Interested in this AI/ML Engineer role at Filevine?
Apply Now →About This Role
Filevine is a Legal AI company delivering Legal Operating Intelligence for the future of legal work. Grounded in a singular system of truth, Filevine brings together data, documents, workflows, and teams into one unified platform—where modern legal work happens with clarity and consistency.
Powered by LOIS, the Legal Operating Intelligence System, Filevine connects context across every matter to transform legal operations from reactive to proactive. LOIS reads, understands, and reasons across your data to surface insight, automate complexity, and give professionals the clarity and confidence to see more, know more, and do more. Fueled by a team of exceptional collaborators and innovators, Filevine’s rapid growth has earned AI awards and recognition from Deloitte and Inc. as one of the most innovative and fastest\-growing technology companies in the country. Role Summary:
Legal professionals spend millions of hours every year reviewing contracts, drafting documents, researching complex questions, and making high\-stakes decisions. We're building AI that fundamentally changes how that work gets done, combining deep legal reasoning with thoughtful product design to create experiences professionals trust every day.
We're looking for a Senior Product Designer to help define how professionals collaborate with AI. You'll transform complex legal workflows into simple, elegant experiences and invent interaction patterns that don't yet have established conventions. Many of the experiences you'll design simply don't exist yet.
We're a small, senior team that ships quickly, challenges each other's thinking, and cares deeply about craft. We move quickly, iterate constantly, and maintain an exceptionally high bar for the products we build.
This is an execution\-first role. If you enjoy solving difficult product problems, sweating the details, and shipping products that fundamentally change how people work, we'd love to talk.
### What You'll Do:
- Design end\-to\-end product experiences from early concepts through polished implementation.
- Turn ambiguous product problems into intuitive workflows, interactions, and interfaces.
- Design AI\-native experiences and define new interaction patterns for researching, drafting, reviewing, and negotiating with AI.
- Create high\-fidelity prototypes to explore ideas, validate concepts, and communicate product direction.
- Use AI throughout your design workflow to explore ideas, iterate rapidly, and accelerate execution.
- Collaborate closely with engineers and product managers throughout the product lifecycle to build thoughtful, high\-quality experiences.
- Contribute to and evolve our design system, interaction patterns, and overall product quality.
- Champion simplicity, clarity, and craftsmanship in every detail.
- Raise the design bar through thoughtful critique and exceptional attention to detail.
### What You'll Need:
- 5\+ years designing digital products with a portfolio demonstrating exceptional product thinking and shipped work.
- Exceptional product taste, interaction design skills, and visual craft.
- Experience designing AI products or AI\-powered features used in production.
- Deep understanding of how AI changes user behavior, workflows, and interface design.
- Uses AI fluently throughout the design process to explore more ideas, iterate faster, and improve execution.
- Ability to simplify complex workflows into intuitive user experiences.
- Ability to move comfortably between product strategy and meticulous execution.
- Strong sense of urgency and bias toward shipping. You make thoughtful decisions without waiting for perfect information.
- Relentlessly resourceful. You take ownership, figure things out, and drive work forward.
- Ability to work collaboratively with product managers and engineers throughout the full product lifecycle, from discovery and iteration through implementation, rather than simply handing off designs.
- Comfortable working in fast\-moving environments with high ownership and minimal process.
- Strong communication skills.
- Experience building and evolving design systems.
### Nice to Have
- Experience designing enterprise, legal, or other complex professional software.
- Experience conducting lightweight user research to validate designs.
- Experience with rapid prototyping tools beyond traditional design software.
*Compensation Information: $175,000\+*
*The base salary range represents the low and high end of the salary range for this position. The total compensation package for this position will be determined by each individual’s location, qualifications, education, work experience, skills and performance. We believe in the importance of pay equity \- the range listed is just one component of Filevine’s total compensation package for employees. This position is also eligible for a paid time off policy, as well as a comprehensive benefits package.*
*Filevine is an Equal Opportunity Employer. Qualifications for employment, promotion and other terms and conditions of employment are based upon the ability to perform the job. Equal\-employment opportunities are provided to all applicants and employees without regard to race, creed, religion, color, age, national origin, sex, disability, veteran status, or other legally protected class. Filevine is committed to providing reasonable accommodations for qualified individuals with disabilities. If you need assistance or accommodation due to disability, or if you have concerns related to Filevine’s equal employment opportunities, you may contact us at* *[email protected]*
Cool Company Benefits:
- A dynamic, rapidly growing company, focused on helping organizations thrive
- Medical, Dental, \& Vision Insurance (for full\-time employees)
- Competitive \& Fair Pay
- Maternity \& paternity leave (for full\-time employees)
- Short \& long\-term disability
- Opportunity to learn from a dedicated leadership team
- Top\-of\-the\-line company swag
Privacy Policy Notice
Filevine will handle your personal information according to what’s outlined in our Privacy Policy.
Communication about this opportunity, or any open role at Filevine, will *only* come from representatives with email addresses using "filevine.com". Other addresses reaching out are *not affiliated* with Filevine and should not be responded to.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Filevine, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Filevine AI Hiring
Filevine has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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