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About This Role
Harbor Labs is building the next generation of AI\-enabled legal services. We partner with leading law firms and corporate legal departments to turn emerging AI capabilities into practical, scalable ways of working often before a proven playbook exists. This role sits at the center of that work: leading complex programs, guiding senior legal stakeholders, and converting what we learn into repeatable approaches that can scale across clients and Harbor.
Build what comes next. Join a team working at the edge of legal AI where the problems are real, the answers are still emerging, and your judgment can directly shape how leading legal organizations adopt and scale new technology.
Why this role exists:
As our embedded AI engagements expand, clients increasingly need more than strong project execution. They need a leader who understands the realities of legal AI delivery, can shape the conversation with lawyers and legal\-operations teams, and can turn technical and adoption signals into sound program decisions.
The Legal AI Program Manager will create the structure that allows multidisciplinary teams to move quickly without losing rigor. You will bring clarity to ambiguity, challenge assumptions constructively, and help clients make confident decisions about where AI can create meaningful value and where it cannot.
What makes this different:
- This is not a traditional PMO role and it is not a schedule\-management position. You will be expected to understand the work deeply enough to influence it.
- You will operate at the intersection of AI, legal work, client delivery, and organizational change—partnering with AI delivery leaders and technical specialists while serving as a trusted advisor to senior client stakeholders.
- Because Harbor Labs is still evolving, you will have a meaningful voice in how we work, what we build, and how this capability scales. The frameworks and operating practices you create will shape future engagements, offerings, and teams.
#### What you'll do:
- Lead complex legal AI programs from strategy through adoption. Create the structure, priorities, decision forums, and momentum required to move enterprise AI initiatives forward across one or more embedded client engagements.
- Guide senior legal stakeholders with credibility. Advise General Counsel, legal\-operations leaders, and lawyers on use cases, sequencing, governance, adoption, and what strong AI programs look like in practice.
- Orchestrate multidisciplinary teams. Connect client leaders with Harbor specialists across AI delivery, legal operations, workflow design, change, and technology so that decisions translate into coordinated action.
- Turn delivery signals into program decisions. Translate what is happening in configuration, prompting, evaluation, workflow design, risk, and adoption into clear recommendations about scope, sequencing, staffing, and investment.
- Create momentum without creating bureaucracy. Establish an operating cadence that gives teams clarity and leaders visibility while preserving speed, experimentation, and ownership.
- Challenge constructively. Push back on weak use cases, unrealistic timelines, scope drift, or decisions that will not create sustainable value while strengthening trust and maintaining executive alignment.
- Build reusable Harbor Labs approaches. Develop practical frameworks, governance models, reporting methods, and implementation patterns that can be reused and improved across engagements.
- Help shape the future of the capability. Bring market and client insight back into Harbor Labs, identify emerging needs, and influence how our delivery model, offerings, and team evolve.
#### What we're looking for:
- Legal AI delivery experience. You have led or played a central role in legal AI implementations—not only general technology or software projects. You understand what it takes to move from use\-case selection through workflow design, evaluation, rollout, and adoption.
- Credibility with lawyers and executives. You can engage senior legal stakeholders as a peer, explain complex issues clearly, and hold a well\-informed point of view even when it challenges the initial direction.
- A builder mindset. You enjoy creating structure where none exists, testing new approaches, learning quickly, and improving the playbook rather than simply following one.
- Strong program judgment. You know how to identify the decisions that matter, surface risk early, sequence work intelligently, and keep teams focused on outcomes rather than activity.
- Current AI fluency. You actively follow developments across leading model providers, agentic workflows, retrieval\-augmented generation, evaluation methods, and enterprise AI patterns. You separate durable capability from hype.
- Hands\-on curiosity. You experiment with current AI tools and understand how they behave in practice. Relevant training or credentials from Anthropic, OpenAI, Google, Microsoft, or other leading platforms are valued, but demonstrated learning and applied experience matter more than certificates.
- Comfort with ambiguity and change. You are energized by an environment where roles, methods, and opportunities will evolve as the market develops and the team learns.
- Collaborative confidence. You work well with technical experts, consultants, operators, and client teams; you can lead without relying on hierarchy and make others better through clear thinking and strong partnership.
Helpful experience:
- Experience in a law firm, corporate legal department, legal\-operations team, legal technology company, or professional services environment
- Experience leading embedded, managed\-services, or recurring delivery models rather than only fixed\-scope projects
- Experience establishing governance, reporting, or program infrastructure for a new or rapidly evolving capability
- Exposure to product thinking, service design, change management, or enterprise technology adoption
- Experience working across global teams and complex stakeholder environments
What success looks like:
- Clients view you as a trusted advisor who improves the quality and pace of their AI decisions.
- Engagement teams have clear priorities, fast escalation paths, and the right level of operating discipline.
- AI initiatives move beyond pilots toward practical adoption and measurable business outcomes.
- Harbor Labs has stronger, more repeatable methods because of the frameworks and insight you contribute.
- You have helped define how this capability and your own role within it scales over time.
Who thrives here:
- People who prefer solving ambiguous, consequential problems over maintaining established processes
- Leaders who combine strategic thinking with hands\-on execution
- Curious practitioners who learn emerging technology by using it
- Low\-ego collaborators who challenge ideas, not people
- Individuals who want meaningful ownership and the opportunity to influence how a new capability grows
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. Mid-level AI roles across all categories have a median of $194,400.
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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