Interested in this AI/ML Engineer role at brightwheel?
Apply Now →Skills & Technologies
About This Role
Our Mission and Opportunity
Early education is one of the most important determinants of childhood outcomes, a critical support for working families, and a $175B market that remains underserved by modern technology. Brightwheel is the largest, fastest growing, and most loved platform in early ed, trusted by millions of educators and families every day. We are a three\-time Cloud 100 company, backed by top investors including Addition, Bessemer, Emerson Collective, Lowercase Capital, Notable Capital, and Mark Cuban.
Our Team
Our team is passionate, talented, and customer\-focused. We embody our Leadership Principles in our work and culture. We are a distributed team with remote employees across every US time zone, as well as select offices in the US and internationally.
Who You Are
---------------
You are an AI\-native Senior Technical Program Manager who combines deep systems\-level engineering judgment, strong product sense, and exceptional execution to drive our hardest, most technically complex, multi\-stakeholder initiatives. You act as the technical glue in ambiguous spaces, transforming company\-wide priorities into high\-velocity delivery across multiple engineering pods, product lines, and operations teams. You lead by example in how modern, automated execution gets done, ensuring our AI\-driven capabilities are safe, scalable, and delivered with incredible velocity.
You will succeed in this role if you are:
- Focused on execution and business impact: You care about scaling brightwheel’s value and impact by orders of magnitude. You don't just manage timelines; you ruthlessly unblock technical dependencies and manage systemic risks to deliver better outcomes for our users.
- A practitioner\-leader and builder: You do not coordinate from a distance. You are a hands\-on technical force multiplier. You use AI assistants, agents, and modern tooling in your own daily workflows to automate tracking, write code scripts, build prototypes, and show what great execution looks like.
What You’ll Do
Brightwheel already supports the workflows that keep early education businesses running: enrollment, billing, staffing, classroom operations, family communication, and compliance. The next step is bigger. We are using AI to turn brightwheel from a system of record into a system of action, reducing toil, automating routine work, improving decision\-making, and accelerating how we build.
You will orchestrate the technical execution of this vision. You will manage the cross\-functional delivery of our most complex initiatives, cutting across product engineering, infrastructure, data pipelines, and internal business systems.
In this role, you will:
- Drive high\-stakes, multi\-stakeholder technical programs that embed AI capabilities across our entire product surface area—ensuring tight synchronization between data, platform, and frontend product teams.
- Manage the complex execution dependencies of building software that recommends next steps, completes routine work, and automates meaningful parts of our customers’ operations.
- Partner with engineering leadership to design and operate an AI hybrid workforce, establishing the necessary guardrails, evaluation frameworks, governance, and observability required for autonomous agents.
- Streamline internal engineering velocity by implementing AI\-powered tooling, automated testing pipelines, and agentic workflows that catch issues early and minimize operational overhead.
- Apply an AI\-native programmatic approach to optimizing internal functions like customer support and onboarding, ensuring cross\-functional alignment as we scale.
- Facilitate critical technical trade\-offs, driving consensus on build\-versus\-buy decisions, architectural patterns, and platform leverage without losing momentum.
What You’ve Done
We are open to a variety of backgrounds, but qualified candidates usually bring:
- A strong computer science foundation: You have a 4\-year computer science degree or equivalent depth in core CS topics, giving you the technical grounding to reason well across distributed systems, machine learning abstractions, and data architecture.
- A record of orchestrating complex systems at scale: You have a proven track record of landing highly technical, multi\-team programs from inception to production deployment. You understand what it takes to make deeply integrated products succeed in live production environments.
- Applied AI familiarity in production: You have driven programs involving AI, LLM orchestration, or automated data pipelines. You understand the nuances of non\-deterministic software, including evaluation, prompt engineering, latency bottlenecks, and safety boundaries.
- Influence without authority: You have extensive experience driving alignment across senior engineers, product directors, and executive stakeholders without having direct reporting lines over them.
What Sets You Apart
The candidates who will thrive in this role go beyond the standard program management tracking toolkit:
- Product taste and technical intuition: You operate without needing a product manager crutch. You use your deep technical knowledge to bridge the gap between architectural constraints and consumer value, ensuring we build the right things the right way.
- A "T\-Shaped" builder mindset: You are deeply expert in program execution and system architecture, but capable of diving just\-in\-time into data engineering, cloud infrastructure, or frontend flows. You use AI to multiply your learning speed, allowing you to confidently review code or write scripts to validate integration paths.
- Fearless handling of ambiguity: You run toward the company's messiest, least\-defined technical friction points. You create structure, define clear milestones, and build high\-fidelity alignment where none existed.
- Hands\-on fluency with AI\-native development: You don't just write documentation; you use AI coding tools (Claude Code, Cursor, v0\) to automate your own technical tracking, build internal tooling dashboards, or construct technical proof\-of\-concepts to unblock engineering discussions.
Technology
We work with:
- AI \& Automation: Frontier models, vector databases, orchestration and agent frameworks, and modern AI coding tools like Claude Code / Cursor.
- Backend \& Data: Ruby on Rails, Sidekiq, PostgreSQL on Amazon RDS, Redis, event and analytics pipelines.
- Frontend \& Mobile: React with TypeScript, Native iOS (Swift), and Android (Kotlin with Jetpack Compose).
- Cloud \& Infrastructure: Docker, Kubernetes on Amazon EKS, GitHub Actions, FluxCD, and core AWS services (S3, CloudFront, CloudWatch, SNS).
*Brightwheel is committed to creating a diverse and inclusive work environment and is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity, gender expression, sexual orientation, national origin, genetics, disability, age, or veteran status.*
*Protecting Our Applicants: Please be aware of recruiting scams impersonating Brightwheel. All legitimate communications come from* *@**mybrightwheel.com* *addresses, and we never ask for payment or sensitive personal data as part of our hiring process. If you suspect fraudulent contact, reach out to* *[email protected]**. Thank you for helping us keep our applicant community safe.*
Compensation Range: $154K \- $237K
Salary Context
This $154K-$237K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At brightwheel, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($195K) sits 8% above the category median. Disclosed range: $154K to $237K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
brightwheel AI Hiring
brightwheel has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $237K - $263K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.