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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, and Pocket, a service for keeping up with the best content online. 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:
The AI Platform team is responsible for building the foundational infrastructure that powers intelligent experiences across Mozilla products. This includes model training pipelines, high\-throughput inference services, GPU orchestration, and secure, privacy\-respecting AI systems that operate reliably at global scale.
We’re looking for a Machine Learning Engineer with a strong platform mindset to help design, build, and operate Mozilla’s AI platform. In this role, you’ll work at the intersection of machine learning, distributed systems, and production infrastructure—ensuring that models can be trained, deployed, and served efficiently, securely, and at scale. You will collaborate closely with product, infrastructure, and security teams to enable fast iteration while meeting strict performance and privacy requirements.
What You’ll Do:
- Design, build, and operate core AI platform components used to train, deploy, and serve machine learning models in production environments.
- Own model serving and inference workflows end\-to\-end, driving improvements in reliability, scalability, performance, and operational excellence.
- Lead efforts to optimize inference systems for throughput, latency, and cost efficiency across CPU and GPU workloads.
- Design and manage GPU\-based inference and training workloads, including performance tuning, capacity planning, and resource utilization optimization.
- Own and improve critical parts of the model lifecycle, including packaging, versioning, testing strategies, validation, and deployment automation.
- Implement and evolve observability practices (metrics, logging, tracing, alerting) to improve visibility and operational resilience of ML services and pipelines.
- Partner closely with product, infrastructure, security, and data teams to design scalable platform capabilities that enable AI\-powered features.
- Contribute to technical design discussions, propose architectural improvements, and mentor junior engineers through code reviews and knowledge sharing.
- Participate in and help improve operational processes, including incident response, on\-call rotations, and post\-incident reviews.
What You’ll Bring:
- Bachelor’s degree with 4–6 years of relevant industry experience, or Master’s degree with significant hands\-on experience building and operating production ML systems, or work experience equivalent
- Strong experience developing in Python for machine learning systems, backend services, or distributed data processing.
- Proven experience deploying and operating ML workloads in cloud environments, including production\-grade infrastructure.
- Solid understanding of model serving architectures, inference pipelines, and performance tradeoffs (latency, throughput, cost, scaling strategies).
- Hands\-on experience working with GPU\-based workloads and accelerated computing in production settings.
- Experience designing CI/CD pipelines and development workflows that support reliable ML system deployment.
- Ability to independently scope and drive technical initiatives while balancing product and operational priorities.
- Strong problem\-solving skills and the ability to debug performance and reliability issues in distributed systems.
- Clear and effective communication skills, with experience collaborating across engineering, product, and infrastructure teams.
Bonus Skills:
- Experience implementing inference optimization strategies such as batching, quantization, compilation, model conversion, or hardware\-specific tuning.
- Familiarity with containerization and orchestration systems (e.g., Docker, Kubernetes) in production environments.
- Experience designing observability systems for distributed services, including metrics strategy and performance profiling.
- Exposure to privacy\-preserving ML techniques, security best practices, or responsible AI system design.
- Contributions to open\-source ML infrastructure projects or leadership in building reusable internal ML tooling.
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: D
\#LI\-REMOTE
Req ID: R3074
Hiring Ranges:
US Tier 1 Locations
$163,000 \- $218,000 USD
US Tier 2 Locations
$150,000 \- $200,000 USD
US Tier 3 Locations
$139,000 \- $185,000 USD
Salary Context
This $139K-$218K range is below 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 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $139K to $218K.
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.
Mozilla Corporation AI Hiring
Mozilla Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $218K - $218K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 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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