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About This Role
At Torrid we're committed to cultivating a welcoming, inclusive and diverse culture driven by a focus on open dialogue, empowerment, recruiting, training, development and retention. We believe inclusion of diverse backgrounds and perspectives is fundamental to our success.
The Digital Content Producer \- Email is primarily responsible for managing content production workflow and delivering email marketing content. This position will work primarily within our Email Platform (Cordial) to build and schedule daily email experiences, including complex segmentation and personalized campaigns. The producer works closely with cross\-functional teams, project managers, and stakeholders to conceive and implement overall content updates.
What You’ll Do:
Segmentation \& Personalization* Build highly targeted audience segments using Cordial data (purchase behavior, site activity, real\-time signals)
- Automate dynamic personalization using logic and tokens (vs. static templates)
Campaign Execution \& Delivery* Execute daily email campaigns in partnership with marketing teams
- Optimize and prepare creative for deployment, including slicing and testing assets
- Build and manage triggered, event\-based journeys
- Ensure timely launches aligned to project milestones; communicate risks or delays
Quality Assurance \& Technical Oversight* Ensure accuracy, QA, and proper deployment across all campaigns
- Troubleshoot and resolve email production, rendering, and delivery issues
- Monitor marketing automation systems for performance and stability
Performance \& Optimization* Track engagement using Cordial analytics and reporting
- Conduct A/B testing to refine content and targeting strategies
- Continuously optimize campaigns based on performance insights
Data \& Cross\-Functional Collaboration* Partner on data feed integrations into/out of the ESP to support communication goals
- Collaborate with cross\-functional teams to ensure alignment with brand standards and best practices
- Leverage knowledge of database marketing, CRM, and segmentation
Execution Excellence* Manage multiple projects simultaneously in a fast\-paced environment
- Prioritize effectively while maintaining strong attention to detail
What You’ll Need:* Bachelor's Degree or an equivalent combination of education and work\-related experience required.
- 3 years’ experience in email and digital operations, with hands\-on production experience including HTML coding, automated customer journeys, A/B testing and audience queries. Experience using Cordial is a plus.
- Proficient in CMS and Ecommerce platforms.
- Skilled in digital content development across various platforms, integrations, and channel marketing.
- Team player with a can\-do attitude, and with the ability to work independently or collaboratively with multiple people and/or teams.
- Strong verbal and written communication skills.
- Provide honest, direct, and constructive feedback.
- Must be available to work primarily on Pacific Time hours to meet the needs of the business.
What You'll Get:
- A culture where people are accepted and encouraged to be who they are.
- Competitive compensation, 401k with company matching contribution, plus potential to earn company performance\-based bonuses.
- Comprehensive wellness package including, medical, dental, vision, and Flexible Spending Account
- Generous 50% employee discount and access to employee\-only sales.
- Support the causes you’re passionate about. We pay you up to 32 hours annually for volunteering your time in the community.
- Tuition reimbursement program
- Employee Assistance Program (EAP) \- Aimed at helping employees address a variety of personal and family issues including legal financial consultations, mental health services and more.
- Discounts on cell phones, and computer purchases, entertainment tickets and more.
- Pet insurance for your fur babies.
- Work and learn alongside industry\-leading executives while making huge strides in impacting the lives of women.
- You’ll be challenged and grow. Opportunity for upward mobility is available at all levels of the organization.
Salary range: $78,000 – $93,500
We are a proud Equal Opportunity Employer and will not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status or any other protected status. If you are unable or limited in your ability to use or access www.torridcareers.com you can request reasonable accommodations by sending an email to accommodations@torrid.com. Only messages left for this purpose will be returned. Our company participates in E\-Verify. If the links below do not work, please copy and paste the following URLs in a new browser window:
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Salary Context
This $78K-$93K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Torrid, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($85K) sits 49% below the category median. Disclosed range: $78K to $93K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Torrid AI Hiring
Torrid has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $93K - $93K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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