Interested in this AI/ML Engineer role at Sinclair Broadcast Group?
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About The Role
Sinclair is building a practical AI Product and Delivery function that makes AI tools and solutions useful in the real world. The team owns the employee-facing AI product experience and supports the AI product strategy: listening to what employees need, developing the right signals into a focused product portfolio, and moving priority work through execution, delivery, launch, and improvement.
The AI Product and Delivery Coordinator is a hands-on product and delivery partner to the Director, AI Product. You will help run the day-to-day product experience for ChatGPT Enterprise and other AI tools, keep the backlog and roadmap well informed, and help move priority work from a real employee or business problem to a delivered and supported solution.
This is an early-career role for someone who likes turning ambiguity into momentum. You will not be expected to write code or carry out technical administration. You will be expected to walk into conversations with people across the company, ask good questions, understand what matters, and help the group leave with a clear next step. We are looking for judgment, confidence, follow-through, and the ability to be a positive business partner as much as we are looking for a conventional product background.
What You Will Do
Employee Requests And Product Support
- Manage the day-to-day flow of employee questions, requests, access needs, change requests, and support signals for approved AI tools.
- Talk with employees to understand the need behind a request; provide timely guidance when a question can be resolved directly and route actionable work to the right administrative, support, or delivery partner.
- Keep requests moving and employees informed, making sure ownership, context, and next steps are clear across the teams involved.
- Identify patterns in employee needs, recurring friction, and requests that should become a product improvement, a release consideration, or a new product opportunity.
AI Tool Releases And Employee Education
- Help plan and coordinate the release of new features, functions, and AI tools, including the intended audience, timing, access and provisioning needs, product messaging, and learning materials.
- Create clear, useful employee-facing guidance that explains what is new, why it matters, and how to use it responsibly and effectively.
- Work with enablement, communications, training, and administrative partners to make releases understandable, well-supported, and ready for employees.
- Collect early feedback and issues after a release, then turn that input into improvements, updated guidance, or follow-up product work.
Product Discovery Backlog And Roadmap
- Help maintain the AI product backlog and portfolio, with clear opportunity records, status, owners, business context, dependencies, decisions, and expected outcomes.
- Support discovery with business partners and employees to understand users, workflows, pain points, constraints, and the value a solution needs to create.
- Turn problems, requests, ideas, and other signals into concise product briefs, requirements, user stories, workflow documentation, and recommended next steps.
- Prepare the product and workflow context that helps technology partners evaluate solution options and delivery paths.
- Support the Director in preparing roadmap recommendations and portfolio priority decisions with current business context, delivery information, and clear documentation.
Solution Delivery And Product Improvement
- Coordinate priority product work from approved concept through build, pilot, release, and ongoing support with internal and external delivery partners.
- Maintain delivery plans, milestones, dependencies, risks, decisions, and readiness activities; surface issues early and help keep the work moving.
- Translate approved product direction into clear documentation and requirements for technology, administrative, implementation, and business partners.
- Manage post-launch follow-through for selected solutions, including feedback collection, change needs, release-material updates, adoption signals, and enhancement backlog items.
- Synthesize product usage, employee feedback, requests, and delivery learnings into clear updates and practical recommendations for the Director and cross-functional partners.
How You Work
- Build productive working relationships with AI Technology Strategy, AI Operations and Enablement, IT, Engineering, Security, Legal, Communications, and business teams.
- Bring the right people together at the right time, recognize when specialized review or implementation is needed, and make sure each partner has the context to move the work forward.
- Be a thoughtful, credible representative of the AI Product team with everyone from frontline employees to executive leadership.
Who You Are
- Two to four years of experience in product operations, product management, project or program delivery, strategy and operations, consulting, transformation, or a comparable cross-functional role.
- A confident, clear communicator who can have a useful conversation with anyone in the organization, from a frontline employee to an executive leader.
- Strong business acumen and the ability to quickly understand a team's priorities, ask the right questions, and communicate a credible point of view.
- A natural instinct for getting work done: you can turn a conversation into a plan, keep commitments visible, and follow through without damaging relationships.
- Product instincts: you can understand users and workflows, distinguish a request from the underlying problem, write clearly, and think about adoption and success measures.
- Strong organization and comfort managing a high volume of requests, details, handoffs, and competing priorities without losing the human side of the work.
- A collaborative, positive working style. You know how to manage competing priorities, bring people along, and be direct without being difficult.
- Comfort learning new technology, including generative AI tools, and applying practical judgment without needing to be the technical implementer.
This position is on site in Hunt Valley, MD. Candidates must be local or willing to commute.
Please note that this position is not eligible for visa sponsorship, including employer sponsorship for an H-1B visa, OPT-STEM employment, etc.
Sinclair is proud to be an equal opportunity employer and a drug free workplace. Employment practices will not be influenced or affected by virtue of an applicant's or employee's race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, military or veteran status or any other characteristic protected by law.
About Sinclair:
Sinclair, Inc. (Nasdaq: SBGI) is a diversified media company and a leading provider of local news and sports. The Company owns, operates and/or provides services to 177 television stations in 79 markets affiliated with all major broadcast networks; owns Tennis Channel, the premium destination for tennis enthusiasts; and multicast networks CHARGE, Comet, ROAR and The Nest. Sinclair's AMP Media produces a growing portfolio of digital content and original podcasts. Additional information about Sinclair can be found at www.sbgi.net .
About the Team
The life-blood of our organization is our people. We have a compelling story, a goal-oriented culture, and we take really good care of people. How good? Here is a glimpse: great benefits, open-door policy, upward mobility and a strong desire to see you succeed. Ready to be part of a winning team? Let's talk.
The base salary compensation range for this role is $85,000 to $105,000. Final compensation for this role will be determined by various factors such as a candidates' relevant work experience, skills, certifications, and geographic location. Full time positions are eligible for benefits that include participation in a retirement plan, life and disability insurance, health, dental and vision plans, flexible spending accounts, sick leave, vacation time, personal time, parental leave and employee stock purchase plan.
Salary Context
This $85K-$105K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Sinclair Broadcast Group, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($95K) sits 55% below the category median. Disclosed range: $85K to $105K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Sinclair Broadcast Group AI Hiring
Sinclair Broadcast Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cockeysville, MD, US. Compensation range: $105K - $105K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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