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About This Role
Harbor Labs is a new team within Harbor focused on solving hard AI implementation problems in the legal market. We have strong hypotheses about what works, but we're building this like a startup: learning as we go, adjusting direction based on what the market tells us, and staying flexible about how we operate.
We're looking for an AI Delivery Lead to join at the ground floor. This role sits at the critical intersection between our engineering team and our clients, translating ambiguous business problems into concrete AI solutions, then driving them across the finish line.
This is not a management role. You will be hands\-on: building AI\-powered applications and solutions, designing transformational workflows, running workshops, configuring systems, testing with real data, and working shoulder\-to\-shoulder with clients to make AI actually useful in their day\-to\-day work.
What Makes This Different:
Our edge is combining deep legal domain experience with product sensibility and strong engineering. We understand how legal teams actually work, what change management looks like in risk\-averse environments, and how to build software that people will use. We also know the common pitfalls of enterprise software in traditionally low\-innovation areas like law, and we're determined to avoid them.
We focus on hard, novel problems where standard platforms fall short. The work is ambiguous, the solutions aren't obvious, and that's the point.
#### What You'll Do:
- Own client delivery end\-to\-end. Lead engagements from problem definition through production deployment. You're accountable for outcomes, not just activity.
- Translate between business and engineering. Work with legal teams to understand what they actually need (not just what they say they want), then spec solutions our engineers can build.
- Design AI\-native workflows. Figure out where AI fits in existing processes, what needs to change, and how to make the human \+ AI collaboration actually work.
- Get your hands dirty with the product. Configure systems, curate and structure knowledge sources, write prompts, design test cases, and validate outputs against real\-world data.
- Drive integrations and architecture decisions. Work with engineers to figure out how AI fits into client tech stacks: APIs, data flows, security requirements.
- Run enablement and change. Train users and knowledge source owners on how to work with AI systems. Help people understand what AI can and can't do, and design around both.
- Help build Harbor Labs itself. This is a ground\-floor opportunity. You'll shape how we work, what we build, and where we go next.
#### What We're Looking For:
- You define problems, not just solve them. You don't build to spec. You challenge assumptions, ask hard questions, and carve a path through unstructured, complex problem spaces. You can develop a vision when there isn't one.
- You've shipped AI products or implementations. You understand how these systems actually work: RAG architectures, agentic workflows, prompt engineering, evaluation methods. You've seen what breaks in production.
- You're deeply fluent in AI, but not captured by hype. You follow what's happening in the space closely and understand current best practices. You also know how to separate signal from noise when every vendor is claiming to be the next breakthrough.
- You hold current model\-provider credentials. Anthropic Academy certifications (AI Fluency: Framework \& Foundations, Claude Code, Building with the Claude API, Model Context Protocol) are a strong plus, and current OpenAI Academy coursework counts too. This is a real signal for us, not a checkbox: it shows you're building with today's tools.
- You're technical\-adjacent, not afraid of code. You don't need to be writing Python all day, but you can read an API doc, understand a system architecture diagram, and have an intelligent conversation with engineers about tradeoffs.
- You can run a room. Workshops, executive presentations, working sessions with skeptical users. You're comfortable leading conversations at any level.
- You're relentlessly practical. You care about what actually works in production, not what sounds impressive in a deck.
- You're comfortable with ambiguity. Harbor Labs is new. The market is evolving. You'll need to figure things out without a playbook.
Nice to Have:
- Background in legal technology, legal operations, or enterprise software
- Experience at a product company (PM, solutions engineering, customer success)
- Hands\-on experience with a broad range of AI development tools and LLM APIs
- Experience in consulting or professional services environments
Why Harbor Labs:
We're a small team working on hard problems with clients who are serious about deploying AI in production. The team is senior, the work is hands\-on, and you'll have real ownership over what you deliver. This is early. You'll have meaningful input into what Harbor Labs becomes.
How to Apply:
Send us a note about why this role interests you and what you've built or shipped that you're proud of. Links, portfolios, or examples of your work are more valuable than a polished resume.
About Us:
Harbor is the preeminent provider of expert services across strategy, legal technology, operations, and intelligence. Our globally integrated team of 900\+ strategists, technologists, and specialists navigate alongside our clients – leading law firms, corporations, and their law departments – to provide essential resources and invaluable insights. Anchored in a rich heritage of deep knowledge, steadfast relationships, and mutual respect, our unwavering dedication lies in shaping the future of the legal industry and fostering enduring partnerships within our community and ecosystem.
*Harbor is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, sexual orientation, gender identity, marital status, civil union status, national origin, ancestry, age, parental status, disabled status, veteran status, or any other legally protected classification, in accordance with applicable law.*
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 Harbor, 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 $214,900 based on 6,420 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 $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.
Harbor AI Hiring
Harbor has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 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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