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About This Role
Why Mozilla?
Mozilla Corporation is the non\-profit\-backed technology company that has shaped the internet for the better over the last 25 years. We make pioneering brands like Firefox, the privacy\-minded web browser. Now, with more than 225 million people around the world using our products each
month, we’re shaping the next 25 years of technology and helping to reclaim an internet built for people, not companies. Our work focuses on diverse areas including AI, social media, security and more. And we’re doing this while never losing our focus on our core mission – to make the internet better for people.
The Mozilla Corporation is wholly owned by the non\-profit 501(c) Mozilla Foundation. This means we aren’t beholden to any shareholders — only to our mission. Along with thousands of volunteer contributors and collaborators all over the world, Mozillians design, build and distribute open\-source software that enables people to enjoy the internet on their terms.
About This Team and Role:
Mozilla is building a new browser infrastructure platform for the new generation of web automation. We are looking for an experienced Staff Engineer to join our early\-stage team at Mozilla to lead the architectural design and implementation of our next\-generation AI agent frameworks and workflows. This is a "ground\-up" role within a fast\-moving, nimble team, making it a perfect fit for someone with early\-stage startup experience who thrives on a blank slate. You will be the technical anchor for our AI engineering efforts, establishing the patterns for how we build, evaluate, and scale autonomous capabilities.
This role sits within Mozilla’s New Products organization, which operates as an internal incubator for high\-potential ventures. You will join a fast\-paced autonomous team building a new product from its earliest stages.
What You’ll Do:
- Architect Agentic Systems from Zero: Design and deploy production\-grade multi\-agent frameworks, complex orchestration layers, and custom AI developer tooling.
- Balance Speed and Scale: Navigate the tension between rapid prototyping and long\-term sustainability, knowing exactly when to cut corners to validate a concept and when to slow down to lay rock\-solid architectural foundations.
- Orchestrate LLM Workflows: Navigate and integrate various commercial and open\-source large language models, selecting and optimizing the right models for specific agentic behaviors.
- Accelerate with AI Tooling: Lead by example by extensively leveraging advanced AI coding tools to maximize velocity, while establishing best practices for AI\-assisted engineering across the team.
- Drive Evaluation and Performance: Implement rigorous frameworks for benchmarking, prompt engineering, and evaluating agent reliability, latency, and accuracy.
- Collaborative Technical Leadership: Set engineering standards for code quality and system performance, mentoring other engineers and acting as a primary bridge between research and product.
What You’ll Bring:
- Deep Experience: You have 7\+ years of professional software engineering experience, particularly in fast\-paced environments where you've shipped complex systems from scratch.
- Pragmatic Architecture: A proven ability to judge technical debt. You know how to ship an MVP in days, but you also know how to design a clean API boundary so that early speed doesn't block future scale.
- Agentic Mastery: You bring 2\+ years of direct, hands\-on production experience specifically building autonomous workflows, custom tools, and agentic systems.
- LLM Fluency: Deep familiarity with diverse LLMs and orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen, or similar tools).
- Startup Mentality: Prior early\-stage startup experience, meaning you are comfortable with ambiguity, pivot quickly based on data, and focus heavily on execution.
- AI\-Native Workflow: Deep, everyday proficiency building software with advanced AI coding tools (such as Cursor, Copilot, or custom LLM extensions) to radically speed up delivery.
- Strong Foundations: Comfort writing clean, high\-performance code in languages like Python or Go, with a solid grasp of distributed backend systems.
Bonus Points For
- Experience building developer\-facing products, SDKs, or open\-source libraries.
- Prior contributions to open\-source AI or agent frameworks.
- Experience with vector databases and retrieval\-augmented generation (RAG) at scale.
What you’ll get:
- Generous performance\-based bonus plans to all eligible employees – we share in our success as one team
- Rich medical, dental, and vision coverage
- Generous retirement contributions with 100% immediate vesting (regardless of whether
- you contribute)
- Quarterly all\-company wellness days where everyone takes a pause together
- Country\-specific holidays plus a day off for your birthday
- One\-time home\-office stipend
- Annual professional development budget
- Quarterly well\-being stipend
- Considerable paid parental leave
- Employee referral bonus program
- Other benefits (life/AD\&D, disability, EAP, etc. – varies by country)
About Mozilla
Mozilla exists to build the Internet as a public resource accessible to all because we believe that open and free is better than closed and controlled. When you work at Mozilla, you give yourself a chance to make a difference in the lives of Web users everywhere. And you give us a chance to make a difference in your life every single day. Join us to work on the Web as the platform and help create more opportunity and innovation for everyone online.
Commitment to diversity, equity, inclusion, and belonging
Mozilla understands that valuing diverse creative practices and forms of knowledge are crucial to and enrich the company’s core mission. We encourage applications from everyone, including members of all equity\-seeking communities, such as (but certainly not limited to) women, racialized and Indigenous persons, persons with disabilities, persons of all sexual orientations, gender identities, and expressions.
We will ensure that qualified individuals with disabilities are provided reasonable accommodations to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment, as appropriate. Please contact us at [email protected] to request accommodation.
We are an equal opportunity employer. We do not discriminate on the basis of race (including hairstyle and texture), religion (including religious grooming and dress practices), gender, gender identity, gender expression, color, national origin, pregnancy, ancestry, domestic partner status, disability, sexual orientation, age, genetic predisposition, medical condition, marital status, citizenship status, military or veteran status, or any other basis covered by applicable laws. Mozilla will not tolerate discrimination or harassment based on any of these characteristics or any other unlawful behavior, conduct, or purpose.
Group: C
\#LI\-DNI
Req ID: R3177
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 Mozilla Corporation, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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.
Mozilla Corporation AI Hiring
Mozilla Corporation has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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