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About This Role
The Role
Wpromote is looking for a sharp Paid Social Senior Manager with deep expertise in TikTok Shop to manage scalable, full\-funnel paid social and commerce programs across a portfolio of clients. This is a hands\-on role: you will build integrated media \+ commerce strategy, execute high\-quality activations, optimize to business KPIs, and partner cross\-functionally to position TikTok Shop as a measurable revenue\-driving channel.
This role requires someone who understands not just TikTok Ads Manager—but the TikTok Shop ecosystem, including Seller Center operations, affiliate/influencer commerce, product merchandising, and promotional levers that drive in\-platform conversion.
We’re looking for someone who’s dedicated to cultivating curiosity, operates with a learning mindset, and is passionate about driving measurable growth while having fun along the way. If we could describe an ideal candidate for this role, words like analytical, strategic, proactive, collaborative, commerce\-minded, and conversion\-focused come to mind..
This is a 1099 freelance role with an expected commitment of approximately 40 hours per week. The engagement is expected to run 3 months with the potential to extend based on business need.
At Wpromote, we believe that great work is only possible with great people. Our goal is to build a better, more inclusive work environment and support our people at every stage of their careers by prioritizing a strong work\-life balance through our policies and benefits listed below. As a Best Place to Work according to both Ad Age and Glassdoor and Adweek’s Fastest Growing Digital Agency, we are moving fast to expand our teams and bring new experts into the fold to keep pushing the boundaries of what’s possible in marketing.
The anticipated hourly rate for this role will be $40\.00/hr \- $50\.00/hr based on a variety of factors unique to each candidate, including skill set, years and depth of experience, education and certifications, competitive benchmarks, scope of responsibility, market dynamics, geographic location, and the respective state’s salary threshold for exempt employees. At Wpromote, pay ranges are subject to change and are based on specific market medians for similar jobs according to third\-party salary benchmark surveys. Individual pay within that range can vary due to skills, experience, and available budget.
- This role may be performed remotely in most U.S. states, with some exclusions.
\*\*This position is not eligible for immigration sponsorship
\*\*\* This position is a contract, 1099 freelance role
\#LI\-JJ
\#LI\-Remote
### You Will Be
- Leading TikTok Shop growth strategy, including traffic\-driving media, affiliate strategy, promotional planning, and conversion optimization within the Shop ecosystem.
- Formulating full\-funnel paid social and TikTok Shop strategy that ties media and commerce decisions to client business objectives (revenue growth, MER/ROAS, CPA, AOV, LTV, new customer acquisition).
- Driving influencer/affiliate recruitment strategy in partnership with Influencer and Creator teams; activating and optimizing affiliate performance within TikTok Shop.
- Translating creative insights into testable commerce hypotheses and partnering with Creative leads to iterate on TikTok\-native, shop\-enabled creative formats (LIVE, Spark Ads, UGC, creator\-led assets).
- Informing teams on TikTok Shop best practices, promotional cadence planning, and operational requirements to unlock scale.
- Influencing day\-to\-day campaign optimizations across Meta, TikTok, Snap, Reddit, Pinterest, X, NextDoor and other relevant platforms — with advanced ownership of TikTok Shop and Seller Center workflows.
- Participating in cross\-functional pod workflows (Creative, Influencer, Data Strategy, Paid Search, Programmatic) to align TikTok Shop into holistic digital KPIs.
- Maintaining strong platform relationships with TikTok reps, advocating for beta access, LIVE shopping opportunities, and alpha commerce products.
- Applying strict QA, tagging, and tracking discipline to ensure delivery accuracy and commerce measurement integrity.
- Partnering with Data Strategy to evaluate incrementality, blended MER, and cross\-channel halo impact of TikTok Shop programs.
### You Must Have
- 5\+ years of paid media experience (paid social, paid search, and/or media buying) in an agency or client environment.
- Direct hands\-on experience managing TikTok Shop and Seller Center with strong operational fluency.
- Hands\-on experience building, optimizing, analyzing, reporting and application of experimental design across major paid social platforms (Meta, TikTok, Snap, Reddit, Pinterest, LinkedIn, X, NextDoor, Google).
- Familiarity with Google Analytics / GA4 (or Northbeam, Adobe, TripleWhale, etc.) and understanding of cross\-channel attribution.
- Detail orientation, systematic QA habits, and a bias toward documenting processes and results.
- Comfort working independently in a fast\-paced, deadline\-driven environment.
### Nice To Have
- Experience using Smartly
- Experience supporting TikTok LIVE shopping activations.
- Exposure to server\-side tracking (CAPI), enhanced conversions, and incrementality testing.
- Familiarity with marketplace/ecommerce platforms (Shopify, Amazon, etc.) and product feed management.
Wpromote is committed to bringing together individuals from different backgrounds and perspectives, providing employees with a safe and welcoming environment free of discrimination and harassment. We strive to create a diverse \& inclusive environment where everyone can thrive, feel a sense of belonging, and do impactful work together. As an equal opportunity employer, we prohibit any unlawful discrimination against a job applicant on the basis of their race, color, religion, gender, gender identity, gender expression, sexual orientation, national origin, family or parental status, disability\*, age, veteran status, or any other status protected by the laws or regulations in the locations where we operate. We respect the laws enforced by the EEOC and are dedicated to going above and beyond in fostering diversity across our workplace.
Applicants with disabilities may be entitled to reasonable accommodation under the terms of the Americans with Disabilities Act and certain state or local laws. A reasonable accommodation is a change in the way things are normally done which will ensure an equal employment opportunity without imposing undue hardship on Wpromote.
This employer participates in E\-Verify and will provide the federal government with your Form I\-9 information to confirm that you are authorized to work in the U.S. If E\-Verify cannot confirm that you are authorized to work, this employer is required to give you written instructions and an opportunity to contact Department of Homeland Security (DHS) or Social Security Administration (SSA) so you can begin to resolve the issue before the employer can take any action against you, including terminating your employment. Employers can only use E\-Verify once you have accepted a job offer and completed the Form I\-9\. For more information on E Verify, or if you believe that your employer has violated its E\-Verify responsibilities, please contact DHS.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $83K-$104K 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 Wpromote, LLC, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($93K) sits 44% below the category median. Disclosed range: $83K to $104K.
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.
Wpromote, LLC AI Hiring
Wpromote, LLC has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $100K - $104K.
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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