Interested in this AI/ML Engineer role at Rocket55?
Apply Now →About This Role
Description:
If you’re looking for a fast\-paced, growth\-focused environment to flex your digital marketing muscle, Rocket55 is a perfect fit. Rocket55 has been a full\-service digital marketing agency for 16\+ years, no small feat in an industry that changes and evolves daily. Keeping up with the technological innovations and market trends in our industry makes every day working here an adventure. We’re looking for fellow hard\-working digital enthusiasts who are eager to drive results for our clients, thrive in a diverse and dynamic workplace, and add to a culture of curiosity, innovation, and awesomeness. Join our energetic, success\-driven team and be part of a company that values relentless pursuit of results, curiosity, collaboration, and adaptability. If you are driven by excellence and hungry for success, we want to hear from you!
Job Summary
Rocket55’s Search Engine Optimization (SEO/GEO/AEO) strategist is responsible for organic search performance across various search platforms for select clients. This position works closely with clients to determine the most impactful strategies and oversees the execution of the tactics that will contribute to their business goals. The SEO \& AI Search (GEO) Strategist deeply understands traditional search engine algorithms, has experience in generative AI platforms, and is familiar with social media platforms such as TikTok and Instagram.
This position is for a problem\-solver, who effectively communicates challenges, opportunities and solutions to the internal Rocket55 team and our clients. We’re looking for someone who is confident yet curious, experienced yet always learning, and passionate about delivering exceptional client outcomes. Your enthusiasm will help fuel outstanding results.
You will report to the Account Director of one of our vertical industries, contributing to both client success and the continuous development of Rocket55’s SXO services.
### Responsibilities
- Strategic Leadership on Client Accounts:
+ Develop high\-level SEO/GEO/AEO strategy based on comprehensive analysis of site performance, the search landscape, audience and industry trends, and client business goals.
- Ownership of 10\+ clients
+ Lead strategic planning initiatives in partnership with client success managers and strategy leads, ensuring alignment with client business goals.
+ Create comprehensive organic digital marketing strategies, incorporating multi\-channel approaches (e.g., traditional search engines, social media, AI platforms, third\-party websites) to drive organic growth.
- Execution:
+ Oversee traditional SEO strategies to improve visibility on Google and other traditional search engines, with a strong focus on both local and broad\-based search.
+ Conduct regular keyword research, direct on\-page optimization elements, and monitor algorithm updates to ensure continuous SEO improvements.
+ Identify and provide next steps to solve technical SEO challenges presented by an array of content management systems.
+ Design and implement strategies to influence the output of large language model\-based systems
+ Identify and optimize for social search trends, particularly on platforms like TikTok and Instagram
- Data Analysis:
+ Utilize analytics tools such as GA4 to track and assess SEO strategy effectiveness, conducting regular performance reviews and identifying areas for improvement.
+ Generate performance reports, analyzing organic search metrics across platforms, and presenting key insights to clients and internal teams monthly, quarterly and annually.
- Collaboration:
+ Coordinate with cross\-functional teams (content creators, web developers, etc.) to ensure the successful execution of organic tactics.
+ Collaborate with content and social media teams to ensure brand visibility across social media search channels.
+ Assist with the management project timelines, deadlines, and deliverables, ensuring that tasks are completed according to client expectations and quality standards.
- Learning:
+ Continuously monitor search trends, algorithm updates, AI advancements, and social media search behaviors to keep SEO/GEO/AEO ahead of the curve.
+ Stay informed about advancements in generative AI and adapt strategies to align with the evolving capabilities of these platforms.
+ Open to attending industry webinars, workshops, and conferences, sharing insights with clients and the internal team.
+ Help with the continuous development of new SXO tactics and deliverables that help support client success.
Requirements:
- Proven SEO/GEO/AEO and content strategy experience, with a strong background in developing and executing strategies.
- Deep understanding of SEO and how it contributes to achieving business goals.
- Experience conducting keyword research, technical optimizations and content strategy, to inform strategic decisions.
- Exceptional written and verbal communication skills, with experience delivering client\-facing presentations, workshops, and pitches.
- Successful track record of collaborating with cross\-functional teams, contributors, and stakeholders at various levels of seniority.
- Ability to manage multiple projects and priorities simultaneously, working independently with minimal direction.
Preferred Qualifications:
- Work in Minneapolis office 3 days per week
- Experience working in an agency setting is preferred.
Salary Range:
$70,000 \- $92,000 \- depending on experience and qualifications.
Total Compensation Package:
This position offers full\-time employment along with a comprehensive benefits package that includes:
- Complimentary parking
- Health, dental, and vision insurance
- 401(k) with matching contributions from the Company
- Life insurance and Accidental Death \& Dismemberment (AD\&D) coverage
- Company\-provided Short Term Disability insurance
- Long Term Disability coverage
- Paid Time Off
- Holiday pay, including Floating Holiday
- Flexible work schedules
Commitment to Inclusion:
Rocket55 is an equal opportunity employer and is dedicated to creating an inclusive work environment void of harassment and discrimination. Our goal at Rocket55 is to cultivate a culture where each team member feels appreciated, empowered, and motivated to pursue both personal and collective objectives. This commitment entails ensuring opportunity and accessibility for individuals of all backgrounds, including but not limited to race, ethnicity, age, marital status, gender, sexual orientation, gender identity, gender expression, religion, national origin, disabilities, political affiliation, and socioeconomic status.
Note: This job description is intended to provide a general overview of the position and does not encompass all responsibilities and qualifications that may be required. The role may evolve based on the needs of the company.
Salary Context
This $70K-$92K 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 Rocket55, 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 ($81K) sits 62% below the category median. Disclosed range: $70K to $92K.
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.
Rocket55 AI Hiring
Rocket55 has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US. Compensation range: $92K - $92K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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