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About Firework
Join Firework – Where Innovation Meets Impact
Firework is redefining the future of commerce as an AI and video commerce company — combining cutting\-edge technology, an exclusive network of enterprise brands and retailers, and a first\-mover position to win the agentic commerce race.
We've built the world's most advanced and largest video commerce platform, trusted by global brands and leading retailers. But we're more than software — our compounding network effect grows stronger with every partner we add, bringing the energy of in\-store experiences online and transforming how businesses engage, convert, and build lasting customer relationships at scale.
Having raised over $235M to date, led by investors such as SoftBank Vision Fund 2, and operating at global scale, we offer unparalleled opportunities to solve complex challenges and drive meaningful impact in the future of connected commerce.
If you're curious, ambitious, and energized by big ideas — Firework is the place to grow, lead, and shape what comes next. Together.
Summary
Want to build with AI tools before most companies even have a team for it? As our AI Content Intern, you’ll sit right at the intersection of R\&D, Creative Services, and Product—getting hands\-on with the newest generative AI tech and the in\-house agents our team is building, and using them to create real content that ships. You won’t just watch AI reshape content creation from the sidelines; you’ll help launch it, working alongside the team to bring new AI capabilities to life for real brands. This role is open to current seniors and recent graduates, and can be structured as a paid internship or for work\-study credit—with the potential to turn into a full\-time offer for the right person.
### What you’ll be doing
- Partner closely with R\&D, Creative Services, and Product to explore and apply the latest AI content\-generation techniques to Firework’s video commerce platform
- Get hands\-on creating content using Firework’s in\-house AI tools and agents, iterating quickly to see what works and sharing what you learn
- Support AI feature launches by working with Creative Services, Product, and GTM teams to make sure creative assets and workflows are ready for release
- Assist translating R\&D prototypes into production\-ready creative workflows, surfacing issues and gathering feedback from creative and success teams
- Take on cross\-functional coordination between the teams as needed as new AI capabilities roll out
- Document learnings, workflows, and best practices as Firework’s AI creative tooling evolves
### We’ll be excited if you have
- Senior standing in an undergraduate program or a recent graduate, in a relevant field such as computer science, design, media, or marketing
- Fluency in both Mandarin and English, spoken and written—you’ll work across crossborder creative and R\&D teams
- Genuine curiosity about generative AI and how it’s reshaping content creation, and comfort experimenting with new AI tools and agents
- Some hands\-on experience with content creation—video, design, or copy—through coursework, personal projects, or prior internships
- Excellent communication skills and the ability to work cross\-functionally with engineering, creative, and go\-to\-market teams
- Comfort operating in a fast\-moving, ambiguous environment where priorities can shift as new AI capabilities evolve
Location
The role is hybrid in our San Mateo office
Compensation
The following represents the expected range of compensation for this role: The estimated pay range for this role is $19\-$25/hour. We can also consider work study credits in lieu of pay. Other factors that impact compensation include stock options. The role has the potential to turn into a full\-time offer for the right person, visa sponsorship included.
The posted pay range represents the anticipated low and high end of the compensation for this position and is subject to change based on business need. To determine a successful candidate’s starting pay, we carefully consider a variety of factors, including primary work location, an evaluation of the candidate’s skills and experience, market demands, and internal parity. Candidates may receive more information from the talent partner.
Don’t hold back
We understand some candidates may see the above and not apply because they don’t meet all the qualifications. We encourage you to apply anyway; we often find talented candidates that fit many other opportunities we have and look for potential too, not just what you did in the past. As an equal employment opportunity employer, we are a diverse team that strives for an inclusive environment for all. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, age, disability, genetic information, pregnancy, or any other protected characteristic as outlined by federal, state, or local laws.
Salary Context
This $39K-$52K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Firework, 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 in Demand for This Role
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. Entry-level AI roles across all categories have a median of $120,000. This role's midpoint ($45K) sits 79% below the category median. Disclosed range: $39K to $52K.
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.
Firework AI Hiring
Firework has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Mateo, CA, US. Compensation range: $52K - $52K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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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