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About This Role
About Us:
Rare Beauty Brands is a prestige skincare company with a portfolio that includes Kate Somerville, Patchology, and Good Day. We are growing fast, and our strategy is clear: acquire and build mid-sized premium skincare brands that serve discerning consumers, and run them better than anyone else could. AI is now central to how we plan to do that. Rare Beauty Brands is an equal opportunity employer.
The Role
Rare Beauty Brands is hiring a Director, AI Transformation to move the company from using AI tools to running on them. The role reports directly to the President & CEO and works across every function: brand, digital and ecommerce, marketing, supply chain, finance, and people.
Most companies, RBB included, have adopted AI at the individual level: people use it to write faster, summarize, and draft. The next stage is different. It means redesigning how work gets done, building agents and automations into real processes, and measuring the return. Getting there takes a dedicated owner with the technical range to build it and the business judgment to know what is worth building.
That is this job. You will sit with each department head, find the friction in how their teams operate today, and then design, build, and ship solutions that remove it. Some of that work you will do yourself. Some you will do with vendors, agencies, and the people already on the team. All of it has to show up in scalability, efficiency, profitability, or growth.
Responsibilities:
Find the work worth transforming
- Partner with each member of the executive leadership team to map current workflows, pain points, and sources of friction in their functions
- Build and maintain a prioritized pipeline of AI and automation opportunities, ranked by business impact, effort, and risk
- Say no to shiny projects with no ROI, and be able to explain why
Build and ship
- Design, prototype, and deploy AI agents, automations, and integrations across our systems (ERP, ecommerce platforms, Amazon, CRM and email marketing, data and reporting tools, and the tools we have not bought yet)
- Do hands-on work yourself where it is faster, and direct vendors, agencies, or internal builders where it is not
- Own the technical architecture: how our tools connect, where data lives, how agents get access, and what guardrails are in place
Make it stick
- Lead the change management side: training, documentation, adoption tracking, and partnering with teams to redesign processes in ways that ensure adoption
- Build internal capability so the company gets more self-sufficient over time, not more dependent on you
Govern it
- Own AI governance at RBB and chair a small AI governance group (CEO, select members of the Executive Leadership Team, HR) that approves policy and signs off on higher-risk deployments
- Update and maintain RBB’s AI policy: acceptable use, approved tools, what data can go into which systems, brand safety for anything customer-facing, and where a human has to review before anything ships
- Run an agent and tool registry. Every agent, automation, and AI tool in use has a named business owner, a documented purpose, the systems and data it can touch, its cost, and a review date. Nothing goes live unregistered
- Set guardrails for agents that take action: least-privilege access, limits on what they can spend or change, logging, and a fast way to shut them off, especially when they touch customers, money, or inventory
- Protect company and customer data: classify what is sensitive, control who and what can access it, and run a security and privacy review on every AI vendor before we sign (how they store, retain, and train on our data)
- Get ahead of shadow AI: know which tools people are actually using, bring the useful ones into approved channels, and retire the risky ones
- Own the response when an agent gets something wrong, exposes data, or behaves unexpectedly: how it is caught, fixed, and reported
- Track the privacy and AI regulations that apply to RBB and keep us compliant without slowing the work down
Measure and report
- Define the metrics for every major initiative before it starts (hours saved, cost avoided, revenue lifted, cycle time cut) and report against them
- Give the CEO and ELT a clear, regular read on what is working, what is not, and what is next, including where AI risk stands: what is in the registry, what has been reviewed, and any incidents
- Stay ahead of the AI curve, continually and rapidly identifying disruption opportunities and translating complex breakthroughs into lean, high-velocity execution plans for our company.
Requirements:
Must have
- 7+ years of experience spanning operations, technology, consulting, or product, with at least 2 years leading hands-on AI, automation, or digital transformation work that you built yourself, with specific, measurable results you can point to, such as hours saved, costs reduced, or revenue gained
- Hands-on fluency with current AI tools and the ability to build with them: LLM APIs and agent frameworks, automation platforms (Zapier, Make, n8n or similar), and enough scripting (Python or JavaScript) to connect systems and move data
- Working knowledge of AI governance and data security: access controls, data privacy, vendor security review, and how to put guardrails on an agent that can take real actions in real systems. You have written an AI or data policy that people actually followed
- A track record of changing how a team actually works, not just recommending it. You have redesigned a process, lived through the rollout, and can talk about what went wrong. You have experience bringing non-technical teams through a process change, with adoption you tracked after launch
- Business judgment. You can read a P&L, size an opportunity, and tell the difference between a demo and a solution
- You have the communication range to explain a technical architecture to an engineer and a business case to a CEO in the same afternoon
- You are comfortable operating in a lean, fast-moving company where you own outcomes end to end and there is no team under you on day one
Nice to have
- Experience in consumer products, beauty, or ecommerce, and familiarity with the systems that run them (Shopify, Amazon Seller Central, NetSuite or similar ERP, Klaviyo or similar)
- Experience evaluating and managing vendors, agencies, or freelance developers
- Background in data: building reporting, defining metrics, cleaning up messy sources
What success looks like in year one
By the end of your first 90 days, you have met with every department, documented the top friction points across the company, and brought the ELT a ranked roadmap with your first two or three builds already underway. You have also refined and published RBB’s AI use policy, stood up the AI governance group, and logged every AI tool and agent already in use in a central registry.
By six months, at least one process in each major function has been redesigned around an agent or automation, with a before-and-after measure the function head would vouch for. The company is tracking value creation across all projects using standardized metrics and a universally accessible dashboard. Every agent and automation in production is in the registry, has a named owner, and has passed a security and data review.
By twelve months, the company can point to transformational gains in cost, speed, and/or revenue tied to this work, the ELT treats you as the first call when a process problem comes up, and RBB has a repeatable way to evaluate, build, govern, and roll out AI solutions that does not depend on any one person.
How this role works
- Reports to the President & CEO and works day-to-day with the full executive leadership team
- Individual contributor to start. The role is expected to prove value with the tools we already have and build internal capability, rather than a large team or expense base
- Full-time and fully remote. RBB is a work-from-home company, and this role can be based in states where we are already registered: California, Colorado, Florida, Georgia, Illinois, Kentucky, Massachusetts, New Jersey, North Carolina, Pennsylvania, Rhode Island, South Carolina, Texas, Virginia, Washington
- $130,000-$150,000 annual salary plus bonus potential
- 401(k) plus employer match, medical, dental, and vision benefits, paid time off, including company-wide self-care days
- Ownership over a growing function at a fast-moving, founder-led beauty brand incubator
- Salary Range Disclaimer - The salary range for this role reflects the minimum and maximum compensation for the role. Offers are typically made between the range minimum and the range midpoint. Actual compensation will be determined based on job-related skills, experience, and expertise, as evaluated during the interview process. Compensation may also vary based on where a role is performed
Salary Context
This $130K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Rare Beauty Brands, Inc., 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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $130K to $150K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Rare Beauty Brands, Inc. AI Hiring
Rare Beauty Brands, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $150K - $150K.
Location Context
AI roles in Boston pay a median of $215,000 across 386 tracked positions.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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