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About This Role
About OpenSesame
While it appears to most people that we just sell training courses (over 50,000 of them), what we really offer is the opportunity for companies to upgrade the skills of each of their employees and reinvent their workforce in an AI world.
We have strategic partnerships with 150\+ Global 2000 companies who rely on our training programs to develop the world's most productive and admired workforces. Now we are building what comes next.
About the Team
Our Workforce Reinvention team is at the forefront of driving efficiency and innovation across OpenSesame. We operate in an agile, continuously improving environment focused on optimizing workflows and empowering internal teams through practical automation and AI\-driven solutions.
The team partners closely with non\-technical business groups including Sales, Marketing, and Finance to build scalable systems that reduce operational toil and accelerate productivity across the enterprise.
About the Role
The Workforce Reinvention – AI Engineer will help scale modular tools, intelligent workflows, and production\-grade Retrieval\-Augmented Generation (RAG) systems that support internal business operations. This person will serve as a pragmatic builder who consistently selects the most efficient solution for each challenge, whether through system integrations, lightweight scripting, reusable automation components, or custom AI agents.
The role will focus initially on maturing existing AI proofs\-of\-concept into reliable production systems while establishing engineering rigor through Test\-Driven Development (TDD), CI/CD automation, infrastructure\-as\-code practices, and operational monitoring.
Success in this role will require strong partnership with internal "AI Champions" across departments to create reusable automation capabilities that empower teams to safely self\-serve and scale intelligent workflows across OpenSesame.
Performance Objectives
30 Days — Onboarding, Context, and First Contribution
- Develop a comprehensive understanding of the current Workforce Reinvention architecture, codebase, deployment pipeline, and existing AI proofs\-of\-concept while building strong working relationships with AI Champions in Sales, Marketing, and Finance.
- Within the first month, successfully contribute and deploy a small but meaningful workflow improvement, automation script, or enhancement to an active internal tool while demonstrating the ability to operate within the team's TDD and CI/CD practices and release processes.
60 Days — Production Launch \& Component Foundations
- Take ownership of deploying a production\-grade RAG workflow or intelligent internal tool built from a previously validated concept, ensuring the solution is stable, maintainable, and valuable to internal stakeholders.
- Partner closely with AI Champions to identify and prioritize workflow bottlenecks while beginning to establish reusable modular automation blocks and standardized development patterns that form the foundation of a scalable enterprise component library.
90 Days — Engineering Rigor \& Pipeline Optimization
- Lead improvements to the CI/CD pipeline that significantly reduce manual deployment steps, improve release reliability, and accelerate software delivery across automation initiatives.
- Begin implementing infrastructure\-as\-code practices using tools such as Terraform while introducing robust monitoring, logging, and observability standards that improve system reliability, track operational costs, and support a target uptime of 99\.9% for critical internal automation APIs and workflows.
6 Months — The Scalable Automation Ecosystem
- Fully establish and maintain a reusable component library that enables rapid deployment of AI\-driven and traditional automation workflows across multiple business units while empowering AI Champions to safely self\-serve and scale departmental solutions independently.
- Create a sustainable operational model where engineering maintains stable infrastructure, automated testing eliminates operational toil, documentation supports non\-technical users, and intelligent workflows become a dependable force multiplier across the organization.
What Success Looks Like
- Successfully turns experimental AI ideas into scalable production systems that improve operational efficiency across OpenSesame.
- Builds reliable, maintainable automation frameworks that internal teams can confidently use and expand on.
- Becomes a trusted technical partner to AI Champions and helps create a scalable automation ecosystem that increases productivity across the company.
You might notice we don't list a traditional set of requirements or buzzwords here. That's intentional.
We're looking for proven examples from your career that show you can build brands, create scalable systems, and drive measurable marketing impact. When you look back a year from now, you'll know you've elevated OpenSesame's brand and strengthened its market presence.
Location: This position can be based anywhere in the US. We operate as a remote\-first company and invest in all\-company in\-person meetings several times a year.
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 salary for this role range between $150,000 \- $170,000 per year, 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. We carefully consider a wide range of compensation factors, relying on market data to determine compensation and consider your specific job family, background, skills, and experience. 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.
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 [email protected]. Your security matters to us — thank you for staying vigilant
Salary Context
This $150K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2088 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 4,021 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $180,000 based on 12,397 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $163,400. This role's midpoint ($160K) sits 11% below the category median. Disclosed range: $150K to $170K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
OpenSesame AI Hiring
OpenSesame has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $170K - $170K.
Location Context
AI roles in Austin pay a median of $218,800 across 509 tracked positions. That's 9% 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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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