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Position Title
Channel Manager, SEO and AI SearchLocation
Nationwide, MI 48098Job Summary
The Marketing Channel Manager owns the strategy, execution, and continuous optimization of an assigned marketing channel, ensuring it operates at best\-in\-class performance standards and contributes measurably to the organization's growth objectives. This role is accountable for channel\-level results, building audience, targeting, campaign architecture, and governance strategies that convert demand into measurable business growth.
The Marketing Channel Manager serves as the primary owner of their channel's performance, connecting audience intent, campaign execution, and conversion strategy to qualified leads, applications, funded accounts, and long\-term customer value. This role partners cross\-functionally with Analytics, Creative, Enablement, and Compliance to ensure the assigned channel operates with precision, scalability, and continuous improvement.Job Responsibilities:
JOB RESPONSIBILITIES
Channel Strategy and Performance Ownership
- Define and own the assigned channel strategy, including audience targeting, campaign structure, budget allocation, and performance priorities.
- Establish channel\-level KPIs and performance baselines, tracking results against defined targets on a regular cadence.
- Develop and maintain the channel roadmap, identifying capability improvements, automation opportunities, and investment optimizations.
- Stay current on channel developments, industry best practices, and competitive strategies, bringing relevant advancements into the organization's operating model.
- Contribute to the enterprise channel mix strategy, providing channel\-specific perspective on demand capture, audience sequencing, and cross\-channel integration.
Campaign Execution and Optimization
- Build and optimize audience targeting, segmentation, and channel\-specific campaign structures that align demand, product relevance, and business growth priorities.
- Develop campaign governance standards that manage eligibility, frequency, quality assurance, and customer experience across all channel activity.
- Partner with digital experience teams to improve post\-engagement conversion rates and reduce friction across the customer journey.
- Measure channel impact beyond surface\-level engagement metrics, connecting performance to applications, funded accounts, product usage, retention, and incremental growth.
- Monitor changes in channel behavior, audience patterns, competitive coverage, and performance trends to inform the optimization roadmap.
- Identify and prioritize tactical improvements that increase efficiency, conversion quality, and channel contribution to business outcomes.
- Establish governance practices that protect channel health, brand relevance, and long\-term performance effectiveness.
- Evaluate campaign structure, budget pacing, and targeting decisions to improve efficiency, scalability, and growth contribution.
Measurement, Reporting, and Testing
- Own channel\-level performance reporting, translating data into clear, actionable insights for marketing leadership.
- Partner with the Growth Marketing Enablement function to ensure tracking, tagging, and conversion measurement are accurate and complete for the channel.
- Build and execute a continuous testing agenda, including creative, audience, offer, and format experiments, using results to drive ongoing performance improvement.
- Maintain a learning log that captures test outcomes and ensures insights are applied to future campaign decisions.
Cross\-Functional Collaboration
- Partner with the Growth Marketing Enablement team on measurement architecture, audience activation, and automation initiatives relevant to the channel.
- Collaborate with Creative and Content teams to develop channel\-optimized assets that meet performance requirements.
- Work with Analytics to ensure channel data is accurately captured, reported, and connected to business outcomes.
- Partner with Legal and Compliance to ensure all channel activity meets regulatory requirements.
- Uses independent judgement and discretion to make decisions.
- Analyzes and resolves problems.
ADDITIONALACCOUNTABILITIES
- Performs special projects, and additional duties and responsibilities as required.
- Consistently adheres to regulatory and compliance policies and standards linked to the job as listed and complete required compliance trainings. Accountable to maintain compliance with applicable federal, state and local laws and regulations.
JOB REQUIREMENTS
Required Qualifications:
- Education level required: Undergraduate Degree (4 years or equivalent) in Marketing, Business, Analytics, or a related field.
- Minimum experience required: 6\+ Years of experience in performance marketing or digital marketing, with at least 3 years of dedicated hands\-on experience managing a defined marketing channel.
- Demonstrated track record of driving measurable performance improvement in an owned channel, with clear accountability for results.
- Strong analytical skills with the ability to translate channel data into optimization decisions and strategic recommendations.
- Experience working cross\-functionally with Creative, Analytics, and Technology teams to improve channel performance.
- Demonstrated experience managing channel\-specific tactics, including targeting strategy, campaign architecture, performance governance, and optimization programs.
- Strong understanding of audience strategy, conversion\-focused campaign execution, and performance measurement across the customer journey.
Preferred Qualifications:
- Financial services or highly regulated industry experience.
- Experience supporting acquisition, onboarding, engagement, retention, or cross\-sell programs with measurable connection to customer and business outcomes.
- Experience developing structured testing agendas across creative, audience, offer, messaging, and format variables.
Job Competencies:
- Possesses deep channel expertise with the ability to manage strategy, execution, and optimization with full ownership of results.
- Demonstrates strong analytical skills, with the ability to translate performance data into clear optimization decisions and channel strategy adjustments.
- Displays a continuous testing mindset, using structured experimentation to drive ongoing performance improvement.
- Exhibits strong cross\-functional collaboration skills, with the ability to work effectively across Creative, Analytics, Enablement, and Compliance.
- Demonstrates executional discipline, with the ability to manage complex campaign operations accurately and consistently.
- Possesses strong communication skills, with the ability to present channel performance and strategic recommendations clearly to marketing leadership.
- Demonstrates strong channel judgment, with the ability to connect audience strategy, campaign execution, and conversion pathways to measurable business outcomes.
- Displays strong governance discipline across targeting, campaign structure, budget management, and quality assurance.
- Demonstrates a strong ability to build and maintain effective relationships with stakeholders by communicating clearly, engaging in proactive collaboration, and leveraging cross functional insights. Aligns relationship building efforts with enterprise goals to accelerate performance and drive strategic results.
- Builds trusted client relationships, whether internal or external, by identifying needs and delivering tailored solutions to enhance the overall client experience.
- Fosters or supports a positive work culture and productive work environment, displaying importance of effective relationships with customers and stakeholders.
- Physical demands (ADA): No unusual physical exertion is involved.
Flagstar is an Equal Opportunity Employer
We are committed to providing clear and accurate compensation information in accordance with applicable laws. Actual starting base pay will be determined based on location, experience, and other non\-discriminatory factors permitted by law. Total compensation may also include variable incentives, bonuses, commissions, or other awards as outlined in the offer of employment. Flagstar provides teammates access to a variety of benefits including medical, dental, vision, life, and disability insurance, as well as a comprehensive leave program. Please click the following link for detailed information: Benefits \| Flagstar Bank
Pay Range
$94,536\.00 \- $161,600\.00Qualified applicants with arrest or conviction records will be considered for employment in accordance with the California Fair Chance Act, the Los Angeles County Fair Chance Ordinance, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, and the San Francisco Fair Chance Ordinance, as appliable.
Salary Context
This $94K-$161K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At FlagStar Bank, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($128K) sits 40% below the category median. Disclosed range: $94K to $161K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
FlagStar Bank AI Hiring
FlagStar Bank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $161K - $161K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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