Interested in this AI/ML Engineer role at ABSORB?
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"The Marketing team is poised for unprecedented growth and opportunity. We're in a very unique position where we've experienced tremendous success but are far from exhausting the whitespace around us. There is a unique opportunity to join a rising organization and make a significant and immediate impact." Mark Lynch - Senior Director, Demand Generation
Absorb is building a new AI-enabled demand generation channel to reach buyers our existing programs do not. The channel does not exist today. We are looking for someone to build it from the ground up: identifying the right audiences and signals, standing up the initial programs, developing the outreach, running the experiments, and turning what works into a repeatable source of incremental pipeline.
This is a hands-on senior individual-contributor role with no direct reports. You will have budget and latitude in how the channel is built, and you will work closely with our Marketing AI & Analytics team, which owns the more technical parts of the stack. In the first year, much of the work will be yours to do directly. You will own the strategy and the commercial results.
What you’ll do:
- Build the channel from the ground up: audiences, signals, messaging, offers, sequencing and the supporting tooling.
- Partner with our AI and analytics team to define and develop the data, scoring and automation capabilities the program needs.
- Evaluate external platforms and determine where to buy, build or combine the two.
- Develop and test the outreach directly: messaging, offers, channels and cadence.
- Run a fast experimentation loop, scaling what works and retiring what does not.
- Determine where AI should automate, augment or remain out of the process.
- Measure incremental pipeline and commercial impact rigorously, in partnership with our analytics team.
- Maintain deliverability, brand, privacy and compliance standards for the program.
- Establish how the program works with Sales, including the handoff of engaged prospects.
What you’ll bring:
- Experience building a B2B demand or growth channel from nothing — as a founding marketer, early growth hire, founder or in a small agency — with results you can point to.
- Hands-on experience with modern GTM tooling: contact and intent data, enrichment, automation and AI-enabled outbound platforms. You are comfortable setting these up yourself.
- Strong B2B messaging instincts and a track record of outreach that generates engagement from senior HR, L&D or operations buyers.
- A track record of using experimentation, segmentation and data to improve performance, and the analytical judgment to distinguish activity from genuine commercial impact.
- Enough technical fluency to be largely self-sufficient — light SQL, APIs, no-code automation — and to work effectively with data and AI teams.
- Working knowledge of email deliverability, privacy and compliance considerations relevant to outbound marketing.
- Comfort with ambiguity and limited process, and a bias toward shipping and learning over extended planning.
Are you ready to become an Absorber?
What we offer:
- Fully remote-first work with flexible work arrangements
- Comprehensive Health and Wellness Benefits including retirement savings programs, eligibility for two different bonus plans, generous time off, comprehensive medical and dental benefits based on your country of location
- New Hire Equipment Allowance and monthly Flex Allowance to support your success
- Endless opportunity for career growth and internal mobility
- Employee driven DE&I programs
Who are we?
Absorb Software is a remote-first company that provides online training solutions to leading organizations around the world. Absorb is a cloud-based learning management system (LMS) engineered to inspire learning and fuel business productivity. Our online learning platform combines forward-thinking technology built to scale as our customer’s organizations grow. We empower learners to enrich their lives, workplaces and communities.
Our values are simple:
- We achieve exceptional results by genuinely caring about each other and the work we do
- We’re united, and we grow through our commitment to elevating continual learning!
Absorb is proud to be an equal opportunity employer, we celebrate diversity and are committed to creating a safe and inclusive environment for all our people. All employment decisions are based on business needs, job requirements and individual qualifications. In the event a current Absorb employee would like to apply for this role they will inform their supervisor prior to submitting their application. Successful candidates for this position will be subject to pre-employment background screening, including a criminal record check and must be able to show proof of legal eligibility to work in the country they have applied to without sponsorship.
Should you require any accommodation during the recruitment process, please indicate this on your application and we will work with you to meet your accessibility needs. For any questions, please contact us at [email protected]
We use AI tools to support our recruitment process, such as organizing applications and highlighting potential matches based on role requirements. While these tools help streamline our review, all screening and final hiring decisions are made by our recruitment team. We encourage candidates to apply authentically and not rely solely on AI-generated responses in their applications.
#LI-REMOTE
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 ABSORB, 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. Senior-level AI roles across all categories have a median of $228,700.
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.
ABSORB AI Hiring
ABSORB has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tampa, FL, US.
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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