Interested in this AI/ML Engineer role at Gemology?
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About This Role
While our main office in Orange County, we have experienced growth into Los Angeles and Valencia and we are looking for a strong AI Implementation Engineer to join the team and dominate that territory for us. You will have some work at home but will need to be available to drive to these locations when necessary to be on the work sites.
We are a rapidly growing AI start-up that's helping small and mid-sized businesses adopt AI in practical, meaningful ways. This isn't about flashy demos or theoretical use cases. It's about teaching clients how to use AI confidently while building automations that save them real time and solve real business problems.
If you're the type of person who pays for with Claude Pro, ChatGPT Pro, Cursor, Codex, or other AI tools because you genuinely are curious and enjoy experimenting—not because your employer requires it—you'll probably really enjoy this opportunity. We're looking for someone who's naturally curious, enjoys building things, loves learning new technology, and gets excited about helping others succeed. This role combines AI consulting, implementation, customer success, education, and business development.
We offer, a comprehensive compensation package including but not limited to:
- $80,000–$90,000 base
- Performance-based bonuses AND commission opportunities
- Medical, dental, and vision benefits
- Unlimited PTO
- Several Paid holidays
- Hybrid work environment (some work at home, some work at client locations in Valencia and Downtown Los Angeles)
- Opportunity to join a growing, entrepreneurial team at the forefront of AI consulting
What You'll Do
- Work directly with business owners and leadership teams to help them adopt AI across their organizations.
- Design and build AI-powered workflows and automations using modern AI tools, APIs, and automation platforms.
- Teach non-technical users how to confidently use AI in their day-to-day work.
- Identify opportunities to streamline manual processes and improve operational efficiency.
- Stay current on emerging AI tools, models, and best practices.
- Build relationships through networking, referrals, and community involvement while contributing to business growth
What we're looking for: The strongest candidates don't just use AI—they're constantly experimenting with it. You probably:
- Use AI every day in your personal and professional life.
- Pay for premium AI tools because you're passionate about learning and staying ahead.
- Build personal AI projects simply because you're curious.
- Enjoy figuring out better ways to solve problems.
- Can explain technical concepts to non-technical audiences.
- Thrive in fast-paced environments where you'll wear multiple hats.
- Enjoy meeting new people and building relationships.
- Recent graduates with exceptional AI experience are encouraged to apply.
Bonus Points
Experience with any of the following:
- Claude Code
- ChatGPT Pro / Codex
- Cursor
- MCPs (Model Context Protocol)
- n8n
- APIs & Webhooks
- AI Agents
- Workflow Automation
- Prompt Engineering
- Low-code / No-code automation platforms
Interview Process
We're much more interested in how you think than what's listed on your resume.
Be prepared to discuss:
- The coolest thing you've built with AI that nobody asked you to build.
- Your daily AI workflow.
- How you evaluate new AI models and tools.
- A project you've built from start to finish.
- How you would explain AI concepts to someone with little or no technical background.
Pay: $85,000.00 - $120,000.00 per year
Benefits:
- 401(k) matching
- Dental insurance
- Flexible schedule
- Health insurance
- Vision insurance
Work Location: Hybrid remote in San Fernando Valley, CA 91325
Salary Context
This $85K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Gemology, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($102K) sits 52% below the category median. Disclosed range: $85K to $120K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Gemology AI Hiring
Gemology has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Fernando Valley, CA, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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.
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