Interested in this AI/ML Engineer role at Galvin Patent Law LLC?
Apply Now →About This Role
*The Work*
We prosecute patents at the frontier: machine learning and AI systems, cryptography and data compression, satellite and communications technology, and other advanced software\-driven fields. Our clients are building things that do not have established playbooks — which means our practitioners cannot just document inventions. They have to understand them deeply enough to contribute meaningfully to strategy.
Our clients are established technology businesses with substantial, actively growing patent portfolios — companies that treat patent IP as a strategic asset, not a checkbox. We have almost no one\-off individual inventor clients. That means your work here is sustained and portfolio\-level: long\-term client relationships, sophisticated technical teams, and strategy that compounds over years rather than one\-and\-done filings.
In this role, you will work in partnership with the firm’s founder and our clients and their teams to identify patentable innovation they may not have recognized yet, develop portfolio strategy alongside the technology roadmap, and prepare and prosecute applications at the USPTO. If you enjoy helping inventors recognize protectable innovation they had not yet identified, you will likely thrive here.
*About the Firm*
We are a nineteen\-year\-old boutique patent practice, virtual from day one — and we have grown every single year of those nineteen years. That growth has been 100% organic: no advertising, no purchased leads — just repeat clients, referrals, and word\-of\-mouth recommendations. Our clients bring us with them as they leave companies, build and sell companies, and start new ones. That is what happens when clients see their patent counsel as a partner in innovation rather than a vendor — and it is why the work here is steady and the pipeline keeps compounding.
Seven practitioners trained here now practice patent law independently or in close association with the firm. Developing skilled, autonomous patent professionals is not a side effect of how we work; it is the model. You will have direct, frequent access to the firm’s founder for training, strategy, and mentorship, with a clear path toward increasingly independent practice.
*Who We’re Looking For*
- Minimum 5 years of hands\-on patent preparation and prosecution experience at the USPTO
- Active USPTO Registration \# required; patent agents are welcome. Attorney candidates should have a J.D. and active state bar membership in at least one state.
- Degree in physics, computer science, computer engineering, machine learning, AI, electrical engineering, or a similar science or engineering field
- Strong working fluency in software, machine learning/AI, and emerging technologies \- you can hold your own in a technical conversation with an ML engineer
- Exceptional technical writing and analytical skills
- An inventor’s mindset: curiosity about how things work and an instinct for where the innovation actually lives
*The Arrangement*
This is an ongoing independent contractor engagement expected to support a full\-time workload, with flexibility in how and when the work is performed. The engagement will support the firm’s existing client base and may be structured as a monthly retainer, overflow work, or a combination, with a guaranteed monthly minimum so your income floor is predictable from day one. Target annualized starting compensation is $150k/year, commensurate with experience.
This is not a salaried position with benefits. In exchange, you get:
- Genuine autonomy. No billable\-hour quotas, no commute, and a work structure designed for autonomy rather than bureaucracy. Set your own schedule; we ask only that you remain responsive to clients and available for collaboration during core business hours as needed.
- Fully remote, permanently. Not a pandemic accommodation — this practice has been virtual for nineteen years.
- Real support. Training, administrative support, docketing, and frequent direct contact with the founder and other legal team members for questions, teaching, and strategy.
- A long\-term professional path. Our track record of developing independent practitioners speaks for itself.
*How to Apply*
Send your resume along with a short note — a paragraph is fine — describing a patent, application, or invention you are genuinely proud of, and why. Each application will be reviewed carefully.
Pay: From $150,000\.00 per year
Work Location: Remote
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 Galvin Patent Law LLC, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
Galvin Patent Law LLC AI Hiring
Galvin Patent Law LLC 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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