Principal Machine Learning Engineer

Summary

We're looking for a Principal ML Engineer to be the technical authority behind our ML and GenAI work in the legal domain. This is a deeply technical, architecture-focused leadership role. You'll set direction across AI initiatives, but you won't manage people.

You're a good fit if you started your career as a software engineer (ideally in Java and/or Python), later moved into ML and Generative AI, and can be the strongest technical voice in the room.

What you'll do

• Serve as the principal technical authority for ML and GenAI initiatives
• Define and communicate the technical roadmap, architectural direction, and long-term AI strategy
• Evaluate legacy systems and platforms, identifying constraints, risks, and modernization paths
• Make high-impact technical decisions that guide engineering teams and shape platform evolution
• Lead and design POCs to validate new ML and GenAI approaches, tools, and architectures
• Assess feasibility, scalability, and integration needs before broader adoption
• Turn experimental results into clear architectural recommendations and next steps
• Build a deep understanding of existing backend systems, data flows, APIs, and legacy codebases
• Bridge historical system design and future-state AI/ML capabilities
• Partner with engineering teams so strategy matches implementation reality

Note: This role isn't focused on day-to-day coding, but it demands exceptional technical depth and sound judgment.

What you bring

• 10+ years in technology, with early-career experience as a software developer
• 5+ years in machine learning or AI
• Strong foundation in Java and/or Python, and the ability to read, assess, and reason about legacy code
• Experience with, or willingness to learn, Go and other newer languages
• Advanced experience in ML, AI, or GenAI, including architectural and system-level design
• Deep understanding of backend engineering, system design, and data-driven architectures
• Proven ability to make technical decisions at scale without owning daily coding tasks
• Demonstrated success as a technical leader without people-management responsibilities

Job Description

We're looking for a Principal ML Engineer to be the technical authority behind our ML and GenAI work in the legal domain. This is a deeply technical, architecture-focused leadership role. You'll set direction across AI initiatives, but you won't manage people.

You're a good fit if you started your career as a software engineer (ideally in Java and/or Python), later moved into ML and Generative AI, and can be the strongest technical voice in the room.

What you'll do

• Serve as the principal technical authority for ML and GenAI initiatives
• Define and communicate the technical roadmap, architectural direction, and long-term AI strategy
• Evaluate legacy systems and platforms, identifying constraints, risks, and modernization paths
• Make high-impact technical decisions that guide engineering teams and shape platform evolution
• Lead and design POCs to validate new ML and GenAI approaches, tools, and architectures
• Assess feasibility, scalability, and integration needs before broader adoption
• Turn experimental results into clear architectural recommendations and next steps
• Build a deep understanding of existing backend systems, data flows, APIs, and legacy codebases
• Bridge historical system design and future-state AI/ML capabilities
• Partner with engineering teams so strategy matches implementation reality

Note: This role isn't focused on day-to-day coding, but it demands exceptional technical depth and sound judgment.

What you bring

• 10+ years in technology, with early-career experience as a software developer
• 5+ years in machine learning or AI
• Strong foundation in Java and/or Python, and the ability to read, assess, and reason about legacy code
• Experience with, or willingness to learn, Go and other newer languages
• Advanced experience in ML, AI, or GenAI, including architectural and system-level design
• Deep understanding of backend engineering, system design, and data-driven architectures
• Proven ability to make technical decisions at scale without owning daily coding tasks
• Demonstrated success as a technical leader without people-management responsibilities