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About This Role
About Seekeasy
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Seekeasy helps restaurants and hospitality businesses unlock revenue growth with creator\-led demand intelligence. We turn social media signals into actionable insights on trends, customer demand, and marketing opportunities and help brands activate on these insights with creator marketing campaigns through Seekeasy’s managed creator marketplace. As a funded seed\-stage startup and a veteran team, we're on a mission to leverage AI to deliver the tools to bring creators and restaurants together to ensure restaurants are actively part of the social media conversation.
What You'll Do
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Build our AI intelligence platform: Design, implement, and continuously improve AI\-powered workflows that transform structured and unstructured data into actionable business intelligence. Build systems that enable AI to perform increasingly sophisticated analytical work while maintaining quality and reliability.
Build our analytics infrastructure: Develop scalable data pipelines, dashboards, reporting systems, and analytical models that power product decisions, customer insights, restaurant intelligence, and strategic partnerships.
Turn data into competitive advantage: Analyze millions of AI\-generated restaurant attributes and social media signals to identify emerging consumer trends, marketplace opportunities, product insights, and business recommendations that influence company strategy.
Shape how we use AI: Develop and refine AI workflows, prompt engineering strategies, evaluation frameworks, and automation systems that continuously increase the speed, quality, and scale of analytical work across the company.
Ensure our intelligence platform is working: Continuously improve the quality, accuracy, and reliability of our data and AI outputs. Monitor data quality, identify anomalies, troubleshoot issues, and optimize systems for scalability and performance.
Lead and mentor: Help establish best practices for AI\-assisted analysis, automation, and data\-driven decision making. Set the standards for the team of people and AIs we will eventually have.
Who You Are
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You are an AI multiplier: You don't simply use AI. You instinctively think about how to teach AI to perform the work you do so you can continuously increase your impact. You automate first and repeat yourself as little as possible.
You are a builder: You love to build, ship, get feedback, and iterate because that's the only way to know what's working and what isn't.
You are an owner: You take pride in your work and can operate autonomously to drive your work forward. Seekeasy is not the right fit for people who default to waiting for instructions.
You are a team player: You see yourself as part of the team, not just as someone working on the same project. Whatever it takes for the team to succeed, you want to be a part of it.
You are insight obsessed: You love finding patterns hidden inside messy data and turning them into decisions that create meaningful business impact.
Your Qualifications
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- Proficient with Python, SQL, AWS services, Git, relational databases, and modern data platforms.
- Proficient with AI platforms, APIs, and frameworks including large language models, prompt engineering, retrieval augmented generation (RAG), vector search, AI agents, Model Context Protocol (MCP), LangGraph, LangChain, or similar technologies.
- Experience building scalable data pipelines, analytical models, dashboards, and reporting systems using modern data engineering and analytics best practices.
- Experience working with search technologies such as Elasticsearch, OpenSearch, or similar search and indexing platforms.
- Passionate about AI and its potential to transform how businesses operate. You don't just use AI to be more productive. You think about how to train AI to become a trusted analytical teammate that scales your expertise.
- Strong analytical and statistical problem\-solving skills with the ability to translate data into actionable business recommendations.
- A problem solver who thrives in the ambiguity and fast pace of startup environments.
- A seasoned AI, data, or analytics professional with 6\+ years of experience in Analytics Engineering, Data Engineering, Data Science, Machine Learning, Business Intelligence, or a related technical discipline, with a track record of building scalable analytical systems.
- Live in the San Francisco Bay Area and can commute to our office in San Francisco.
- Previous startup experience.
Compensation and Benefits
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- $150,000 \- 200,000/year
- Generous equity grant – be a true owner in the company you're building.
- Comprehensive health, dental, and vision insurance \- we cover 100% for employees and 80% for dependents.
- Flexible vacation policy – we trust you to manage your time and bring your best.
Seekeasy is committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or any other legally\-protected status. We are committed to providing reasonable accommodations for candidates with disabilities who need assistance during the hiring process \- please contact jobs@seekeasy.ai to request an accommodation.
Seekeasy will consider qualified applicants with criminal histories in a manner consistent with applicable law.
Salary Context
This $150K-$200K 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 Seekeasy, 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 ($175K) sits 20% below the category median. Disclosed range: $150K to $200K.
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.
Seekeasy AI Hiring
Seekeasy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $200K - $200K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% 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 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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