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About This Role
About OpenSesame
OpenSesame is the trusted partner for workforce reinvention in the age of AI. We deliver integrated software, curated and customizable content, and expert services embedded into existing learning, HR, and work systems to help organizations expand their human \+ AI potential and thrive through change.
About the Role
Our growth marketing team is the engine behind pipeline, and we're looking for someone who sits at the intersection of campaign operations and AI to help us create better experiences for our prospects and customers, faster and more effectively.
This role owns the execution layer: you are the owner of our email channel – operations, performance, deliverability, and compliance – alongside campaign builds, database health, and the workflows that connect it all. You'll use AI to streamline execution, personalize interactions, and build workflows the whole team benefits from, making every touchpoint more relevant and timely at scale. Your work ensures a seamless experience for prospects, reliable data to inform future decisions, and full activation of our growing database.
This role is ideal for someone who geeks out on how marketing operations actually works and gets genuinely excited about what AI makes possible. You're already hands\-on with tools like Claude, ChatGPT, and HubSpot Breeze, and you have opinions about them. You care about the experience on the other end of every email you send, you're energized by building systems that scale, and you thrive in a team that moves fast and values impact. You'll work with best\-in\-class tools including HubSpot, Salesforce, Qualified, Clay, and Asana – and you'll bring the curiosity and drive to push what's possible with all of them.
Performance Objectives
30 Days — Ramp Up \& Foundations
Goal: Learn the systems, understand the team's work, and start contributing to execution.
- Complete onboarding and gain access to all systems (HubSpot, SFDC, Asana, Claude, Qualified, ChatGPT, etc.)
- Meet key stakeholders across growth marketing, RevOps, sales, and GTM ops; join campaign status meetings and begin contributing
- Review current email governance standards, segmentation logic, compliance requirements (GDPR, CAN\-SPAM), and baseline email performance metrics
- Shadow and support 2–3 live campaigns across email, webinar, and event channels
- Audit HubSpot — workflow organization, campaign folder hygiene, and AI features currently activated vs. available (including Breeze)
- Review lead lifecycle stages, scoring model, UTM tracking framework, and campaign reporting structure
- Deliverables:
- + Baseline email health report covering deliverability, list quality, and performance trends
+ HubSpot AI audit — summary of features available vs. in use, with top 3 recommendations
+ List of recommended workflow or automation improvements based on initial audit
+ Propose one AI use case for campaign ops or QA efficiency to test in the next 60 days
60 Days — Ownership \& Execution
Goal: Take ownership of recurring processes, execute independently, and begin applying AI in your workflow.
- Independently own the build, QA, and launch of 3\+ campaigns across email, webinar, event, or partner channels
- Own email operations end\-to\-end — segmentation, sends, deliverability monitoring, compliance, and performance reporting
- Map and document current campaign ops processes — intake, build, QA, and launch — for at least two recurring campaign types
- Build and launch your first agent
- Draft a structured intake checklist and Asana template for campaign requests
- Contribute optimization ideas based on email performance data — segmentation, timing, subject lines, CTAs
- Deliverables:
- + 3\+ campaigns launched independently with clean attribution
+ Email performance report with optimization recommendations
+ Documented campaign ops process for at least two campaign types
+ First agent live in HubSpot. Claude or other tooling, with a brief summary of what it does and the time it saves
90 Days — Impact \& Growth
Goal: Demonstrate full autonomy, improve systems meaningfully, and deliver something that moves a metric.
- Own the email channel end\-to\-end — including a defined testing roadmap and ongoing performance optimization
- Build and launch at least one AI\-powered nurture sequence with improved personalization or segmentation tied to a specific funnel stage or ICP segment
- Lead a campaign retrospective using a HubSpot dashboard you've built — share findings and recommendations with the marketing team
- Contribute at least one documented, scalable workflow to the team's shared AI playbook
- Partner with relevant stakeholders to finalize and socialize an org\-wide campaign intake and ops process
- Deliverables:
- + Documented email testing roadmap and performance recap
+ AI\-powered nurture sequence live, with engagement results vs. baseline
+ HubSpot\-based campaign report shared with the marketing team
+ One documented AI workflow added to the shared team playbook
You might notice we don't have the typical list of requirements and buzzwords here. That's intentional.
We're looking for proven examples from your career that show you can do this job — that you've built systems, driven alignment, and created impact at scale. When you look back a year from now, you'll know you've made OpenSesame better, faster, and stronger because of your leadership.
Although it should go without saying (but it doesn't), OpenSesame is an equal opportunity employer and we strive to create a welcoming, inclusive environment that celebrates diversity.
Location: This position can be based anywhere in the US. We operate as a remote\-first company, and invest in mandatory all\-company meetings several times a year in addition to required team travel as necessary.
Performance Driven: We're looking for self\-starters with a track record of delivering excellent results, but we're highly selective about who we hire. We don't focus on typical job requirements, instead, we're interested in specific examples from your past experiences. All positions can be based anywhere in the US, and require up to 15 days of travel per year, with senior management and leadership teams requiring up to 35 days.
Compensation: The base salary for this position generally ranges between $100,000 and $115,000, depending on experience. At OpenSesame, we offer a comprehensive benefits package to employees upon hire, including professional development, ISOs, health insurance, 401(k) matching, and paid time off.
Equal Employment Opportunity: OpenSesame is an Equal Employment Opportunity and Affirmative Action employer that values and welcomes diversity. We do not discriminate on the basis of various legally protected characteristics, including criminal history, and strive to provide reasonable accommodations to qualified individuals with disabilities. We prioritize safety and security and may use your information accordingly, and you can contact us for assistance or accommodations during the job application process.
Pay Transparency: At OpenSesame, we prioritize pay transparency, fairness, and equity to create a positive and inclusive work environment, regularly reviewing our compensation practices to align with our values and goals. We provide competitive and fair compensation to our employees based on their skills, experience, and performance.
CPRA (California Candidates): When you submit your application, OpenSesame may collect and use your personal information in accordance with our privacy policy and the CPRA. This may include personal details and employment history, and will only be used for employment\-related purposes. We may share this information with third\-party service providers, but we will not sell it to third parties. If you have any questions or concerns, please contact us, and for more information on your rights under the CPRA, refer to our privacy policy or the California Attorney General's website.
We Care About Your Security: We've been made aware of a phishing scam involving individuals impersonating OpenSesame recruiters. All legitimate communication from our team will come from @opensesame.com email addresses. If you receive a suspicious message, please contact us directly at careers@opensesame.com. Your security matters to us, thank you for staying vigilant.
Salary Context
This $100K-$115K range is above 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At OpenSesame, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($107K) sits 36% below the category median. Disclosed range: $100K to $115K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
OpenSesame AI Hiring
OpenSesame has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Phoenix, AZ, US. Compensation range: $115K - $115K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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