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About This Role
Aboutthird
Third is a modern-commerce growth company helping brands turn new and emerging platforms into real sales channels.
Our integrated acquisition strategy connects best-in-class teams across TikTok, Whatnot, Meta, Snap, and beyond - eliminating the need to stitch together multiple partners.
Third blends deep niche expertise with modern execution to launch, operate, and scale commerce on new platforms, transforming culture, creators, and content into real product sales.
The Role
We're looking for a Data & AI Engineer to build the technical foundation of Third's Creative Engine, an AI-powered platform that connects creative performance, creator data, and commerce insights.
You'll own the data architecture, AI integrations, and engineering systems that power the platform, building everything from scalable data pipelines to LLM-powered video analysis and search capabilities. This is a hands-on, high-impact role with significant technical ownership and the opportunity to shape an emerging product from the ground up.
What You'll Do
Data Architecture & Engineering
- Design and build scalable data pipelines that connect performance, sales, engagement, and creator data across TikTok, Meta, and other platforms.
- Develop robust data models, databases, and backend infrastructure using Python, Django, and PostgreSQL.
- Integrate third-party APIs and manage complex data processing challenges, including rate limits, authentication, and asynchronous workflows.
AI & Machine Learning Integration
- Build LLM-powered workflows for video analysis, creative tagging, and AI-generated insights using technologies such as Gemini and Claude.
- Develop semantic search and retrieval capabilities that transform complex data into actionable creative intelligence.
- Optimize AI systems for accuracy, reliability, scalability, and cost efficiency.
Product & Full-Stack Development
- Build tools that connect creative performance, creator history, and engagement data into a centralized intelligence platform.
- Develop end-to-end features, including dashboards, search interfaces, content libraries, and reporting tools.
- Partner with product and creative teams to translate business needs into scalable technical solutions.
Technical Ownership & Execution
- Own engineering solutions from architecture and development through deployment, monitoring, and optimization.
- Leverage AI-assisted development tools to accelerate coding, testing, and delivery.
- Contribute to technical standards, architectural decisions, and continuous improvements as the platform scales.
What We're Looking For
- 5+ years of experience building production applications, with a strong focus on data-intensive systems and backend engineering.
- Advanced proficiency in Python, Django, PostgreSQL, and SQL.
- Experience designing scalable data models, pipelines, and asynchronous processing systems using Celery, Redis, or similar technologies.
- Hands-on experience integrating LLM APIs into production, including prompt engineering, structured outputs, and cost management.
- Experience working with third-party APIs, including authentication, rate limits, and complex data structures.
- Proficiency with AI-assisted development tools such as Claude Code or similar platforms.
- Ability to independently build and deploy end-to-end features, including backend and frontend components.
- Strong problem-solving, communication, and independent decision-making skills.
Nice to Have: Experience with RAG, pgvector, AI agents, video processing, AWS, or ad tech and creator economy platforms.
Our Tech Stack: Python, Django, PostgreSQL, Celery, Redis, HTMX, Tailwind CSS, Gemini, Claude, Docker, AWS, and GitHub Actions.
Who You Are
- You're a builder who enjoys solving complex problems and shipping practical solutions.
- You're passionate about AI and excited to explore new technologies and development approaches.
- You thrive with autonomy and are comfortable making technical decisions in an evolving environment.
- You're collaborative, resourceful, and motivated by creating scalable systems that deliver real business value.
Why This Role Matters
Third is building the intelligence layer behind its creative operations. As our Data & AI Engineer, you'll develop the technology that transforms disconnected data into meaningful insights, helping our teams make faster, smarter, and more effective creative decisions.
Location
United States, Remote
Compensation
- Competitive salary + performance bonus eligible
- Comprehensive health, dental, and vision coverage
- Flexible PTO
- Remote-first work environment with periodic team gatherings
- Access to an industry-leading network across social commerce, creator, and brand marketing
Third is an equal opportunity employer. We are committed to building a diverse team and an inclusive environment where all employees feel valued and empowered
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Salary Context
This $140K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At THIRD, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $140K to $170K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
THIRD AI Hiring
THIRD has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $170K - $170K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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