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About This Role
We are looking for 1 remote employee in one of 7 states (CA, TX, VA, NY, IL, CO, NC), or 1 hybrid employee located in Richmond, VA.
Who We Are
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Workshop Digital is a results\-oriented digital marketing agency based in Richmond, Virginia, committed to building strong, lasting partnerships with our clients. For over a decade, we’ve championed transparency and collaboration to deliver measurable results that help businesses grow.
We act as an extension of our clients’ marketing teams, developing customized digital marketing strategies aligned with their unique goals and services.
Our team of experienced digital marketing analysts works side\-by\-side with clients to gain a deeper understanding of their business, industry, and competitors. By identifying ideal target audiences and uncovering what sets our clients apart, we help their brands stand out and achieve meaningful, measurable impact.
Position Summary
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The Senior Paid Media Manager continues to develop their skills with some support from the Director of Paid Media as they take on medium \& large\-sized clients. They continue to:
- Develop strategies and tactics that support client goals and drive strong paid digital marketing performance
- Build and maintain stronger, deeper client relationships with day\-to\-day contacts to ensure high client retention and growth
- Troubleshoot minor to complicated client issues
- Implement and test new capabilities to enable client growth
In addition, a Senior Paid Media Manager may be asked to train new hires on basic concepts and lead internal account teams.
Responsibilities
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Overview
- Develop, execute, manage and report on paid media strategies that support client goals and drive strong paid media marketing performance
- Build and maintain strong, deep client relationships with day\-to\-day contacts and senior stakeholders to ensure high client retention and growth
- Strategically build, manage, and optimize client paid digital marketing accounts across Google Ads, Google Display Network, YouTube, Microsoft Ads, Meta Ads, LinkedIn Ads, Programmatic, and other paid digital marketing channels
- Actively manage and seek ways to improve account performance
- Generate new quarterly content encompassing blog posts, webinars, internal presentations, or other multimedia formats.
- Enhance client relationships through a comprehensive understanding of their strategic growth objectives, ensuring long\-term client retention
- Create a test hypothesis and implement landing page optimization tests to improve conversion rates
- Lead junior analysts on joint accounts
- Help create and execute educational initiatives for the paid media team, such as developing internal resources and training sessions
- Provide support for client services deliverables during the sales process for upsells and new business
Client Communication and Relationships
- Develop strong relationships with clients and identify senior stakeholders
- Attend and participate in all meetings and calls with clients
- Communicate effectively with various audiences, with support for some clients
- Create and present insightful, meaningful, accurate reports and analyses with minimal support
- Execute the Workshop Digital client flag process and proactively identify resolutions
- Go the extra mile for assigned clients within the scope of the SOW
Internal Communication and Relationships
- Proactively seek and gracefully accept feedback
- Tactfully share feedback with colleagues
- Immediately inform the Director of Paid Media of any concerns or issues from clients when appropriate
- Inform the Director of Paid Media of any updates, concerns, or potential points of interest from within the digital marketing community
- May participate in the interview process
- Support special projects
Business Development
- Identify new opportunities for clients to meet their business goals
- Identify opportunities for potential case studies and coordinate with marketing for case study development, with some management support
- Manage beta tests of new services for the client set
- Elevate client requests for SOW changes with some support
Training
- Provide feedback on training content
- Develop basic training content with QA
- Occasionally, conduct basic team trainings to help develop team expertise
- Read industry blogs, books, whitepapers, and articles to stay current on relevant tactics, updates, and best practices
- Occasionally contribute thought leadership content to the Workshop Digital blog and the industry
Channel Management
- Manage paid digital marketing accounts across Google Ads, Google Display Network, YouTube, and other paid digital marketing channels with minimal support
- Strategically build, manage, and optimize client\-paid digital marketing accounts with minimal support
- Perform keyword and audience research to optimize accounts and influence creative decisions with minimal support
- Actively manage and seek ways to improve account performance and diversify client budgets to maximize results with minimal support
- Create and prioritize strategies and supporting tactics that embody clients’ business goals with minimal support
- Meet and exceed client expectations and set KPIs with minimal support
- Set up and review Google Analytics (and any other third\-party tracking software) with minimal support
- Identify potential threats, changes in landscape, industry updates, and potential opportunities for client accounts, and present these to clients with minimal support
- Conduct ongoing tests that drive towards growth and achieving client goals with minimal support
- Temporarily take on extra client hours to support the team during times of limited bandwidth
Qualifications
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- 4\+ years of demonstrated agency experience with clients, with respect to:
- + Building strong relationships
+ Prioritizing workload with some support
+ Creating insightful, actionable, and accurate insights for client strategies and reports with some support
+ Presenting data and insights in a way that is logical, clear, and actionable, with some support
+ Creating effective meeting agendas and leading client meetings with some support
+ Delivering basic training
+ Logging assigned client hours worked
+ Meeting deadlines
- Proven track record of managing a full client workload consisting of medium and large clients
- Courteous written, verbal, and visual communication skills
- Strong analytical capabilities, but may need support for advanced analysis and insights
- Occasionally provides basic training to other team members
- Proactively expand basic knowledge base by doing solo research/learning, asking peers to shadow, asking for support projects, etc.
- Understands and supports leadership vision/decisions
- May identify and/or support the implementation of initiatives that push the team forward
- Intermediate\-level skills in Microsoft Office and Google Workspace, particularly in Excel and Google Sheets
Benefits
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- Competitive salaries
- 25 days PTO, 5 days STO, 12 Holidays
- Hybrid workspace for Richmonders (only Mondays are required)
- 3 months fully paid Parental Leave
- 8 hours of paid volunteer time per quarter
- 401k with 3% non\-elective contributions
- A comprehensive health benefit package, including 2 medical plans, dental, and vision insurance
- Life insurance, short\-term, and long\-term disability
- Profit sharing
- 2x a year company\-wide meetup in Richmond, VA
- Committee Membership (Community, Culture, Health \& Wellness)
List ofAwards
- 1x US Search Awards \- Best Small PPC Agency
- 8x Outside Best Places to Work
- 8x Virginia Business Best Places to Work
- 3x Search Engine Land Finalist
Salary
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- Expected base salary range is $80,000 \- $87,000 annually, based on experience
Equal Opportunity \& Inclusion Statement
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*As an equity\-affirming company, Workshop Digital searches for, encourages, and engages with individuals and groups that represent diversity in our community. We not only challenge discrimination in regard to gender identity and expression, sexual orientation, racial identity, faith identity, age, disability, and social class, but we also consciously make efforts to reach out and include diversity in employment, vendors, and clients. All individuals authorized to work for any employer in the U.S. should apply.*
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 Workshop Digital, 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.
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.
Workshop Digital AI Hiring
Workshop Digital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US.
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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