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About This Role
AI Operations Lead
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*This is a fully remote opportunity and can be worked from any location in the United States.*
About Us
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1mind is building the next generation of revenue teams.
We are creating a new category of enterprise software: Autonomous Customer Experience. Our platform deploys Superhumans, go\-to\-market teammates with a face, a voice, and a GTM brain that serve the buyer across the entire journey with one continuous memory. They engage, demo, onboard, and support in the moments no company could ever staff, so the handoffs disappear and growth stops leaking.
Join us as we define the category and build the platform that powers the next generation of revenue teams. When the buyer wins, the company wins. Every1 Wins.
About the Role
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At 1mind, we believe AI should transform the way people work, not just for our customers, but for our own teams too.
As our AI Operations Lead, you’ll help define what an AI\-native company looks like from the inside out. You’ll partner with teams to identify high\-impact opportunities, build intelligent workflows and operational systems, and scale what works across the business. You’ll begin by partnering closely with Sales, then expand your impact across go\-to\-market and the broader organization as you shape how teams collaborate, make decisions, and operate at scale.
You’re a strategic operator first and a builder second. You know how to identify high\-impact opportunities, turn ideas into practical AI\-powered solutions, and build systems that scale with the business.
What You'll Do
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- Map operational workflows across the business, beginning with Sales and expanding across the broader organization, to identify friction, measure impact, and build better ways of working
- Partner closely with Revenue Operations to identify high\-impact opportunities for AI, designing and deploying intelligent workflows, automations, and operational playbooks that can scale across go\-to\-market and the broader business
- Drive adoption by creating documentation, guardrails, and training that ensure the systems you build are embraced, trusted, and built to last
- Partner with Engineering when solutions require custom development, translating business needs into scalable technical solutions and owning successful delivery
- Scale successful AI playbooks beyond go\-to\-market, helping build the operational foundation of an AI\-native company
What You'll Bring
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- 5\+ years of experience in an operations, systems, or technical business role, with a track record of building AI\-powered workflows, automations, or internal tools that measurably improved how teams operate
- Comfortable working with modern AI platforms, APIs, and automation tools. You don’t need to be a software engineer, but you enjoy writing code, connecting systems, and bringing ideas to life
- Working knowledge of Python and APIs, with the ability to prototype solutions and collaborate effectively with Engineering
- Strong strategic judgment and a track record of solving high\-leverage business problems
- An operator’s mindset. Curious, resourceful, highly autonomous, and energized by building systems that eliminate friction
- Proven ability to influence cross\-functional teams and drive adoption of new tools and workflows
- Thrive in early\-stage environments where ownership is high, ambiguity is constant, and every day looks a little different
Why Join Us?
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- We're completely transforming how companies go to market, scaling revenue beyond human capacity
- Help define a new category and build the operational engine from the ground up
- Work alongside a top\-tier, high\-ownership team during a rapid growth phase
- High visibility, high impact role, a company\-wide mandate with room to grow as we scale
Compensation
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The salary range for this position is $150,000 to $165,000. The compensation package for this position also includes stock options and company\-paid benefits.
1mind's total compensation package is designed to be competitive and includes base salary, equity, and a full range of benefits and perks. Final compensation will depend on factors such as your skills, experience, qualifications, and location, and will be determined during the interview process. The hiring manager will share more details about the full compensation package and benefits as you move through the process.
\[Please note that all legitimate communication from 1mind will come only from email addresses ending in @1mind.com. We will never ask for payment, financial information, or personal details outside of our official application process. If you receive a suspicious message, please disregard it and alert us at careers@1mind.com]
*We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, notetaking, or summarizing responses. These tools assist our recruitment team but do not replace human judgment \- all hiring decisions are made by people. If you would like more information about how your data is processed or prefer to opt out of any AI\-assisted tools, please let your recruiter know. Opting out will not impact your experience or consideration.*
Salary Context
This $150K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At 1Mind, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($157K) sits 28% below the category median. Disclosed range: $150K to $165K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
1Mind AI Hiring
1Mind has 2 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Based in US. Compensation range: $165K - $220K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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