Advanced Software Engineer

Description

The company is a legal data intelligence business that builds AI-powered cloud software for organizing information, finding what matters, and acting on it in litigation, investigations, regulatory inquiries, and data breach responses.

Its customers include large law firms, corporations, and government agencies, which use the platform to manage high-stakes matters.

The company has used AI for two years, and billions of documents have already been processed with it. The team tests ideas quickly, learns from results, and improves often.

The role is a senior-level software position focused on taking features from design to a live SaaS product, using AI both as a tool and as part of the product. The work covers production C and SQL, plus the services, monitoring, evaluation tools, and data pipelines that make applied science work reliable for customers.

The team owns the flagship AI product in the Case Strategy suite, which helps case teams analyze, organize, and understand case information and build case strategies. The team works with multiple databases, processing and web servers, search engines, and clustering technologies.

You will work with Applied Scientists, who own the science, and with Product, who owns the value case, so that shipped work meets its promised reliability target.

Responsibilities

• Design, build, and maintain the company's primary product using object-oriented design, SOLID principles, and design patterns across several languages.
• Improve application and system performance at scale.
• Fix defects and respond to client-specific problems when issues come up.
• Work in the team's Scrum process and deliver through CI/CD, with strong observability.
• Move prototypes and notebooks from applied science to deployed, monitored production services through CI/CD.
• Instrument models and prompts for quality, latency, and cost, and build dashboards and alerts against the reliability target.
• Find and fix common AI failure modes, including drift, latency regressions, cost spikes, tool-call and retrieval errors, and data quality problems that affect outputs.
• Build and maintain the evaluation harness and regression checks that gate releases, and widen coverage as use cases grow.
• Design AI solution components with senior review, and document interfaces and non-functional requirements using patterns proven elsewhere in the company.
• Build and run feature pipelines with reproducibility, lineage, and automated data quality monitoring.
• Document implementation trade-offs such as batching, caching, model tier, timeouts, and fallbacks, with clear reasoning about quality and cost.
• Work with the company's security team to protect customer data and meet compliance requirements in every AI feature.
• Use modern AI coding tools to speed up and improve design, development, testing, and code review, and share what works with the team.
• Set your own priorities across competing tasks and deliver on schedule.
• Mentor L3 engineers and collaborate with the team, Applied Science, product managers, and the AI platform team.
• Keep the team informed about AI developments, learning opportunities, and future direction.

Requirements

• 3+ years of professional software development experience with C and Microsoft SQL Server, or related object-oriented languages.
• Experience building web applications with JavaScript and ASP.NET.
• Experience building scalable solutions on Azure or another cloud platform.
• Experience on an agile software development team.
• Experience with CI/CD and a grasp of DevOps principles.
• 1+ year building or running AI/ML systems or their data pipelines, such as LLM integrations, prompt design, retrieval-augmented generation (RAG), or embeddings.
• Experience deploying and monitoring models or LLM-based services in a cloud environment.
• Practical ML knowledge: you can read evaluation results, reason about model failure modes, and weigh quality, latency, and cost trade-offs.
• Experience building data pipelines with orchestration tools such as Prefect or Airflow, with attention to reproducibility.
• Working knowledge of Python alongside C.
• Regular use of AI-assisted development tools, with good judgment about when to trust their output and when to check it.
• Ability to design and document a component of a larger system, including its non-functional requirements.
• Ability to work effectively with scientists, engineers, and product partners.
• Preferred: Experience building evaluation harnesses or regression suites for AI systems, including testing LLM output quality.
• Preferred: Experience with LLM orchestration or agent frameworks, agentic workflows, and tool calling.
• Preferred: Experience with large-scale or sensitive data.
• Preferred: Experience with Azure DevOps and Azure AI and data services.
• Preferred: Experience with Docker, Kubernetes, and Helm.
• Preferred: Experience with infrastructure as code, such as Terraform, Pulumi, AWS CDK, or CloudFormation.
• Preferred: Experience with MLflow or Kubeflow, or with big data frameworks such as Apache Spark or Kafka.
• Preferred: Experience building or deploying models with PyTorch, TensorFlow, or Keras, or depth in an area such as NLP.
• Preferred: A bachelor's or advanced degree in Computer Science, Computer Engineering, Mathematics, Statistics, or Artificial Intelligence.
• Skills listed for the role: engineering principles, hardware integration, innovation, problem solving, process improvement, quality assurance (QA), research and development, system design, technical documentation, and troubleshooting.

Conditions

The work model is hybrid or remote.

The expected base salary range is $103,000 to $155,000.

The final salary depends on experience, skill set, qualifications, and internal pay equity. Offers at the top of the range are not typical, so that there is room for future salary growth in the role.

The role includes a competitive base salary, an annual performance bonus, and long-term incentives.

The company is committed to fair and equitable pay.

Benefits

• Competitive salary, health and retirement programs, and wellness resources.
• Discretionary time off (DTO), parental leave for primary and secondary caregivers, and 2 company breaks each year.
• A home office stipend and an equity program.
• Volunteer opportunities and company-sponsored charitable programs.
• Exposure across the stack, from distributed systems to AI-enabled workflows, with continuous delivery and end-to-end ownership.