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About This Role
Company: Omada.ai
Compensation: $40\-$50
Schedule: Part\-Time (20\-30 hrs/week)
Type: 1099 Independent Contractor
Location: United States or Canada (Remote)
About Omada
Omada.ai is an AI\-powered marketing platform purpose\-built for small businesses. Backed by HubSpot Ventures and Crosslink Capital, we’re on a mission to give every local business access to the kind of sophisticated marketing that used to be reserved for companies with big budgets and big teams. Our field sales organization is scaling rapidly, and we’re building something that looks more like a movement than a traditional sales team.
The Role
We’re looking for a Field Sales Trainer to own the weekly onboarding and ongoing pitch\-practice training for our growing network of Territory Partners (TPs)—independent field sales reps who sell Omada directly to local businesses. You’ll be the person who turns new hires into confident, polished reps and keeps experienced reps sharp. This is a high\-impact, high\-visibility role that sits at the center of our field operations.
What You'll Do
- Design and deliver weekly onboarding sessions for new Territory Partners, covering the Omada product, value proposition, objection handling, and field workflow
- Run live pitch\-practice sessions (role plays, recorded walkthroughs, group critiques) so reps get real reps before they’re in the field
- Build and iterate on training curriculum, playbooks, and reference materials that scale with the organization
- Partner with the VP of Field Operations and Director of Field Activation to align training content with current GTM priorities and field feedback
- Evaluate rep readiness and provide structured feedback to field leadership on individual and cohort performance
- Champion Omada’s culture and values in every training touchpoint—we’re building a movement, not just a sales team
- Identify patterns in rep struggles and proactively develop targeted skill\-building modules
- Maintain a library of best\-practice recordings, talk tracks, and one\-pagers for ongoing self\-serve learning
Who You Are
- 3\+ years of experience in sales training, sales enablement, or a player\-coach field sales role
- You’ve trained reps in a high\-velocity, SMB or door\-to\-door/field sales environment—
- bonus if it was a 1099 or independent contractor model
- Excellent facilitation skills: you can command a Zoom room, give direct feedback, and make training sessions something people actually look forward to
- Strong content creation instincts—you can turn a messy call recording into a teachable moment or a one\-page quick\-reference guide
- Comfortable with ambiguity and a fast\-moving startup environment; you build the plane while flying it
- Energized by helping people get better at what they do
- Familiarity with CRM tools, async video platforms (e.g., Hireflix, Loom), and virtual training best practices
Nice to Have
- Experience training reps on an AI or SaaS product sold to small businesses
- Background in behavioral science, adult learning theory, or instructional design
- Experience working with a distributed, remote\-first field sales organization
- Bilingual (English/Spanish) is a plus given our market footprint
Pay: $40\.00 \- $50\.00 per hour
Work Location: Remote
Salary Context
This $83K-$104K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 10,872 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Omada, 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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000. This role's midpoint ($93K) sits 39% below the category median. Disclosed range: $83K to $104K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Omada AI Hiring
Omada has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $104K - $104K.
Location Context
AI roles in Austin pay a median of $212,800 across 405 tracked positions. That's 12% above the national 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 10,872 open positions tracked in our dataset. By seniority: 871 entry-level, 6,773 mid-level, 2,013 senior, and 1,215 leadership roles (Director, VP, C-Level). Remote roles make up 11% of the market (1,212 positions). The remaining 9,626 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 10,872 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (9,872), AI Software Engineer (262), Data Scientist (256). 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 (871) are outnumbered by mid-level (6,773) and senior (2,013) 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 1,215 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 11% of all AI roles (1,212 positions), with 9,626 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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 (1,782 postings), Aws (1,065 postings), Azure (796 postings), Rag (710 postings), Gcp (617 postings), Pytorch (602 postings), Prompt Engineering (516 postings), Tensorflow (511 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.
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