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About This Role
Location
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New York
Employment Type
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Full time
Location Type
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Hybrid
Department
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IT
Compensation
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- $133\.5K – $200\.3K • Offers Equity • Offers Bonus
*Additionally, this role is eligible to participate in our equity plan and benefits program. Benefits include, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits (401k match up to 4%), and flexible PTO.*
Why Harvey
==============
At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise\-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come.
This is a rare chance to help build a generational company at a true inflection point. With 1500\+ customers in 60\+ countries, strong product\-market fit, and world\-class investor support, we’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched.
Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you.
At Harvey, the future of professional services is being written today — and we’re just getting started.
Role Overview
=================
We're seeking a Sr. AI Enablement Engineer to advise on, build, integrate, and operate AI tooling for several departments within Harvey. You'll be the dedicated technical partner who turns AI capability into real workflows that teams use every day, owning the connector and MCP integrations that make those workflows possible, and work with cross\-functional teams to evaluate new technologies in this space that we are seeking to adopt.
This is a hands\-on technical IC role inside BizTech (Business Technology), partnering closely with teams across G\&A, GTM, and Engineering. You'll spend your time evaluating vendors, prototyping AI workflows for business processes, writing integration code, and shipping tools that internal teams can extend on their own. The ideal candidate is a senior IC engineer who is equally comfortable in a code review, a privacy lawyer's office hours, and a People Operations workflow whiteboard — and who sees making other Harvey employees more productive with AI as the actual job.
What You'll Do
==================
- Extend and govern AI workflows across the company. Harvey already runs AI agents in production, including automated IT support, with more functions coming online. You'll extend that into People, Legal, Finance, and Workplace: partner with the team that owns each high\-friction workflow, ship the AI\-powered version, and make sure it's governed and measured.
- Own technical governance of internal AI tools. Define the publishing process, scoping rules, and review cadence for the plugin and skill marketplace. Own the pre\-deployment security\-review path for new AI tools, and stand up spend and usage monitoring so cost and access stay visible as adoption scales.
- Own the MCP and connector roadmap for enterprise systems. Several MCP integrations are already piloted against core systems. You'll harden those into production and scale the pattern across HRIS, ERP, contract management, ticketing, and knowledge bases — defining the roadmap, building the integrations, and communicating what's available.
- Translate emerging AI capability into Harvey's internal roadmap. Track new MCP servers, agent frameworks, and new agentic features across the platforms in use, and make a clear, opinionated call on what to adopt, ignore, or wait on for relevant use cases.
- Run AI vendor security and privacy reviews as a structured workstream. AI\-adjacent vendor evaluations land on BizTech regularly and are handled ad hoc today. You'll build a documented intake, a reusable AI vendor risk framework, and a clear sign\-off path, partnering with Privacy, Security, and Legal.
- Build integration prototypes and reference architectures. Ship working examples that internal teams can extend on their own, so you're not the bottleneck for every new workflow.
- Be the technical partner of choice for G\&A teams. Sit next to Finance on a NetSuite workflow, next to People on a Workday\-flavored automation, next to Legal on a contract intake flow, and next to Privacy on a vendor review. Translate fluently in every direction.
What You Have
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- 5\+ years of software or integration engineering experience, with at least 2 years building integrations between SaaS systems (HRIS, ERP, contract management, internal platforms, communication tools).
- Hands\-on experience with API integration patterns, OAuth and identity, webhook architectures, and the kind of glue work that makes enterprise systems actually talk to each other reliably.
- Practical experience with LLM\-based applications and AI tooling — prompt design, agent workflows, retrieval, evaluation, or production integration of model APIs. You don't need to have trained a model; you do need to have shipped something real that depends on one.
- Working knowledge of the Model Context Protocol (MCP) or comparable agent\-tool integration patterns. If you haven't shipped MCP yet, you've at minimum read the spec and built something against it.
- Strong communication and stakeholder\-management instincts, especially with non\-technical partners — you can be the most technical person in a Privacy review and the most pragmatic person in an engineering one in the same afternoon, sitting between Finance, People, Legal, IT, and Privacy without losing context.
- Strong DevOps and operational fundamentals — CI/CD, infrastructure\-as\-code, secrets management, and observability. You treat the integrations and AI tools you ship as production systems: version\-controlled, monitored, and safe to change without breaking the teams that depend on them.
- Demonstrated experience applying data governance \& security best practices.
- Demonstrated comfort evaluating third\-party vendors — reading DPAs, reasoning about subprocessor chains, asking the right questions about data flows, and translating findings into clear go/no\-go recommendations for the business.
- Ability to thrive in a fast\-paced, high\-growth, and global environment with significant ambiguity.
Bonus Points:
- Background that includes both a customer\-facing role (Forward Deployed Engineer, Solutions Engineer, Applied AI Engineer) and an internal tooling role.
- Familiarity with enterprise iPaaS platforms.
- Experience inside a Business Technology, IT, or internal platform org at a high\-growth B2B SaaS company.
- Hands\-on work with enterprise platforms like NetSuite, Workday, Ironclad, Zendesk, Salesforce, etc, including their APIs, custom development, or AI\-adjacent extensions.
Compensation
================
$133,500 \- $200,300 USD
### Depending on your location, an Applicant Privacy Notice may apply to you. You can find all of our Applicant Privacy Notices \[here].
\#LI\-RB1
*Harvey is an equal opportunity employer and does not discriminate on the basis of race, 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 are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai
Salary Context
This $133K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At HARVEY, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($166K) sits 24% below the category median. Disclosed range: $133K to $200K.
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.
HARVEY AI Hiring
HARVEY has 3 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $340K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 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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