Senior ML Ops Engineer

Posting Type

Hybrid

Job Overview

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 with confidence.

• We are focused on algorithm excellence, to provide the most robust and trusted experience possible.
• We are creating a world class toolset to solve complex challenges quickly and iteratively.
• AI will be leveraged everywhere, in all stages of the discovery process to better manage cases and to optimize product operations.

As a team, we believe in exploration, experimentation, and bringing your curiosity to work every day. We know that you can’t innovate without experimentation — and a little failure happens on the path to invention. We use the latest and greatest to ensure we are the best. We strive to experiment, ship, and learn every day.

About Data Science at Relativity
Relativity’s scale and breadth create tremendous variety for rich data exploration and insights; our market position and scaled products mean our models and insights can quickly be in the hands of our users. Great insights can’t happen without great data, and the best insights come from massive data. Our data infrastructure and engineering ensure that the breadth of Relativity data is available for insights, confidential data is kept confidential, data is always protected, and we are investing heavily in data pipeline and data lake technology moving forward. If you’re looking for a data rich environment that is already heavily using AI, with at-scale challenge and a ton of innovation and experimentation ahead, you will find yourself at home on the AI team within Relativity. Join a fastgrowing AI team where you'll help shape the culture, best practices, and technical vision for machine learning at Relativity. You’ll have the freedom to experiment with and participate in deciding which big data, deep learning and NLP tools are right for Relativity on an ongoing basis. We seek collaborative builders who want to move fast and love a challenge.

About the Senior ML Ops Engineer Role
As a Senior ML Ops Engineer at Relativity, you will design and build the AI/ML platforms and processes that support applied science and product engineering teams. You will focus on standardizing GenAI workflows and model management, ensuring scalability, security, and reliability. This role requires close collaboration across teams to enable impactful machine learning solutions that improve user experiences and product performance. If you're eager to tackle technical challenges and drive innovation in ML Ops, this position is for you.

Job Description and Requirements

Responsibilities:

• Work with a team of engineers from a technical perspective, focused on data science enablement, automation, and model management.
• Design AI/ML solutions with the appropriate delivery timelines, extensibility, performance, and scale.
• Plan larger data science and machine learning efforts in conjunction with data scientists and product managers, minimizing risks and maximizing opportunities.
• Collaborate with Relativity's security team to ensure robust data protection and compliance.
• Collaborate with product managers, data engineers, data scientists focused on innovation and new product development.
• Design, communicate, and deploy our machine learning operations processes and platforms (i.e., ML Ops).
• Prototype new machine learning technologies to find opportunities to reduce costs, gain efficiencies, unlock insights, or facilitate new product development.
• Contribute towards project work and model technical acumen via hands-on contributions, coaching, code review, and system design review.
• Communicate across the broader AI team, keeping the team aware of AI platform innovation, learning opportunities, and future areas of innovation. Deploy and monitor highly available data science solutions via CI/CD with health and performance metrics.

Minimum Qualifications:

• 4+ years of experience engineering software systems.
• 2+ years of industry experience in machine learning-focused roles and big data environments.
• Fluent in C#, Python, or Java. • 3+ years of experience with Docker.
• 2+ years of experience creating resources on AWS, Azure, or GCP using infrastructure as code (e.g., AWS CloudFormation, AWS CDK, Terraform, CDKTF, Pulumi, etc.).
• Experience with Prefect, Airflow, or an analogous workflow tool.
• Experience with Helm and Kubernetes.
• Ability and desire to mentor teammates in the use of applied data science and data processing technologies.
• Prior experience owning and maintaining a major system within a business from a technical and architecture perspective.
• Experience maintaining and monitoring deployed data science solutions for performance and algorithmic health.

Preferred Qualifications:

• Advanced degree in Computer Science, Computer Engineering, Mathematics, Statistics, or Artificial Intelligence.
• Experience with Azure cloud environment and Azure’s big data infrastructure, data processing, and data science toolset.
• Experience building or deploying models using frameworks such as Tensorflow, Keras, or PyTorch.
• Demonstrated deep experience in a particular area of data science (computer vision, NLP, etc).
• Experience with MLFlow or Kubeflow.
• Experience in a DevOps, infrastructure, or site reliability team or function.
• Experience deploying solutions in big data processing frameworks such as Apache Spark, Hadoop, EMR, and Kafka.
• Experience tuning data processing engines such as Spark to optimize costs, resource consumption, and execution times.

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.