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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 a Principal Engineer who is both AI\-native and relentlessly product\-minded. You turn ambiguous, cross\-team opportunities into clear technical direction, and then you prove the path by building. You care about the flagship experiences you ship, and you obsess over the shared "paved highway" that makes it easy for the teams around you to ship safe, reliable AI over and over again.
You will succeed in this role if you are:
- Driven by outcomes. You care about helping centers stay full, save hours every week, and serve families better, not shipping "an AI feature" for its own sake.
- The strategic architect and prolific builder. You bridge strategy and deep implementation. You set the technical direction for your organization, then you get hands\-on to deliver reference implementations that teams can adopt with confidence.
- Decisive on build vs. buy. You have sharp judgment on when to leverage off\-the\-shelf models and platforms versus when to invest in proprietary infrastructure, fine\-tuning, data flywheels, or evaluation systems to create durable advantage.
- Velocity as a strategy ("show, don't tell"). You move with the hunger of a founding engineer: you prototype boldly, de\-risk quickly, and use working systems, not slides, to align teams and raise their ambition and pace.
- A force multiplier with a platform mindset. You build the paved road: evaluation harnesses, retrieval and context patterns, safety guardrails, observability, and developer experience that let product and engineering teams ship AI safely and autonomously.
- Deep on fundamentals, modern in practice. You understand why models fail (data, evaluation, human feedback, reliability), and you aggressively use modern AI\-native tooling to iterate faster without sacrificing rigor.
- Security\- and trust\-minded. You treat sensitive school, educator, and family data with care, design for least\-privilege access, and build systems that are explainable, monitorable, and resilient.
- Mission\-aligned. The idea of using AI to expand childcare capacity and improve early education outcomes, at meaningful scale, genuinely motivates you.
What You’ll Do
You will make brightwheel measurably more valuable to schools, centers, educators, and families by delivering AI\-powered capabilities that move business outcomes—for example retention, enrollment conversion, payment success, operational efficiency, and support burden. This is not a research role or a "build cool demos" role; success is defined by impact in production.
In this role, you will:
- Own outcomes end\-to\-end. Take responsibility for turning high\-leverage opportunities into shipped, adopted capabilities with clear measurement, iteration loops, and sustained improvement—not one\-time launches.
- Define the AI technical direction for your organization. Translate customer problems into a practical strategy, reference architectures, and a roadmap that multiple teams can execute against, with minimal guidance.
- Drive build\-versus\-buy decisions. Recommend when to partner on models and tooling versus when to build differentiated capabilities (data flywheels, evaluation systems, workflow\-specific intelligence), backed by evidence from fast, hands\-on spikes.
- Lead by implementation. Rapidly prototype and ship reference implementations to prove feasibility, de\-risk decisions, and raise your teams' expectation for speed and ambition.
- Help build the paved highway. Contribute shared foundations—evaluation harnesses, retrieval and context patterns, safety guardrails, observability, and developer experience—so teams can ship reliable AI features repeatedly and independently.
- Raise the bar across your organization. Mentor senior and staff engineers, lead design reviews for your teams, help establish standards for quality and safety, and align stakeholders to drive adoption.
What You’ve Done
We are open to a variety of backgrounds, but strong candidates usually bring:
- Foundational AI Depth: You possess deep intuition for why models fail, gained from experience that predates the LLM boom. You have likely worked with model training, fine\-tuning, or classical NLP/ML, giving you the grounding to make sound architectural decisions.
- 8\+ Years of Engineering Excellence: You have end\-to\-end ownership of large, business\-critical systems. You launch large\-scale and/or highly complex software that requires members of multiple teams to contribute, and you deliver largely independently with minimal guidance.
- Architectural Strategy: Experience formulating architectural strategy at the organization level. You have designed systems that required execution across multiple teams, and you regularly influence technical and product priorities.
- Mentorship and Hiring: A track record of mentoring senior and staff engineers and improving their skills, knowledge, and ability to get things done. You actively interview engineers and your judgment carries weight in debriefs.
- Production AI at Scale: A proven track record of shipping AI\-powered products to production. You understand the "last mile" of AI—evaluation, monitoring, and safety—and have built the tools that allow teams to sleep soundly at night.
Nice\-to\-haves:
- Experience earlier in your career with model training, fine\-tuning, classical machine learning, or natural language processing—enough to have intuition for why models fail and how data and evaluation shape outcomes.
- A portfolio of real work (open\-source, demos, writing, talks, or shipped side projects) that shows taste, velocity, and how you think about applied AI systems end\-to\-end.
- Experience building shared internal platforms or frameworks (for example evaluation services, retrieval infrastructure, policy and safety guardrails, observability tooling) that became the default path for multiple teams.
- Formal training in computer science (4\-year CS degree or equivalent depth in core CS topics).
- A strong bar for operational excellence: secure\-by\-default design, performance and cost discipline, testability, incident readiness, and a track record of improving development, testing, and on\-call practices for complex systems.
Technology
We work with:
- Backend: Ruby on Rails, Sidekiq
- Data: PostgreSQL on Amazon RDS, Redis, and event and analytics pipelines
- Frontend: React with TypeScript and Emotion
- Mobile: Native iOS (Swift) and Android (Kotlin with Jetpack Compose)
- Cloud \& Infrastructure: Docker, Kubernetes on Amazon EKS, GitHub Actions and FluxCD for CI/CD, and AWS services such as S3, CloudFront, CloudWatch, and SNS
- AI \& Automation: AWS Bedrock and other hosted large language models,, orchestration and agent frameworks, and modern AI coding tools like Cursor, Claude Code and Codex
*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: $194K \- $263K
Salary Context
This $194K-$263K range is above the 75th percentile 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 ($228K) sits 26% above the category median. Disclosed range: $194K to $263K.
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
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