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About This Role
Hi, I’m Tim! I run Scaled Outbound at Ashby. We’ve spent the last year building the system: campaign architecture, the tooling stack, and the automated email and LinkedIn motion that reaches buyers with thoughtful messaging at a volume no human team can match.
Here’s where it breaks. The moment a campaign ships, my attention is already pulled to the next priority. So the work that makes a good campaign great isn’t happening with the level of care it deserves; refreshing and optimizing copy before it fatigues, finding creative new hooks by pulling in from the amazing content library and listening infrastructure, and partnering with other members of the team to create a complete ‘happy prospect > customer journey’. This role exists to close this gap. We’re looking for someone who is deeply curious, with great taste, and who gets energy from optimizing campaigns to perfection, institutionalizing learnings, and scaling the learnings we have from across the business into the most thoughtful, genuinely valuable, and high-converting campaigns. You can think of yourself as the owner of everything that happens after a Scaled Outbound campaign goes live.
What you’ll be responsible for in this role
Optimizing and scaling outbound campaigns. You will develop highly iterative experimentation methodology sprints to constantly improve the quality of existing campaigns and ensure we land the right message, at the right time, with the right contact.
Keep the automated motion from going stale. You’ll co-own the email and LinkedIn sequences with the Program Manager. Sequence refresh, new angles, and killing fatigue before it shows up in the numbers. A big part of success in this role is ensuring automated motion gets better over time instead of aging out.
Own copy optimization and conversion. You’re copy-obsessed. You can look at an underperforming sequence, understand why, and then fix it. This includes owning the Twain prompt library that shapes our 1:1 generated copy, tuning it so personalization at volume reads like a sharp human wrote it, not a machine.
Turn performance data into action. You’ll own QA and performance analysis for the automated motion and be able to quickly and decisively conclude what is and isn’t working, and then have the curiosity and first principle thinking to know how to redesign and optimize your way into conversion.
Collate and integrate materials and learnings from Sales, PMM, and Content. You’ll work directly with Sales Managers, Product Marketing, and Content to pull fresh proof points, launches, and data into active sequences, so the messaging always reflects our sharpest current story.
Co-own program-level physical touches. The programs will vary from ICP-tailored gifting (never airpods) to event invitations, and user group meetups.
The stack you’ll work in: Clay, Hubspot, Instantly, HeyReach, and Twain. You don’t need to have used all of these before, but you should be the kind of person who gets fluent in a new tool quickly.
You’ll Probably Love This Role If
- You have a creative experimental mindset, strong opinions about what good copy looks like, and no fear of testing unconventional approaches
- You’re energized by optimization, not just creation. You’d rather make a live campaign convert 20% better than write a brand-new one from scratch
- You’re copy and customer obsessed, you take time to understand the buyer and can diagnose why a sequence is underperforming, then act on it
- You read campaign performance data fluently and turn it into the next play
- You’ve run cold outbound sequences before (email or LinkedIn)
- You like owning something horizontal that raises the performance of every campaign the team builds
The background we’re looking for
There are two paths into this role and we’re open to both:
- Marketing side. Demand gen, growth, or lifecycle/outbound marketing at a B2B SaaS company, where you personally wrote and optimized cold sequences rather than briefing them.
- Sales side. You've spent multiple years in outbound sales, starting as a BDR/SDR. If you moved into an AE role, you kept running your own outbound. You built your own lists, wrote your own sequences, and sourced a meaningful share of your pipeline from cold outbound at real volume.
Either way, what we’re actually looking for is the same:
- You’ve written cold email and/or LinkedIn sequences and been measured on what they produced
- You’ve worked in a sequencing or sales engagement tool (Instantly, Smartlead, HeyReach, or similar) and can read performance data without waiting on an analyst/ops
- You reach for AI to compress your own work, whether that’s research, first drafts, pattern-finding in performance data, or building something small to save yourself time
- You care about pipeline, not MQLs
This role is not for you if
- You want to primarily write long-form or brand content.
- You think a live campaign on auto-enroll is done
- You prefer a locked playbook. A lot of this is defining what “great” looks like from first principles
- You’re uncomfortable being measured on the performance of a system you didn’t build from the ground up
- You get overwhelmed by the detailed work of optimizing (sequence refresh, localization, reporting, QA, swapping content)
- You’d rather own a single campaign end-to-end than raise the floor on all of them
- Your outbound experience is agency-side or brand-side rather than in-house at a B2B SaaS company
- You've briefed copy but haven't personally written and shipped cold sequences
- You need an analyst to tell you what the campaign data says
Interview Plan
Our interview process is:
- 30 minutes intro call with Michelle (Recruiter Screen)
- 45 minutes interview with Tim McCann (Hiring Manager)
- 30 minutes interview with Harriet (VP Marketing)
- 30 min take home review
+ Tim, Harriet, Sam
- Final panel:
+ 30 minutes with Claire (Demand Gen) + 30 minutes with Sam (SMB Sales Leader) + 30 minutes with Lia (Product Marketing Leader)
Benefits
- Competitive salary and equity.
- Opportunity to work with a talented and passionate team.
- 10-year exercise window for stock options. You shouldn’t feel pressure to purchase stock options if you leave Ashby —do it when you feel financially comfortable.
- Unlimited PTO with four weeks recommended per year. Expect “Vacation?” in our one-on-one agenda until you start taking it .
- Twelve weeks of fully paid family leave in the US. We plan to expand this to employees in other countries as situations arise.
- Generous equipment, software, and office furniture budget. Get what you need to be happy and productive!
- $100/month education budget with more expensive items (like conferences) covered with manager approval.
- If you’re in the US, top-notch health insurance for you and your dependents with all premiums covered by us and a 401k match.
Ashby’s success hinges on hiring great people and creating an environment where we can be happy, feel challenged, and do our best work. We’re being deliberate about building that environment from the ground up. I hope that excites you enough to apply.
Ashby provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.
Ashby provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.
Ashby is committed to a fair and transparent hiring process. We confirm that this advertisement is for an active, existing vacancy within our organization. Please be advised that we may use artificial intelligence-driven tools to assist our recruitment team in screening, assessing, and selecting candidates for this position.
Compensation Range: $120K - $165K
Salary Context
This $120K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At ASHBY, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($142K) sits 33% below the category median. Disclosed range: $120K to $165K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
ASHBY AI Hiring
ASHBY has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $165K - $165K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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