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About This Role
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 AI 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.
### Getting started
- 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.
### In your role, you'll...
- 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.
### About you
- 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 will do the job vs. a multi-billion parameter model and Transformers.
- You have a track record of solving "noisy data" problems and understanding precision/recall tradeoffs. You'll help teams design evaluation strategies to achieve their product goals.
- You have MLOps experience, making use of cloud-based infrastructure and technologies like Kubernetes, Docker, Weights & Biases, MLFlow, etc and major cloud providers like AWS, GCP, or Azure. You've designed systems with reliability and observability in mind.
- You are proficient in at least one language (like Python, Go, or Java) and experienced with frameworks such as PyTorch, TensorFlow, or JAX. You write production-grade code that is clean, tested, and scalable.
- You're a constant learner, staying abreast of the latest ML/AI developments from the industry, and how you might apply these to your work.
- You're able to collaborate effectively with coworkers on different teams. You can zoom in to discuss technical details with other experienced AI engineers, and can zoom out to explain technical concepts without jargon. You're willing to share your knowledge and help others learn.
### Pluses
- You've applied LLMs to production-grade software, including knowledge of fine-tuning, prompt engineering, and familiarity with the latest offerings from both the frontier labs and open source
- You've devised embedding strategies and approaches for knowledge representation, including vector and graph databases and applied RAG to find answers in very large custom data sets
- You've built agents, MCP servers or clients, or have designed evaluation strategies for monitoring and supervising AI agents
### Benefits
- The expected salary range for this role is between $228,000 - $288,000. The final offered salary will be dependent upon many factors including the candidate's experience and skills. The base pay range is subject to change in the future.
- Equity program
- 401(k) retirement plan with company matching
- Health, dental, and vision
- Flexible Spending Accounts for health and dependent care expenses
- Paid parental leave and approximately 10 days (80 hours) per year of sick leave
- Seventeen paid vacation days plus 11 federal holidays
- Membership to Modern Health to help employees prioritize mental health and wellness
- Annual allocation for Learning & Development opportunities and applicable professional membership dues
- Company-sponsored life and disability insurance
- Find out more about our Benefits and Perks
### Perks
- Work in Downtown Oakland, just steps from the BART line and dozens of restaurants
- You will get a powerful Linux laptop and be able to customize your desk setup
- Bond over team lunches and out-of-the-box events
- Ranked "#1 on G2 for Ediscovery Software and Momentum" and we offer free eDiscovery resources to benefit the greater societal good with Everlaw for Good
- Time off for company-sponsored volunteer events and 4 paid hours per quarter to volunteer at a charitable organization of your choice
- Take advantage of learning and career development opportunities
- Ranked #9 on Glassdoor's Best Places to Work 2023 for US small and medium companies
- One of Wealthfront's 2021 Career Launching Companies, and ranked #2 on the "2022 Bay Area Best Places to Work" list by the San Francisco Business Times and the Silicon Valley Business Journal
- One of Fast Company's World's Most Innovative Companies for 2022 and proud contributor of free ediscovery resources to benefit the greater good through "Everlaw for Good"
- #LI-EJ1
- #LI-Hybrid
Pursue Truth While Finding Yours
At Everlaw, we are deeply invested in pursuing the truth, for our clients and for our employees. We know that when you're empowered to pursue your passions, it is reflected in the work. That's why we're committed to the professional growth of all our team members, offering an annual learning and development stipend and regular career check-ins with managers. If you're looking for a place that values passion, integrity, and a desire to learn, we'd love to hear from you!
About Everlaw
We help law firms, government agencies, and corporations sift through millions of documents of evidence in big lawsuits and investigations to find the proverbial smoking gun (or needle in the haystack - pick your metaphor). It's a multi-billion dollar space typically dominated by service-oriented vendors, and we're coming at it with cutting-edge technology and elegant design. It's working, and we've been growing very rapidly: we host hundreds of terabytes of data and work with all 50 state Attorneys General and hundreds of law firms on some of the most high-profile cases litigated today.
Everlaw is an equal opportunity employer. We pride ourselves on having a diverse workforce and we do not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We respect the gender, gender identity and gender expression of our applicants and employees, and we honor requests for pronouns. It is our policy to comply with all applicable national, state and local laws pertaining to nondiscrimination and equal opportunity, including the California Equal Pay Act. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
We collect and process the personal information you provided along with your job application in accordance with our Applicants Privacy Notice and Notice at Collection.
When preparing to engage with Everlaw as a candidate, you may use AI tools for research, polishing application materials, and interview prep. However, any assessments (unless explicitly stated), remote interviews or live interviews must be completed independently without AI support. By submitting your application, you agree to adhere to these rules. Here's the link to our full policy, and please reach out with any questions!
We use Covey as part of our hiring and/or promotional processes. As part of the evaluation process, we provide Covey with job requirements and candidate-submitted applications. Certain features of the platform may qualify it as an Automated Employment Decision Tool (AEDT) under applicable regulations. For positions in New York City, our use of Covey complies with NYC Local Law 144. We began using Covey Scout for Inbound on the 9th of June, 2025.
Salary Context
This $228K-$288K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $170K across 217 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Everlaw, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $154,000 based on 8,743 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $225,000. This role's midpoint ($258K) sits 68% above the category median. Disclosed range: $228K to $288K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Everlaw AI Hiring
Everlaw has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Oakland, CA, US. Compensation range: $288K - $340K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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