce the operational burden to our customers and keep the team focused on feature delivery during most of their work time; escalate risks and issues to management proactively so better planning for mitigation and resolution of those can be done; follow the industry’s best engineering practices and use the latest tech stack to implement and deliver code artifacts; help product teams and clients by releasing applications and services with elite dev-ops metrics and creating a smooth customer experience during each release; demonstrate work completed frequently to product, management, and leadership so feedback can be collected early and planning can be improved continuously; collaborate with Lead, staff, and principal engineers to develop high availability, scalability, and reliability solutions; mentor interns within the AI group to promote career development. Must take and pass pre-hire coding test related to problem solving, data structures, and algorithms. Option to work remotely from anywhere in the U.S. is available.

Job Description and Requirements

Position requires a Bachelor’s degree, or foreign equivalent, in Computer Science, Electrical Engineering, or a closely related field of study, plus 7 years of experience in the job offered, or as a Member of Technical Staff III, or similar Machine Learning Engineer position. In the alternative, will accept a Master’s degree, or foreign equivalent, in Computer Science, Electrical Engineering, or a closely related field of study, plus 4 years of experience in the job offered, or as a Member of Technical Staff III, or similar Machine Learning Engineer position. Specific experience must include: applying and delivering effective machine learning systems that make skillful predictions; applying deep learning techniques; defining the supervised learning problem and gathering relevant data; preparing data, including feature selection, imputing missing values, scaling, and other transforms; evaluating a suite of models and model configurations using an objective test harness; selecting and preparing final models and using it to make predictions on new data; and performing error analysis of the results to understand where the model is performing well and where it is lacking. Must take and pass pre-hire coding test related to problem solving, data structures, and algorithms. Option to work remotely from anywhere in the U.S. is available. Salary: $164,000/yr. Full time position. Apply by submitting your resumes to Recruiting@relativity.com, to be considered, reference Job ID: 24-9004 in the subject line.

Relativity is committed to competitive, fair, and equitable compensation practices.

This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between following values:

$126,000 and $190,000

The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.