Lead ML Ops Engineer

7 days ago

Charleston, United States Relativity Full time

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 tosolve complexchallenges 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, and 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. The team is small but growing fast; you'll have a huge impact in shaping the culture, best practices, and vision of how machine learning and AI are utilized 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.

Job Description and Requirements

Responsibilities:

  • Lead a team of engineers from a technical perspective, focused on data science enablement, automation, and model management.

  • Design and build a CI/CD framework for releasing and maintaining models using the best available cloud and open-source technologies.

  • Design machine learning 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 that our data science platform protects our customers data.

  • Collaborate with data engineering to assure accuracy, integrity, and compliance of cleansed data to ensure model performance.

  • 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).

  • Explore datasets to identify opportunities for machine learning and business impact.

  • 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.

  • Optimize deployed models to tune for performance and cost optimization using techniques such as sparsity, compression, quantization, and pruning.

Minimum Qualifications:

  • Years of experience engineering software systems.

  • 3+ years of industry experience in machine learning-focused roles and big data environments.

  • 2+ years of leading a team from a technical perspective.

  • Fluent in Python, Java, or Scala.

  • 3+ years of experience with Docker.

  • 3+ years of experience creating resources on AWS, Azure, or GCP using infrastructure as code (e.g., AWS CloudFormation, AWS CDK, Terraform, CDKTF, Pulumi, etc.).

  • 1+ year of experience with Prefect, Airflow, or an analogous tool.

  • 1+ year of experience with Helm and Kubernetes.

  • Ability and desire to teach and 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.

  • Broad and conceptual knowledge of key data science approaches with an understanding of typical tradeoffs between approaches.

  • Experience maintaining and monitoring deployed data science solutions for performance and algorithmic health.

Preferred Qualifications:

  • Advanced degree in Computer science, Mathematics, Statistics, or Artificial Intelligence.

  • Experience with Azure cloud environment and Azure's big data infrastructure, data processing, and data science toolset.

  • Participation in open competition (Kaggle, Leetcode), and contributions.

  • 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 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.

  • Experience with ML Flow or Kubeflow.

  • Experience in a DevOps, infrastructure, or site reliability team or function.

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:

$150,000 and $224,000

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.

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