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About This Role
The Company
We’re Tallwave, a growth partner that helps organizations turn disconnected growth efforts into connected growth systems. We’re a curious, ambitious team of strategic thinkers and creative problem solvers who understand our clients’ worlds deeply, challenge assumptions, and work across disciplines to solve the constraints holding growth back. We move with speed and discipline, embrace innovation, and hold ourselves accountable for meaningful outcomes. Our ambitions are too big to leave room for individual egos, so we value ownership, humility, selflessness, and the belief that our greatest success comes from what we can accomplish together.
The Person
Tallwave seeks out individuals who are a cut above. Their exceptional skill, diverse perspectives, obsession for winning together, and relentless pursuit of continuous self-improvement, both professionally and personally, give them an innate ability to lead, innovate, and inspire. They have well-honed philosophies and points of view in their areas of expertise, they think unconventionally, and their endless curiosity pushes them to constantly explore, learn, improve, and try new things.
The Job
We’re looking for an AI Systems Configurator who is excited about the potential of AI to change how businesses operate, solve problems, and deliver value. This person will help make Tallwave an AI-native firm by identifying high-impact opportunities across delivery, operations, and growth, then turning those opportunities into practical, connected systems and workflows that improve how we work.
This is a role for a curious, strategic, hands-on problem solver who understands that technology is a means to a business outcome. You’ll learn how Tallwave operates, connect our business strategy to opportunities for AI, challenge the way work gets done today, and experiment with new approaches to make us more effective. You’ll build alongside delivery and operations teams, helping people adopt new ways of working rather than simply introducing new tools.
Success is measured in business results: lower cost to deliver each program, earlier visibility into client risk and expansion, more reliable business intelligence, and more revenue per person. You don't need to be a traditional software engineer, but you do need the technical fluency, systems thinking, and resourcefulness to turn ideas into solutions that people actually use.
The Skills
The successful candidate will:
- Understand Tallwave’s business strategy and identify opportunities to use AI to improve how we sell, deliver, and operate—not just automate existing tasks
- Map how delivery, operations, marketing, and account management work today, and redesign those workflows around connected systems and agents
- Help build and maintain Tallwave’s company database, creating a reliable, connected foundation for information about our clients, engagements, delivery, and business performance
- Build and maintain agentic workflows across our stack, including Asana, HubSpot, Claude, Granola, Google Workspace, Slack, Vercel, and Collective 54 Co-founder, using MCP connections, APIs, and automation platforms
- Automate status reporting, hours against estimation sheets, meeting capture, intake, and leadership rollups so the business gets consistent visibility without anyone chasing it
- Surface expansion signals and early signs of client friction to account management, and client wins to marketing
- Own and continuously improve Tallwave's AI tooling: prompts, skills, context, accuracy, and keeping it current
- Partner with project management, operations, and program owners as collaborators, testing every build on live client work before it rolls out
- Bring builds and new opportunities to a quarterly AI review with leadership and program owners, demonstrating impact and helping prioritize what comes next
- Help teams adopt AI-native ways of working through practical training focused on how the work gets done rather than how a tool works
- Measure impact in business terms, including hours returned, cost to deliver per program, adoption, and retire what people do not use
- Uphold Tallwave's AI guardrails, including conducting human review of anything client-facing and abiding by our data retention rules data/AI clauses in client contracts before building on client data
The Requirements
The ideal candidate will have:
- A background in operations, solutions engineering, RevOps or GTM engineering, technical program management, or another role where you translated business needs into systems and solutions other people relied on every day
- 5+ years of relevant experience, including hands-on experience building AI or automation workflows used in production
- Demonstrated success turning ambiguous problems or opportunities into practical solutions that teams adopted and kept using
- Strong strategic thinking and business acumen, with the ability to understand business goals, evaluate tradeoffs, and identify where AI can deliver the greatest impact
- Genuine curiosity about AI and a track record of experimenting with emerging tools, learning quickly, and finding creative ways to apply new capabilities
- Hands-on fluency with Claude or similar LLMs, Claude Code, MCP, APIs, and automation platforms such as Zapier, Make, n8n, or Asana AI Studio
- A track record of getting teams to adopt what you build without formal authority
- Excellence in clear written communication, systems documentation, and working with non-technical teams
- Professional services or agency experience is a plus
The Culture and Perks of Being a Tallwaver:
- We embrace a maverick attitude. We’re willing to challenge convention and think beyond the ask.
- We are committed to relentless pursuit. We’re curious with a focus on personal growth.
- We value selfless teamwork, demonstrating strong leadership qualities with a knack for collaboration.
- We are thoughtfully rigorous, with a strong work ethic and strong attention to detail.
- We foster an environment of empathy and compassion for each other and our clients.
- We are a diverse (and competitive, and hilarious, and sometimes a little crazy) group of coworkers. You know, the kind you actually want to grab a drink with after work.
- We offer career growth and opportunity. We grow leaders here.
- We recognize the contributions of individuals and teams throughout Tallwave and we stop to celebrate along the way.
We are surrounded by badass talent and clients * - our work has been featured in TechCrunch, Forbes, Wired, Product Hunt, and more.
- We’ve been featured on the INC Best Workplaces List, the Phoenix Business Journal Best Places to Work List, and the AZCentral Top Places to Work List.
Benefits
- We offer comprehensive Medical, Dental, Vision, Life, and 401(k) plans.
- We provide employer-paid short-term disability, long-term disability plans and life insurance.
- We’re more concerned about how we work than where we work. We offer remote work and office hours flexibility as needed.
- We trust our team and have a generous PTO policy.
Why You’ll Love Working with Us
Free to be you each and every day! We celebrate diversity and empower you to bring your authentic self to work, always. We know that diversity, equity, and inclusion is not about checking a box. It’s more than words; it is a pledge and a promise that Tallwave is passionate about pursuing. We recognize having an all-encompassing culture will help us learn and grow collectively and will continue to foster our mission of making everyone we work with wildly successful.
Physical Demand:
This job may require sitting for long periods of time, and minimal (if any) lifting. Some travel may be required.
Working Conditions
All employees are expected to operate in a professional home office environment and routinely use standard office equipment like computers, telephones, and teleconferencing equipment.
Position Applicants, please note the following:
At this time, this position is only open to applicants who are legally authorized to work in the United States and who are living in the United States. We are unable to provide visa sponsorship of any kind, so please do not apply if:
- You are not legally authorized to work in the United States, or
- You are not living in the 48 contiguous U.S. states, Alaska, or Hawai'i, or
- You will require any kind of visa sponsorship.
Tallwave is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.
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Salary Context
This $110K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Tallwave, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($135K) sits 36% below the category median. Disclosed range: $110K to $160K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Tallwave AI Hiring
Tallwave has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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