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About Us
At Litify, we're revolutionizing the Legal industry by being the platform powering legal's top performers. As a trailblazer in legal technology, Litify delivers an all\-in\-one legal operating solution that empowers law firms and legal departments to achieve consistent success by continually standardizing, measuring, and improving their legal operations.
Our mission is clear: to deliver better business outcomes to our clients, so they can focus on delivering the best legal service and outcomes to their clients. 400\+ enterprise businesses and 55K\+ legal professionals trust Litify to amplify their impact with innovative technology and service that stands the test of time.
Backed by Bessemer Venture Partners, Litify is proud to be recognized as one of Inc. 5000 and Deloitte Technology Fast 500's fastest\-growing private companies in America along with numerous awards for our unparalleled software. With offices in the vibrant cities of New York and New Orleans, we're at the heart of legal innovation.
About the Role
Litify is transforming how legal teams operate through intelligent technology \- and AI is at the heart of that evolution. We're looking for a SR. CSM, AI who will own the success of Litify's AI products across our customer base. This role blends hands\-on technical curiosity with strategic relationship management \- helping law firms adopt, implement, and scale AI use cases that drive measurable impact.
This person will get into the weeds with our customers, understand how AI fits into their day\-to\-day operations and surface insights that shape the future of our AI roadmap.
#### In this role, you will:
- Own a portfolio of AI Clients, providing ongoing support, strategic guidance, and consulting to ensure the clients' success and satisfaction with the Litify platform.
- Drive AI Adoption \& Value by participating in onboarding and leading continual enablement of LitifyAI customers, ensuring users understand how to incorporate AI into their everyday workflows.
- Identify opportunities to expand AI usage within accounts through new use cases, success stories, and data\-driven recommendations.
- Develop and maintain playbooks, best practices, and success frameworks that accelerate adoption across firm types.
- Gather \& surface feedback by conducting structured interviews and check\-ins with firms to capture product feedback and emerging use cases.
- Partner with Product and Marketing to translate customer insights into roadmap inputs, enablement content, and customer stories.
- Track and communicate recurring themes from customer feedback to influence prioritization and strategy.
- Act as a Cross\-Functional Stakeholder working closely with Product, Engineering, Sales, Marketing, and LSAP to ensure customers receive a consistent and high\-impact AI experience.
- Represent the voice of the customer in AI\-related discussions from feature prioritization to enablement strategy.
- Participate in customer\-facing forums, advisory boards, and internal education initiatives to promote AI adoption.
You have:
- Strong technical aptitude and AI curiosity, with the ability to understand, explain, and apply AI concepts (e.g., generative AI, prompt\-based workflows, automation) in practical, business\-facing ways.
- Experience driving adoption of complex or emerging technology, ideally AI\-powered or data\-driven products, by translating features into clear value for end users.
- Proven ability to design solutions and best practices by gathering requirements, synthesizing feedback, and tailoring recommendations to different customer workflows and maturity levels.
- Consultative customer success experience, with a track record of building trusted relationships, driving measurable outcomes, and influencing renewals and expansion through value realization.
- 5 years of Customer Success experience in a SaaS environment, preferably supporting mid\-market or scaled customer segments.
- Comfort working cross\-functionally with Product, Engineering, Sales, and Marketing to influence roadmap decisions, enablement strategies, and go\-to\-market efforts.
- Strong analytical and storytelling skills, able to use data, usage insights, and customer anecdotes to articulate impact and inform decision\-making.
- Excellent written and verbal communication skills, including experience presenting complex or technical topics to both technical and non\-technical stakeholders.
- Experience enabling and training users, with an interest in educating customers on new technology and helping them evolve their workflows over time.
Disclosure:
This job is fully remote, with the exception of employees living in New York City and New Jersey. Our NYC/NJ employees adhere to our hybrid schedule, which requires working in our Manhattan office 6 days a month.
The estimated compensation range for this role is $100k \- 120k base salary. You will also be offered a bonus opportunity and benefits.
Our salary ranges are based on paying competitively for our size and industry, and are one part of many compensation, benefits and other reward opportunities we provide.
Individual pay rate decisions are based on a number of factors, including qualifications for the role, experience level, skill set, and balancing internal equity relative to peers at the company.
Salary Context
This $100K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Litify, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($110K) sits 49% below the category median. Disclosed range: $100K to $120K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Litify AI Hiring
Litify has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $120K - $120K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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