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About This Role
Why Thunderbird?
Thunderbird is one of the world’s most trusted open\-source email applications, empowering more than 20 million people globally. At MZLA, the team behind Thunderbird, we build privacy\-respecting communication and productivity tools that help people manage their digital lives while staying in control of their data.
We are open source by design. Our products are developed transparently and in collaboration with a global community of contributors, and that same spirit shapes how we work: collaboratively, inclusively, and with a shared commitment to putting users first.
We are a small but growing company of 60\+ people distributed across seven countries. As Thunderbird evolves beyond a single desktop application, we are expanding across desktop, mobile, paid services, and new product experiences that help people and organizations work more effectively without giving up control to big tech ecosystems.
Our revenue model combines user donations, which help keep Thunderbird’s apps freely available, with paid services that cover the costs of hosted offerings while supporting long\-term sustainability and continued innovation.
A note about MZLA and Mozilla: MZLA Technologies Corporation is the nonprofit\-owned company behind Thunderbird and part of the Mozilla family. MZLA is separate from Mozilla Corporation and has its own compensation programs, benefits, and employment policies.
The opportunity
Thunderbird is expanding beyond email with Thunderbolt, an open\-source AI client designed for organizations that need more control over their data, infrastructure, and model choices.
We’re looking for a Staff Design Engineer to help shape the product experience for Thunderbolt as it moves from early product development into enterprise use cases. This role sits at the intersection of product design, front\-end engineering, prototyping, and AI\-assisted product development.
You’ll turn early product ideas, user needs, partner feedback, and workflow concepts into working prototypes and production\-ready user experiences. This is not a role focused only on mockups or design artifacts. You’ll work directly in the codebase, use AI\-assisted development tools thoughtfully, and help define the interaction patterns that make Thunderbolt intuitive, useful, trustworthy, and enterprise\-ready.
You should be comfortable operating independently in ambiguity, moving quickly from concept to prototype, distinguishing between experiments and production\-ready work, and creating reusable product and interaction patterns that help the broader team move faster.
What you’ll do
- Build functional prototypes and product experiments directly in the Thunderbolt codebase.
- Translate ambiguous product ideas, user workflows, partner feedback, and enterprise needs into clear, usable product experiences.
- Help define interaction patterns for AI\-powered chat, search, research, workflow automation, knowledge retrieval, and enterprise administration.
- Use AI\-assisted development tools to accelerate prototyping and implementation while maintaining strong judgment around quality, usability, security, accessibility, and maintainability.
- Partner closely with product, design, engineering, and enterprise\-facing teams to explore, validate, and refine product direction.
- Contribute production\-quality front\-end code where appropriate.
- Identify when a prototype should be discarded, refined, generalized, or productionized.
- Improve the usability, accessibility, consistency, and polish of the Thunderbolt user experience.
- Help develop reusable components, interface patterns, or design\-system elements that create leverage for the broader product team.
- Bring a user\-centered perspective to technical and product discussions, especially where AI behavior, user trust, transparency, permissions, or workflow clarity are important.
- Consider enterprise needs when designing product experiences, including data sensitivity, user control, administrative visibility, auditability, and security expectations.
- Contribute to a product culture that values speed, clarity, experimentation, maintainability, responsible AI use, and thoughtful user experience.
What you bring
- 10\+ years of relevant experience in design engineering, front\-end engineering, UX engineering, product design with strong technical implementation experience, or a closely related role, with demonstrated leadership and impact beyond individual deliverables.
- Strong front\-end engineering skills, including experience with modern JavaScript or TypeScript, component\-based frameworks, and building prototypes or production features directly in an application codebase.
- Strong product and interaction design judgment, including usability, accessibility, information architecture, workflow design, and product polish.
- Experience designing, prototyping, or building AI\-enabled product experiences, such as chat, search, research tools, agents, automation, knowledge interfaces, or workflow tools where user trust, context, output quality, and human oversight are central to the experience.
- Experience translating ambiguous product concepts into usable workflows, prototypes, and shipped product experiences, with the judgment to balance speed, quality, maintainability, learning, and product risk.
- Experience providing technical or product leadership through mentoring, cross\-functional coordination, reusable patterns, project ownership, or influence beyond individual deliverables.
- Practical awareness of AI, privacy, security, and regulatory considerations that may affect product experience, including transparency, user control, permissions, data handling, and trust; familiarity with frameworks such as the EU AI Act, Cyber Resilience Act, HIPAA, or comparable responsible AI, privacy, and security requirements.
- Strong communication and collaboration skills, including experience working closely with product managers, designers, engineers, and other cross\-functional stakeholders.
- Portfolio, case studies, shipped work, or examples demonstrating design\-to\-code execution, interactive prototypes, or product experience improvements.
Bonus points for
- Experience with enterprise software, developer tools, productivity tools, collaboration tools, self\-hosted software, or other technical user workflows.
- Experience contributing to design systems, component libraries, reusable front\-end architecture, or shared product interaction patterns.
- Familiarity with user research, usability testing, product discovery, feature instrumentation, or rapid experimentation methods.
- Experience working in open\-source or highly collaborative engineering environments.
What success looks like
In this role, you will help Thunderbolt move from ideas and early workflows into clear, usable, high\-quality product experiences. You’ll create prototypes that clarify direction, reusable patterns that help the team move faster, and production experiences that make AI\-powered workflows easier to understand and trust.
A successful Staff Design Engineer will help the team decide what is worth building, what needs more testing, and what should not move forward. You’ll raise the quality of Thunderbolt’s user experience while helping the product remain fast\-moving, practical, and enterprise\-ready.
Work environment
You’ll work with our passionate staff and open\-source community members from all over the globe to support the mission and objectives of MZLA Technologies Corporation. In this role, you’ll also collaborate closely with engineering, product, design, enterprise\-facing teams, contractors, and external contributors working on Thunderbolt.
This is a remote, full\-time position. Strong communication and documentation skills are essential for effective collaboration across time zones and disciplines.
Compensation \& benefits
We benchmark our base salaries to local markets and target the 60th percentile of the peer market. The salary ranges for this role are:
- US: $185,000 \- $205,000 USD
We may consider candidates with strong skills but less than the required experience. Title, level and compensation will be determined based on qualifications and experience.
In addition to competitive salaries, we offer a comprehensive benefits package designed to support your whole self.
Work \& career
- Fully remote work \& schedule flexibility
- Company\-provided laptop
- Annual bonus program
- Monthly remote work stipend
- Annual professional development stipend
- Access to Coursera learning platform
- Industry conferences
- Company all\-hands and team gatherings
Rest \& play
- 24 days PTO per year (prorated)
- Your birthday
- Year\-end company shutdown
- 9 wellbeing days
- Public holidays
- Other paid leave
- Quarterly wellbeing stipend for personal / family activities
Health \& family
- 401(k) contributions
- Health, dental, \& vision insurance
- Disability insurance
- Life insurance
- Employee assistance program
- Paid parental leave
- Paid sick days
Work eligibility
Applicants must reside in and have permanent work authorization for the country location(s) specified in the posting. We are unable to consider applicants outside of these markets at this time. And, we do not provide visa sponsorship.
How to apply
Please apply directly through our career page. We carefully review every cover letter and screening question, so take the time to answer each fully.We value authentic, thoughtful responses that reflect your own experience and perspective. It is fine to use AI tools to polish your writing, but your answers should be your own. Candidates who submit generic or unoriginal AI\-generated responses may be disqualified from further consideration.
Our commitment to diversity, equity, and inclusion
MZLA believes in the value of diverse creative practices and forms of knowledge, and knows diversity, equity and inclusion 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 not limited to) women, racialized and Indigenous persons, persons with disabilities, persons of all sexual orientations, gender identities and expressions.
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. MZLA will not tolerate discrimination or harassment based on any of these characteristics or any other unlawful behavior, conduct, or purpose.
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.
\#LI\-DNI
Salary Context
This $185K-$205K range is above 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 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 $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 ($195K) sits 9% below the category median. Disclosed range: $185K to $205K.
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.
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: $205K - $205K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 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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