Advanced Software Engineer

Description

The company is a legal data intelligence business that builds an AI-powered cloud platform, RelativityOne, which turns large volumes of complex information into insights for litigation, investigations, regulatory inquiries, and data breach responses.

Its customers include large law firms, corporations, and government agencies. The AI team works on algorithm quality and scalable AI solutions, and the AI and data science teams work with large datasets, modern data infrastructure, and machine learning.

This role sits between core engineering and data science. You will help build the platforms, pipelines, and practices that move research prototypes into reliable, scalable production systems, and you will work alongside senior and lead engineers.

The role is hybrid or remote.

Responsibilities

• Help design and build ML and AI platforms that are scalable, reliable, and secure, including standardized GenAI workflows.
• Work with data scientists, product managers, security teams, and data engineers on machine learning solutions with real impact.
• Build and improve CI/CD pipelines for ML models and data workflows, using containers, infrastructure as code, and orchestration tools.
• Build and extend automated processes for model training, deployment, and lifecycle management.
• Prototype and assess new MLOps tools that could improve efficiency, lower costs, or enable new product features.
• Deploy, monitor, tune, and debug production ML models.
• Define and track metrics for health, performance, reliability, and cost of AI systems.
• Take part in code and design reviews, and write implementation code yourself.
• Mentor junior engineers and share good practices across engineering and AI.
• Keep learning new tools and techniques and apply them to the AI platform.

Requirements

• At least 3 years of professional software engineering experience, including at least 1 year in ML/AI or big data environments.
• Proficiency in Python, Java, or C.
• Production experience with Docker.
• Experience deploying cloud solutions on AWS, Azure, or GCP.
• Experience with infrastructure-as-code tools such as Terraform or Pulumi.
• Familiarity with workflow orchestration platforms such as Prefect, Airflow, or similar.
• Understanding of Kubernetes and Helm fundamentals.
• Experience deploying, monitoring, and debugging machine learning models in production.
• Ability to collect and analyze metrics on model reliability and algorithm health.
• Strong collaboration and communication skills with cross-functional stakeholders.
• Nice to have: a bachelor's degree in computer science, engineering, mathematics, or a related field.
• Nice to have: a master's degree in a relevant discipline.
• Nice to have: experience with ML lifecycle platforms such as MLflow or Kubeflow.
• Nice to have: experience with model optimization techniques such as quantization, pruning, or compression.
• Nice to have: exposure to distributed data processing tools such as Spark, EMR, or Kafka.
• Nice to have: familiarity with deep learning frameworks such as TensorFlow or PyTorch.
• Nice to have: experience in secure and compliant data processing environments.

Conditions

The role is hybrid or remote.

Pay is a competitive base salary, an annual performance bonus, and long-term incentives, and the posting lists an expected range of $103,000 to $155,000.

The final offer depends on experience, skills, qualifications, and internal pay equity. The company does not typically offer the top of the range, so there is room for future salary growth.

The company aims for fair and equitable compensation.

Equal opportunity: the company values diverse perspectives.

Benefits

• Competitive salary, benefits, paid time off, parental leave, and an equity program.
• Work on distributed, cloud-native systems that process large volumes of data.
• Build with AI, cloud platforms, and scalable architectures in legal technology.
• Gain experience owning systems end to end across cloud and distributed environments.
• Work in a team that shares knowledge and focuses on continuous improvement.