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: