ML​/AI Engineer

Overview

Everlaw is looking for a Staff ML/AI Engineer to help build the intelligent systems that help legal professionals navigate and make sense of complex litigation cases at the scale of millions (or 100s of millions) of documents. In this role, you will bridge the gap between cutting-edge research and production-grade software, bringing new capabilities to market.

As a Staff-level individual contributor, you won’t just be fine-tuning models from the frontier labs; you’ll have a key role in designing the architecture, mentoring senior engineers, and defining how ML/AI are applied in our product, directly contributing to our company’s vision of being the AI leader in legal discovery and litigation technology. AI is central to Everlaw – not a bolt-on or afterthought – and represents a step-function advancement of our company’s mission of promoting justice by illuminating truth.

You’ll also collaborate with (and learn from) a community of other senior staff and principal engineers with industry-leading expertise in databases and storage technologies, search, cloud infrastructure, full stack SaaS application design, data science, and performance. You’ll have regular visibility to, and interactions with, our Chief Technology Officer and other senior leaders at the company.

At Everlaw, engineering is key to our mission. Our company culture is open and vibrant and we’re committed to the professional growth of our team members, offering an annual learning and development stipend and regular check-ins with managers regarding career goals. If you’re looking for a place that values passion, integrity, thinking big, and a desire to learn, we’d love to hear from you!

Think you’re missing some of the skills and are hesitant to apply? We do not believe in the ‘perfect’ candidate and encourage you to apply if you feel you can bring value to our team.

This is a full-time, exempt position located onsite (3 days/week in office) in Oakland, California.

• We want you to feel like part of the team early on! Our onboarding process will integrate you into the company with informative sessions on our product, policies, processes, and team structure and goals.
• We’re excited for you to learn, grow, and contribute right away! We trust that you’ll bring industry experience and knowledge that will uplift and uplevel the team, but we don’t expect you to know everything on Day 1.

• Apply core ML Fundamentals: Apply a deep understanding of probability, statistics, and optimization to ensure our models are not just "smart," but robust and explainable.
• Lead the design and implementation of our ML infrastructure and tooling. Champion MLOps best practices, ensuring that our experimentation-to-production lifecycle is efficient and reproducible.
• Help elevate the technical bar of the engineering team. We’re an organization devoted to constant learning. As a staff engineer, you’ll lead by example in staying abreast of industry and academic research developments and sharing your learnings with the team – whether in structured ways like feedback on design documents or code reviews, or in less formal ways, like giving occasional tech talks or leading a study group on ML research papers.
• Collaborate with other software engineers, security engineers, data scientists, product leads and designers, and other cross-functional teams in the pursuit of our goal of being the AI leader in litigation.
• Be on the lookout for new opportunities that ML and AI enable
  . Help us spot cases where industry or research progression in ML/AI tech will translate to Legal Tech, and influence our engineering and product direction. We’re prepared to act and innovate in bringing the best ideas to market.

• You have 8+ years of experience in software engineering, with at least 4 years dedicated to building and deploying ML models in a production SaaS environment.
• You have a solid grounding in ML Fundamentals: This includes command of the math behind the models (linear algebra, calculus, and loss function optimization).
• You emphasize practicality and product outcomes over hype: You’ll know when a simple random forest or Regressor…