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About AI at Relativity
In the past two years, billions of documents have already benefited from the insights of Relativity AI – and we are just getting started on our journey to use AI to improve each user experience, product, matter, and investigation at Relativity. We are focused on helping our users discover the truth more quickly, and act on data.
About the Machine Learning (ML) Engineering Role
The Machine Learning Engineer is an MLOps-focused role that will work with the data scientists and product teams within the AI group to deploy machine learning solutions at scale. You will help build a platform to support our team and organization in deploying, validating, and monitoring machine learning models at a petabyte scale. You will report to the Manager of Software Engineering.
Your Role in Action
Work with our data scientists, product managers, and engineering teams to bring ML feature ideas to proof of concepts and feature planning for projects.
Manage and facilitate design decisions related to bringing machine learning algorithms to production using best practices in the industry.
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.
Contribute to our technology investments roadmap and help prioritize tech debt and architecture investments.
Help the team maintains operational excellence by ensuring every application/service owned by the team has adequate monitoring, logging, and metrics.
Help management plan the Key Results with great estimates and drive the team to accomplish those each quarter.
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.
Push the team to 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 staff and principal engineers to develop high availability, scalability, and reliability solutions.
Mentor talent within the AI group to promote career development.
Knowledge of data ingestion, cleansing, analyzing, and feature extraction operations.
Experience in using Machine Learning Libraries like TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas etc. when Python is used to implement ML solutions.
Experience training and deploying machine learning models.
Experience in performing testing of machine learning models and generating useful metrics for the engineering team as well as product teams.
Experience in deploying machine learning models from the local environment to production.
Experience in monitoring data and models through the complete ML lifecycle with end-to-end lineage.
Experience designing APIs, service-oriented architectures, and cloud-based distributed systems.
Fluent in programming languages suitable to implement machine learning solutions. Ex: Python or Scala.
Experience in working with single-cloud or multi-cloud environments to deploy, secure, and scale applications and services. Ex: Azure, AWS, GCP
Experience in Job Orchestration workflows and Job Running Frameworks in the cloud. Ex: Prefect, Apache Airflow, Spark, Databricks.
Relativity is a diverse workplace with different skills and life experiences—and we love and celebrate those differences. We believe that employees are happiest when they’re empowered to be their full, authentic selves, regardless how you identify.
Comprehensive health, dental, and vision plans
Parental leave for primary and secondary caregivers
Flexible work arrangements
Two, week-long company breaks per year
Unlimited time off
Long-term incentive program
Training investment program
Transparency in Coverage Information
The Transparency in Coverage Final Rule requires disclosure of the negotiated rates with in-network providers and the historic allowed amounts paid to out-of-network providers, for all health plans available to employers. Files containing this information for the plans covered are published on this page.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.