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About This Role
At Braze, we have found our people. We're a genuinely approachable, exceptionally kind, and intensely passionate crew.
We seek to ignite that passion by setting high standards, championing teamwork, and creating work\-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity – inside and outside our organization.
To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success.
Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one\-of\-a\-kind vibrancy into our culture.
If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can't wait to meet you.
WHAT YOU'LL DO
We're looking for a Senior Lead Systems Engineer who's passionate about building the next generation of enterprise technology through automation, software engineering, AI, and intelligent integrations. In this role, you'll architect scalable solutions that connect the systems powering Braze while helping redefine how employees interact with IT through conversational experiences, automation, and self\-service.
You'll lead complex technical initiatives from concept through delivery, building integrations that span platforms such as Slack, Okta, Workday, Atlassian, GitHub, Google Workspace, and other critical business systems. You'll partner closely with Security, Infrastructure, Product, and IT teams to build reliable, scalable solutions that reduce operational overhead and improve the employee experience.
This role goes beyond traditional system engineering. We're looking for someone who enjoys solving business problems through software, APIs, automation, and AI. You'll help shape our long\-term strategy around enterprise integrations, intelligent workflows, and agentic experiences that enable employees to get work done faster and more intuitively.
You'll be part of a team that values reliability, resiliency, scalability, extensibility, and thoughtful engineering. We operate with a shared sense of ownership, continuously look for opportunities to automate manual work, and embrace emerging technologies that allow us to scale efficiently while delivering exceptional internal experiences.
If you enjoy designing enterprise architecture, building integrations, and leveraging AI to modernize how organizations operate, we'd love to meet you.
You'll lead initiatives that shape the future of enterprise technology at Braze, including:
- Enterprise Automation \& AI:
+ Design and build intelligent automation that streamlines business processes across enterprise platforms. Leverage AI, agentic workflows, and modern integration patterns to reduce manual effort, improve operational efficiency, and enhance employee productivity
- Enterprise Integrations:
+ Design and develop scalable integrations across platforms including Slack, Okta, Workday, Atlassian, GitHub, Google Workspace, and other SaaS applications. Build APIs, middleware, and event\-driven workflows that create seamless employee experiences
- Conversational IT \& Self\-Service:
+ Help evolve how employees interact with IT by building conversational interfaces and self\-service experiences that reduce reliance on traditional ticket\-based workflows while improving service delivery and employee satisfaction
- Enterprise Platform Architecture:
+ Design scalable, resilient solutions that improve how enterprise platforms communicate and operate together. Evaluate architecture decisions with long\-term maintainability, extensibility, and operational excellence in mind
- AI Platform Enablement:
+ Evaluate, pilot, and implement emerging AI technologies, large language models, MCP integrations, and agent frameworks that enable intelligent automation across enterprise systems while establishing best practices for responsible adoption
- Automation \& Developer Experience:
+ Build reusable automation frameworks, integration services, and internal tooling that accelerate delivery, reduce operational toil, and improve the maintainability of enterprise platforms
- Governance \& Operational Excellence:
+ Partner with Security, Compliance, and Infrastructure teams to ensure enterprise solutions meet governance requirements while maintaining simplicity, scalability, and operational maturity. Document architecture, implementation decisions, and operational processes following governance and compliance standards
- Innovation \& Emerging Technology:
+ Continuously research emerging technologies, integration capabilities, and AI advancements that can improve scalability, reduce operational complexity, and create new opportunities for automation
- Strategy \& Technical Leadership:
+ Contribute to enterprise platform strategy and long\-term technical roadmaps. Provide technical leadership on complex initiatives while mentoring engineers on automation, integration, and software engineering best practices
WHO YOU ARE
- 8\+ years of experience designing enterprise\-scale integrations and automation across modern SaaS environments
- Strong software engineering background with experience building APIs, integrations, automation, or platform services
- Proficient in one or more programming languages such as Python, Go, TypeScript, or JavaScript
- Experienced working with REST APIs, webhooks, OAuth, SCIM, GraphQL, and modern authentication standards
- Familiar with enterprise platforms such as Okta, Workday, Slack, Atlassian, GitHub, Google Workspace, or similar SaaS ecosystems
- Skilled in designing scalable architectures that balance business requirements, security, and operational simplicity
- Experienced collaborating across engineering, security, infrastructure, and business teams to deliver impactful technical solutions
- Passionate about leveraging AI, automation, and software engineering to solve operational challenges
- Curious, collaborative, and motivated by continuous improvement and thoughtful systems design
Bonus:
- Experience implementing AI agents, MCP integrations, LLM\-powered workflows, or enterprise AI platforms
- Experience with Terraform, Infrastructure as Code, or cloud automation tooling
- Experience with event\-driven architectures, message queues, or serverless technologies
- Experience integrating Workday, Okta, Atlassian Cloud, Slack Platform, GitHub Enterprise, or Google Workspace APIs
- Familiarity with cloud platforms such as AWS, GCP, or Azure
- Experience building internal developer platforms or enterprise integration frameworks
- Experience supporting high\-growth SaaS organizations with complex enterprise technology environments
For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $116,000 and $182,000/year with an expected On Target Earnings (OTE) between $129,000 and $202,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job\-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.
WHAT WE OFFER
*Braze benefits vary by location, and we encourage you to review our specific benefits offerings for each country* *here**. More details on benefits plans will be provided if you receive an offer of employment.*
From offering comprehensive benefits to fostering hybrid ways of working, we've got you covered so you can prioritize work\-life harmony. Braze offers benefits such as:
- Competitive compensation that may include equity
- Retirement and Employee Stock Purchase Plans
- Flexible paid time off
- Comprehensive benefit plans covering medical, dental, vision, life, and disability
- Family services that include fertility benefits and equal paid parental leave
- Professional development supported by formal career pathing, learning platforms, and a yearly learning stipend
- A curated in\-office employee experience, designed to foster community, team connections, and innovation
- Opportunities to give back to your community, including an annual company\-wide Volunteer Week and donation matching
- Employee Resource Groups that provide supportive communities within Braze
- Collaborative, transparent, and fun culture recognized as a Great Place to Work®
ABOUT BRAZE
Braze is the leading customer engagement platform that empowers brands to Be Absolutely Engaging™. Braze helps brands deliver great customer experiences that drive value both for consumers and for their businesses. Built on a foundation of composable intelligence, BrazeAI™ allows marketers to combine and activate AI agents, models, and features at every touchpoint throughout the Braze Customer Engagement Platform for smarter, faster, and more meaningful customer engagement. From cross\-channel messaging and journey orchestration to Al\-powered decisioning and optimization, Braze enables companies to turn action into interaction through autonomous, 1:1 personalized experiences.
The company has been consistently recognized as a Leader in marketing technology by industry analysts, and was named a G2 "Best of Marketing and Digital Advertising Software Product" in 2026\. Braze was also named a 2026 Best Places to Work by Built In, a 2025 America's Greenest Companies by Newsweek, and a 2025 Fortune Best Workplace in Technology™ by Great Place To Work®. Braze is also proudly certified as a Great Place to Work® in the U.S., the UK, Australia, and Singapore.
The company is headquartered in New York with offices in Austin, Berlin, Bucharest, Chicago, Dubai, Jakarta, London, Paris, San Francisco, São Paulo, Singapore, Seoul, Sydney and Tokyo.
BRAZE IS AN EQUAL OPPORTUNITY EMPLOYER
At Braze, we strive to create equitable growth and opportunities inside and outside the organization.
Building meaningful connections is at the heart of everything we do, and that includes our recruiting practices. We're committed to offering all candidates a fair, accessible, and inclusive experience – regardless of age, color, disability, gender identity, marital status, maternity, national origin, pregnancy, race, religion, sex, sexual orientation, or status as a protected veteran. When applying and interviewing with Braze, we want you to feel comfortable showcasing what makes you *you*.
We know that sometimes different circumstances can lead talented people to hesitate to apply for a role unless they meet 100% of the criteria. If this sounds familiar, we encourage you to apply, as we'd love to meet you.
OUR AI\-POWERED BRAZE RECRUITMENT PROCESS
At Braze, we're committed to a fair and transparent candidate experience. To help our recruitment teams focus on what matters most — the person behind each application — we use AI\-assisted tools at certain stages of our recruitment process.
This includes using AI to analyze the experience, skills and qualifications in your application materials to help with screening and prioritizing candidates. Such screening may amount to a form of solely automated decision\-making. We also use AI for administrative support, like scheduling and recording interviews and summarizing interview notes. Our recruiting teams remain responsible for all hiring decisions and are involved throughout the process.
Depending on where you are located, you may have the right to request further information about how AI is used in our recruitment process, to opt out of AI\-assisted review, to request a manual review of any decision made or to contest a decision.
Please contact us at [email protected] for any requests or questions.To find out more about our hiring process, check out this page.
Notice Regarding Automated Employment Decision Tool (NYC Local Law 144\)
Our use of AI during the application review process may include the use of automated employment decision tools. Pursuant to New York City Local Law 144, for roles based in New York City, or if you reside in New York City, you have the right to request an alternative selection process or a reasonable accommodation instead of AI\-assisted review. Please submit any such request to our Talent Acquisition team at [email protected] promptly after applying. A summary of the most recent bias audit results for such tool is available here*.*
*Please see our* *Candidate Privacy Policy* *for more information on how Braze processes your personal information during the recruitment process and, if applicable based on your location, how you can exercise any privacy rights.*
Salary Context
This $116K-$202K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Braze, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($159K) sits 26% below the category median. Disclosed range: $116K to $202K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Braze AI Hiring
Braze has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span New York, NY, US, Austin, TX, US. Compensation range: $202K - $232K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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