About this Advanced AI Engineer role at Relativity
Relativity · Onsite · Illinois
Posting Type
Hybrid/Remote
Job Overview
WHO WE ARERelativity is a leading legal data intelligence company building technology that helps users organize data, discover the truth, and act on it with confidence. Our AI-powered, cloud platform, RelativityOne, transforms massive volumes of complex information into actionable insights for litigation, investigations, regulatory inquiries, data breach responses, and other high-stakes legal work where accuracy and trust are crucial.
The world’s largest law firms, corporations, and government agencies rely on Relativity’s legal AI software to securely surface and manage the most relevant and impactful information in their matters. Beyond our commercial impact, we are committed to expanding access to technology and supporting pro bono legal work.
WHAT WE DO
Our AI team is focused on helping users discover the truth more quickly and act on data with confidence through AI-powered innovation. We are committed to algorithm excellence, building trusted and scalable AI solutions that improve user experiences, products, investigations, and operational efficiency.
Relativity’s AI and Data Science teams leverage large-scale datasets, modern data infrastructure, and advanced machine learning technologies to deliver insights at scale. Our engineers and data scientists collaborate to build secure, high-performing platforms that support experimentation, innovation, and continuous improvement across the AI lifecycle.
Job Description and Requirements
ABOUT THE ROLE
As an Advanced AI Engineer, you will bridge the gap between core engineering and data science teams, building and evolving the platforms, pipelines, and practices that transform research prototypes into reliable, scalable production solutions.
You will own critical components of the machine learning lifecycle, including automated training pipelines, secure deployments, monitoring, and operational excellence. Working closely with Senior and Lead Engineers, you will help drive continuous improvement across Relativity’s AI platform while contributing to technical strategy, implementation, and innovation.
WHAT YOU’LL DO
- Contribute to the design and implementation of ML/AI platforms with a focus on scalability, reliability, security, and standardized GenAI workflows.
- Partner with data scientists, product managers, security teams, and data engineers to deliver high-impact machine learning solutions.
- Implement and improve CI/CD pipelines for machine learning models and data workflows using containerization, infrastructure-as-code, and orchestration technologies.
- Build and enhance automated model training, deployment, and lifecycle management processes.
- Prototype and evaluate emerging MLOps technologies to improve efficiency, optimize costs, and enable new product capabilities.
- Deploy, monitor, tune, and troubleshoot production machine learning models.
- Establish and track health, performance, reliability, and cost optimization metrics for AI systems.
- Participate in code reviews and design reviews while contributing directly to implementation efforts.
- Mentor junior engineers and share best practices across the engineering and AI organizations.
- Continuously learn and apply new technologies, tools, and techniques to improve the AI platform.
WHAT WE’RE LOOKING FOR
Required
- 3+ years of professional software engineering experience, including at least 1 year working in ML/AI or big data environments.
- Proficiency in Python, Java, or C#.
- Production experience using Docker.
- Experience deploying cloud-based solutions on AWS, Azure, or GCP.
- Experience using infrastructure-as-code tools such as Terraform or Pulumi.
- Familiarity with workflow orchestration platforms such as Prefect, Airflow, or similar technologies.
- Understanding of Kubernetes and Helm fundamentals.
- Experience deploying, monitoring, and troubleshooting machine learning models in production environments.
- Ability to collect and analyze metrics related to model reliability and algorithm health.
- Strong collaboration and communication skills with cross-functional stakeholders.
Preferred
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
- Master’s degree in a relevant discipline.
- Experience with ML lifecycle platforms such as MLflow or Kubeflow.
- Experience with model optimization techniques including quantization, pruning, or compression.
- Exposure to distributed data processing technologies such as Spark, EMR, or Kafka.
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
- Experience working in secure and compliant data processing environments.
WHY WE COULD BE A GREAT FIT
Impactful Mission
- Build systems that help customers organize data, discover the truth, and act on it in high-stakes legal matters.
Engineering at Scale
- Work on distributed, cloud-native systems that process large volumes of data.
Cutting-Edge Technology
- Build with AI, cloud platforms, and scalable architectures shaping legal tech.
Growth and Ownership
- Gain experience owning systems end-to-end across cloud and distributed environments.
Collaborative Culture
- Work in a team focused on knowledge sharing and continuous improvement.
Inclusive Environment
- Diverse perspectives create stronger teams and better outcomes.
Compensation and Benefits
- Competitive salary, benefits, DTO, parental leave, and equity program.
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:
$103,000 and $155,000The 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.
Required Skills:
Engineering Principle, Hardware Integration, Innovation, Problem Solving, Process Improvements, Quality Assurance (QA), Research and Development, System Designs, Technical Documents, Troubleshooting
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