Interested in this AI/ML Engineer role at Patsnap?
Apply Now →About This Role
AI Solutions Lead \- IP Legal / Patent Drafting (US Remote, East Coast)
➡️ Spent years drafting patents and wondering where that experience could take you next?
➡️ Interested in applying your patent expertise beyond practice and helping shape products used by IP professionals worldwide?
➡️ Looking for a role where you'll work closely with customers, influence product development, and help shape the future of patent drafting?
If this sounds like your next move, we’d love to hear from you.
Role Summary:
We have an exciting new role for an AI Solutions Lead \- IP Legal / Patent Drafting to help shape and scale PatSnap’s AI\-powered patent drafting solution.
We're looking for someone with hands\-on patent drafting experience who is interested in applying that expertise in a broader capacity. You may be a Patent Attorney, Patent Agent, or trainee attorney who enjoys the profession but is curious about where technology is taking it and wants to play a more active role in shaping the tools used by IP professionals.
In this role, you'll become one of the key people helping shape PatSnap's patent drafting solution. You'll work closely with customers to understand their workflows, demonstrate the product, gather feedback, and help ensure we're solving genuine problems for patent professionals.
You'll also play an important role in supporting strategic customer opportunities. Working alongside Sales and Customer Success teams, you'll participate in customer meetings, workshops, demonstrations, proof\-of\-concepts, and evaluation projects. Your credibility as someone who has done the job yourself will help customers understand how the product can support their drafting workflows and where it can deliver value.
You'll partner with Product Managers and Engineers, bringing practical patent drafting experience into the product development process. Whether it's identifying workflow improvements, validating new capabilities, or helping prioritise future enhancements, you'll play an important role in how the product evolves.
The ideal candidate combines hands\-on patent drafting expertise with strong communication skills, commercial awareness, and a genuine interest in technology. They enjoy engaging with customers, are comfortable presenting solutions, and want to play a meaningful role in both product development and customer adoption.
This role is well suited to someone who wants to apply their patent expertise in a broader capacity while helping shape the future of patent drafting technology.
Who are we?
PatSnap is a global, pre\-IPO company helping organisations make better decisions across Intellectual Property and Research \& Development. Our platform supports IP, legal, innovation and R\&D teams throughout the innovation lifecycle, using domain\-specific AI to help them work faster and make more confident decisions.
With over 12,000 customers worldwide, including some of the biggest names in innovation, PatSnap is at the forefront of AI\-powered IP and innovation intelligence. Our $300M Series E funding round brought our valuation to $1 billion unicorn status, and we still have a remarkable amount of growth ahead.
We have a vibrant and diverse team with offices in Singapore, Toronto, London and Shanghai, alongside remote teams in the US. Our growth is powered by our people, and we are proud of our shared ambition, work ethic and entrepreneurial spirit.
### What You'll be Doing:
Customer Engagement \& Solution Leadership
- Act as a trusted advisor to patent and IP professionals, helping customers understand how AI can improve patent drafting, search, prosecution, portfolio management, and innovation workflows.
- Support strategic customer engagements, workshops, product demonstrations, proof\-of\-concepts, and enterprise opportunities alongside Sales and Customer Success teams.
- Translate customer challenges and market needs into product enhancements and scalable solutions.
Product Strategy \& Workflow Innovation
- Work closely with Product and Engineering teams to shape the future of AI\-powered patent and IP workflows.
- Bring real\-world practitioner expertise to influence product direction, prioritisation, and workflow design.
- Help define and validate new capabilities through customer feedback, testing, and pilot programmes.
AI \& Industry Expertise
- Act as the subject matter expert for patent drafting, search, prosecution, and innovation intelligence workflows.
- Support the development of AI\-powered solutions across patent intelligence, semantic search, knowledge discovery, and workflow automation.
- Stay at the forefront of developments in patent AI, LegalTech, and innovation intelligence.
Cross\-Functional Collaboration
- Partner across Product, Engineering, Sales, Customer Success, and customer teams to drive successful product adoption and growth.
- Develop enablement materials, demo narratives, workflow playbooks, and best practices.
- Serve as the bridge between customers, product teams, and commercial teams to ensure we're building solutions that solve real\-world problems.
### What We'd Love From You:
- Hands\-on experience drafting patent applications, ideally as a European Patent Attorney, trainee Patent Attorney, Patent Agent, or similar IP professional.
- Strong understanding of patent drafting, prosecution, and wider IP workflows.
- A genuine interest in technology and curiosity about how AI can improve the way patent professionals work.
- Enjoys engaging with customers and is comfortable demonstrating solutions, not just someone who wants to move into product.
- Excellent communication skills and the ability to build credibility with patent attorneys, IP teams, and innovation leaders.
- Comfortable delivering product demonstrations, leading customer discussions, and translating complex workflows into practical solutions.
- A commercial mindset with the ability to understand customer challenges and identify opportunities to create value.
- Experience working cross\-functionally with Product, Engineering, Sales, and Customer Success teams.
- An interest in helping shape products, not just using them.
### Nice to Have
- Experience working with SaaS, LegalTech, IPTech, or AI\-powered software products.
- Exposure to product management, product ownership, or workflow design.
- Familiarity with Generative AI, LLMs, workflow automation, or emerging AI technologies.
- Experience supporting enterprise customers, software implementations, or complex B2B workflows.
*Patsnap is proud to be an equal opportunity employer (EOE) that champions diversity. We do not discriminate based on race, religion, national origin, citizenship, sex, gender identity or expression, sexual orientation, pregnancy, age, or marital, veteran/military, or disability status, or any other protected status in accordance with federal, provincial/state or local laws.*
*Even if you don’t meet 100% of the above qualifications, we encourage you to apply and tell us why you’d be a great fit for this role! If you require any accommodations during the interview process, please email us at* *\[email protected]* *so we can best support you.*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Patsnap, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Patsnap AI Hiring
Patsnap has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, CA, US. Compensation range: $260K - $260K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.