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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 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.
Our small but growing distributed team includes more than 65 people across seven countries. As Thunderbird evolves beyond a single desktop application, we are expanding across desktop, mobile, paid services, AI offerings, and enterprise-facing work to build independent, privacy-respecting alternatives to big tech ecosystems.
Our revenue model combines user donations, which help keep Thunderbird’s applications freely available, with paid services that cover the costs of hosted offerings while supporting long-term sustainability and continued innovation.
A note about Thunderbird and Mozilla: Thunderbird Technologies Corporation is the nonprofit-owned company behind Thunderbird email and a growing portfolio of products. While part of the Mozilla family, Thunderbird is a separate entity from Mozilla Corporation and Mozilla Foundation with 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 Full-Stack Product Engineer: a full-stack engineer who ships production code and works directly with the partners and enterprise customers adopting Thunderbolt. You’ll spend most of your time in the codebase, building the features, integrations, and customizations that real deployments need. The rest of your time goes to working alongside technical partners to get those deployments running and keep them healthy.
Thunderbolt is delivered largely through technical partners and systems integrators who own the day-to-day customer relationship. You’ll be the engineer behind them. You’ll understand how a customer’s environment actually works, turn that understanding into working software, and fix the hard problems that need someone who knows the code.
What you’ll do
- Build and ship full-stack features, integrations, and customer-specific customizations in the Thunderbolt codebase, from front-end UI through APIs, services, and deployment configuration.
- Work directly with technical partners and enterprise customers to understand their environments, requirements, and constraints, and turn them into technical plans and working software.
- Support pilots, proofs of concept, and early production deployments, including partner-hosted and customer-controlled environments.
- Diagnose and fix complex issues across the application, infrastructure, identity and access, integration, and data-access layers, including bugs and regressions that need a code change and a release.
- Maintain customer-specific customizations over time so they keep working as the core product evolves.
- Spot which customer requests should become general product capabilities, and work with product and engineering to build them that way rather than as one-offs.
- Bring real-world deployment feedback into roadmap and prioritization discussions.
- Write the documentation, deployment guidance, and runbooks that let partners handle routine issues on their own.
- Use AI-assisted development tools thoughtfully to move quickly while keeping strong judgment about quality, security, and maintainability.
What you bring
- 10+ years of professional software engineering experience, including significant time building and shipping production applications with Staff-level scope and impact.
- Strong full-stack engineering skills, including modern TypeScript or JavaScript, component-based front-end frameworks, and back-end services, APIs, and integrations.
- Meaningful experience working directly with customers, partners, or systems integrators in a technical capacity where you also wrote the code. Forward deployed, embedded, consulting, and implementation engineering work all count.
- Experience building or supporting AI-enabled applications, such as LLM platforms, RAG systems, agents, enterprise search, or data connectors.
- Solid understanding of enterprise deployment concerns, including authentication and identity providers, access control, self-hosted and hybrid infrastructure, observability, and reliability.
- Judgment about when to build something custom, when to generalize it into the product, and when to push back on a request.
- Clear communication skills with both technical implementers and non-technical stakeholders, including being honest about what the product can and can’t do today.
- Strong collaboration skills, including experience working closely with product managers, designers, engineers, and other cross-functional stakeholders.
- Comfort operating independently in an early-stage product where requirements, architecture, and processes are still taking shape.
Bonus points for
- Experience with self-hosted, private cloud, sovereign, or regulated deployment environments.
- Experience with Docker, Kubernetes, CI/CD, and cloud deployment.
- Experience working with European enterprise or public sector customers, or with data residency and sovereignty requirements.
- Experience supporting security reviews, privacy assessments, or enterprise procurement processes.
- Open-source contributions, or experience in open, highly collaborative engineering environments.
What success looks like
In your first few months, you’ll help land Thunderbolt’s first enterprise deployments. That means shipping the customizations those customers need, getting partner-hosted deployments stable, and fixing the issues that only show up in real environments.
Over time, success means each new deployment takes less custom work than the last. You’ll have turned one-off requests into reusable product capabilities, and partners will be able to handle routine issues without pulling in engineering.
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 Thunderbird Technologies Corporation. In this role, you’ll also collaborate closely with engineering, product, design, enterprise-facing teams, technical partners, contractors, and external contributors working on Thunderbolt.
This is a remote, full-time position. Many of Thunderbolt’s customers and partners are based in Europe, so some regular overlap with European business hours is expected. 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 - $220,000 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
Thunderbird 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. Thunderbird 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.
Salary Context
This $185K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. Disclosed range: $185K to $220K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
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: $220K - $220K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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