Interested in this AI/ML Engineer role at BABYLIST?
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What the Role Is
Registry is the heart of Babylist – how we help growing families feel prepared during one of life's most consequential transitions, and the acquisition and commercial engine powering the rest of our business.
We're hiring a design leader to own it.
This role is the durable design and experience owner for Registry, accountable for the UX, vision, KPIs, and quality bar that define how it feels. You translate company goals into clear priorities so your org always knows what to work on and why. You take bold bets, pushing past safe incrementalism to make the case for step\-function improvements — advocating for customers' long\-term needs while ensuring business impact. You epitomize being a builder: getting into the work, codebase, and data directly rather than delegating, standing up prototypes and contributing to production\-quality interfaces when that's fastest. You partner with engineers, PMs, and analysts as peer\-builders, setting the standard for what an AI\-native designer creates. You shape the craft standards and operating model that define Design at Babylist, leading the team's transition to AI\-native product development at scale.
Reports to the VP, Head of Product Design and Research; sits on the Consumer Tech Leadership team; leads 3–4 product designers.
Who You Are
- Strategic visionary. You combine user needs, business priorities, and sharp product and design sense into an inspirational vision that articulates the future, allowing the org to work backward with clarity and strategic focus.
- Craft and experience obsessed. You care deeply about how things feel, not just how they function. You polish the details like transitions, motion, and accessibility. You hold a high bar for delight and have a portfolio of shipped consumer product work across web and native mobile that demonstrates this.
- Systems thinker. You see the full picture. You think about how a single interaction fits into a broader experience, how Registry flows connect across the Babylist ecosystem, and how the design system enables all of it.
- AI\-native practitioner\-leader. You use AI tools to prototype, explore, and iterate at speed. You stay close to the craft, review work at depth, and can build when building is the fastest path to impact. You lead from a place of real craft knowledge and hands\-on understanding of what it takes to ship.
- Human\-and\-data\-centered. You know how to leverage (and plan and run) generative and evaluative research to unlock actionable insights that guide product decisions. You bring concrete qualitative and quantitative evidence into design decisions and share clear, persuasive design rationale via concise and impactful communication.
- Industry aware and pattern fluent. You stay current with modern product design to borrow what's proven and challenge what's stale. You know the interaction patterns, design system conventions, and digital product best practices that have become table stakes, and when to apply them or evolve them.
- Clear and critical thinker. You cut through complexity with thoughtful synthesis of competing inputs, stress\-testing assumptions, and communicate your point of view with extreme clarity. You don't mistake collaboration for consensus and encourage productive conflict, preferring to surface misalignment early to accelerate and de\-risk decisions.
- Outcome owner. You don't celebrate shipping, you own impact. You instrument your own work, track whether it's moving the right metrics, and hold yourself and your org accountable for meaningful results.
- People leader. You hire, develop, retain, and inspire a team of designers. You give direct, honest feedback, manage performance with care and clarity, and create an environment where designers do their best work.
- Operational leader. You build and maintain the systems, rituals, and processes that allow your team to execute with quality and consistency. You know the difference between process that enables and process that burdens, and you make deliberate choices about both.
- Adaptable to change. You select for change, not against it. You jump in where needed, working across team boundaries without waiting for permission. You are humble, low\-ego, and biased toward action. You bring curiosity to ambiguity, not anxiety.
- Excited about the AI transformation. You believe this is the most interesting moment in modern design careers, and you want to help shape what an AI\-first design organization looks like both at Babylist and as a category.
How You Will Make An Impact
- Own the Registry experience end\-to\-end. Think holistically about user flows, brand expression, information architecture, and interaction patterns across the full Babylist cross\-platform ecosystem. You are accountable for the quality and outcomes of everything your team ships.
- Lead with equal parts vision, strategy, and execution. Connect design vision to company strategy and business goals. Articulate where the Registry experience should be in 12\-18 months and, together with your PM and Engineering peers, set strategy by defining the sequence of bets that get us there while earning organizational alignment.
- Define excellence. Set the bar for quality, delight, and product excellence by demonstrating and teaching the organization what great looks and feels like. Eschew perfection for learning that propels the product toward your high bar.
- Stay deeply connected to users and the work. Plan and execute research to ground design decisions in how families actually behave. Lead from a place of real craft knowledge and hands\-on understanding of what it takes to ship.
- Operate with agency. Don't wait for permission. Create the conditions for your and your team's success. Bias toward action, starting before you have the full picture and bringing others along as you learn. Hold yourself and others accountable and avoid attributing setbacks to external conditions.
- Serve as a model for AI\-first Design at Babylist. Use AI\-native workflows in your own work, ship things yourself, and help define the rituals and practices that the rest of the function will adopt.
- Scale design democratization. Build AI\-enabled workflows that bring others into design tools, creating the conditions for non\-designers to contribute meaningfully to the experience layer. Act as a consultant and quality bar on that work, not a gatekeeper.
- Build a high\-performing design team. Hire, develop, retain, and organize designers that execute with speed and quality. Create a growth\-mindset\-fueled environment where designers do their best work. Manage performance directly and invest in the career growth of everyone on your team.
- Multiply your team. Be an excellent partner to engineering, product management, analytics, marketing, and merchandising leaders as co\-builders to move the highest\-impact work forward. Bring tighter problem framing, sharper user insight, and prototypes that make decisions easier to make.
- Contribute to the shape of the Consumer Tech organization. Work with the VP of Product Design, your peers, and other leaders across Tech to evolve our operating model, our hiring bar, and our craft standards as the team transitions.
About Compensation
We use a market\-based approach to compensation. The starting salary range for this role is:
$244,600 to $305,700 \+ target 22\.5% annual bonus \+ competitive equity
Your starting salary will be based on your location, experience, and qualifications, with increases over time tied to performance, role growth, and internal pay equity.
Who We Are
Babylist is the leading platform for expecting and new families. More than 10 million people shop with Babylist every year, making it the go\-to destination for seamless purchasing, guidance, and expert recommendations. As a modern, AI\-forward tech company, Babylist has expanded from a universal registry into a full ecosystem — the Babylist Shop, Babylist Health, Babylist Money, NYC and LA showrooms, branded content, and more — generating $750M in revenue in 2025\. Building the generational brand in baby, Babylist is reshaping the $235B kids and baby market and helping parents feel confident, connected, and cared for at every step.
Our Ways of Working
Babylist is remote\-first with team members across the U.S. and Canada who move fast, think smart, and use AI as part of how they work every day — not as an experiment, as an expectation. We come together twice a year to build the relationships behind the work, and we hire people who are genuinely excited about what's possible and prove it through how they show up.
Why You Will Love Working At Babylist
Our Culture
- We work with focus and intention, then step away to recharge
- We believe in exceptional management and invest in tools and opportunities to connect with colleagues
- We build products that positively impact millions of people's lives
- AI is intentionally embedded in how we work, create, and scale—supporting innovation and impact
Growth \& Development
- Competitive pay and meaningful opportunities for career advancement
- We believe technology and data can solve hard problems
- We're committed to career progression and performance\-based advancement
Compensation \& Benefits
- Competitive salary with equity and bonus opportunities
- Company\-paid medical, dental, and vision insurance
- Retirement savings plan with company matching and flexible spending accounts
- Generous paid parental leave and PTO
- Remote work stipend to set up your office
- Perks for physical, mental, and emotional health, parenting, childcare, and financial planning
### Important Notices
Recorded Interviews. Babylist uses an interview recording tool to record and transcribe interviews for evaluation purposes in accordance with applicable privacy laws. By participating in an interview, you consent to this recording and transcription.
Interview Integrity. AI is part of how we work at Babylist — we expect you to use it too. Your application and interviews should still reflect you and your own thinking. We'll tell you when AI is encouraged. Misrepresentation at any stage may result in removal from consideration for this and future roles.
Connections at Babylist. If you have a family member or close personal relationship with a current Babylist employee, please let your recruiter know. This helps us keep our process fair and transparent for everyone.
Protect Yourself from Scams. All official outreach comes from the Babylist Talent Team via @babylist.com email addresses only. We will never ask for payment or personal financial information. If you receive outreach via WhatsApp, Telegram, or a non\-Babylist email — it's not us. Verify open roles at babylist.com/careers.
Salary Context
This $244K-$305K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BABYLIST, 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($275K) sits 26% above the category median. Disclosed range: $244K to $305K.
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.
BABYLIST AI Hiring
BABYLIST has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $305K - $305K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 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 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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