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About BOI
BOI is an AI Transformation Studio that helps ambitious companies redesign their growth engines for the autonomous age. We bring together unusually strong people in frontier enterprise AI, then give them the ownership to make it happen.
AI is rapidly reshaping how companies grow: how they innovate, acquire customers, personalize experiences, and scale commercial performance. Yet most organizations struggle to move beyond pilots and point solutions toward AI-enabled growth systems that actually work at enterprise scale.
About The Role As an Applied AI Lead, you will lead the day-to-day delivery of complex AI engagements, working across strategy, product, technology, and transformation to turn ambitious ideas into solutions that create real business value.
Working closely with our Directors and Managing Director of Applied AI, you'll help clients identify where AI can make a meaningful difference, shape what to build, and work with cross-functional teams to get it into the hands of real users. You'll move between business cases and roadmaps, product discovery and prototyping, delivery and adoption depending on what the problem requires.
Applied AI is your major, but you're a generalist at heart. You can zoom out to understand the broader business and transformation challenge, then zoom in to help turn an opportunity into something tangible. You understand the distance between a compelling AI demo and a solution that works in the real world.
You'll also bring credibility through practice. You use AI every day and have built your own workflows, tools, automations, or prototypes. We believe the best people to help companies apply AI are those who live it themselves.
As you grow in the role, you'll take increasing ownership of client relationships, proposals, and commercial opportunities, with coaching and support from Directors.
What You Will DoConnect Applied AI to Broader Transformation* Connect what you're building to the bigger picture: the business case, roadmap, capabilities, operating changes, and adoption required to create lasting value.
- Help clients understand what needs to change around the technology for AI solutions to succeed at scale.
- Translate lessons from individual products and use cases into broader implications for how the organization operates.
- Know when the answer isn't another AI product, and bring broader strategy and transformation thinking when that's what the problem requires.
Turn AI Opportunities Into Working Solutions* Translate business priorities and user problems into AI opportunities worth pursuing.
- Lead product discovery with client teams and end users to understand workflows, define problems, and determine where AI can create meaningful value.
- Shape AI products and solutions from early concept through prototype, build, deployment, and adoption.
- Define what to build first, what success looks like, and what to cut, keeping teams focused on the optimal path to value.
- Lead AI engineers, designers, and client teams to translate between business needs, user needs, and technical feasibility.
- Prototype and test ideas using modern AI tools, agents, workflows, and automations.
- Keep the work grounded in real users and measurable outcomes, not technology for technology's sake.
Lead Complex Client Delivery* Own the day-to-day delivery of complex AI engagements, maintaining a high bar across multiple workstreams.
- Structure work, set priorities, and keep cross-functional teams moving.
- Navigate ambiguity with confidence, proactively solving problems and making pragmatic decisions without waiting for direction.
- Build trusted relationships with senior client stakeholders by leading workshops, challenging assumptions, and bringing structure to complex problems.
- Stay hands-on in the work while keeping sight of the broader objective and what the client needs to do next.
Requirements* 4+ years of experience in consulting, agency, venture building, or product.
- Experience taking AI products, solutions, or use cases from problem definition through prototype, build, deployment, or adoption.
- Demonstrated hands-on use of modern AI tools in your own work. You've built workflows, automations, copilots, agents, or other practical AI solutions- not just advised clients on them.
- Experience leading significant workstreams or owning delivery for strategy or transformation engagements.
- Experience with the broader transformation required to make AI stick — whether through business cases, roadmaps, operating models, capability building, adoption, or change.
- Experience contributing to proposal development, engagement scoping, or growing existing client accounts.
- Strong ability to translate between business ambition and technical feasibility
Comfort operating in complex stakeholder environments with senior leaders *
What We Screen For We are deliberately not-for-everyone. We look for a specific combination of qualities:
- Low ego- You're confident, but grounded. You own mistakes, invite critique, share credit, and make the team better.
- High aptitude- You learn fast. You think clearly. You simplify complexity. You don't just execute- you understand. You've worked on complex strategy or transformation engagements and know the difference between what looks good in a deck and what actually works in practice.
- High ownership- You don't wait to be told. You move things forward, take responsibility beyond your role, and stay resourceful in ambiguity.
- Driven - You focus on outcomes, not activity.
- Commercial sharpness - You recognize the problems worth solving, understand what creates value for clients, and spot opportunities to turn great work into broader relationships.
- Passionate about AI - You use AI daily. You experiment, build, and stay close to what's changing. You've built your own workflows, tools, automations, agents, or prototypes - you don't work with AI from the sidelines.
Benefits Salary: Up to $170,000 base, plus performance bonus.
We also offer comprehensive health benefits, 401(k), 25 days PTO, hybrid working, professional development support, and the opportunity to work alongside some of the world's leading AI practitioners on high-impact enterprise transformations.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At boi, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
boi AI Hiring
boi has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national median.
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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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