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About This Role
Overview
We're a rapidly growing clinical toxicology lab and w need a smart, hands\-on operator who lives and breathes AI tools — and can translate that into real workflow change across sales, billing, clinical operations, and executive reporting.
This is not an IT position. This is not a prompt engineer. This is a builder — someone who can audit how our business actually runs, identify where time and money are being lost to manual process, and systematically replace that friction with AI\-powered workflows that the team actually uses. In addition, we have many forward\-thinking projects that are conceptual that require an AI operator to build out. You'll own the AI stack end\-to\-end: identifying tools, building integrations, training staff, and iterating. You'll work directly with leadership and have visibility into everything from revenue cycle management to clinical reporting to sales rep enablement. The projects we are building will revolution the industry.
Responsibilities
- Lead the design, development, and deployment of AI\-driven solutions tailored to organizational needs, ensuring alignment with strategic objectives.
- Develop comprehensive automation strategies that streamline workflows, reduce manual effort, and improve accuracy across departments.
- Collaborate with management consulting teams to identify process improvement opportunities and craft actionable plans for digital transformation.
- Manage change management initiatives by communicating effectively with stakeholders, facilitating training sessions, and ensuring smooth adoption of new technologies.
- Conduct thorough research and analysis to evaluate emerging AI tools, industry best practices, and innovative automation techniques.
- Support business development efforts by demonstrating the value of AI implementations to clients or internal stakeholders.
- Oversee project management activities related to automation initiatives, including planning, resource allocation, timeline tracking, and risk mitigation.
Requirements
- Proven experience deploying AI tools in a business setting — not experimenting, shipping
- Fluency with LLMs (Claude, GPT\-4, etc.), automation platforms (Zapier, Make, n8n), and CRM integrations (HubSpot preferred)
- Ability to think in systems: you see a process and immediately see where AI fits
- Strong communicator — you can explain what you built to a non\-technical COO and a billing specialist on the same day
- Self\-directed and comfortable with ambiguity; this role is what you make it
- Bonus: experience in healthcare, clinical labs, RCM, or billing environments
- Bonus: familiarity with data viz tools (Looker, Tableau, or equivalent)
- Proven experience in implementing AI solutions and developing automation strategies within complex organizational environments.
- Strong analysis skills with the ability to interpret data insights and translate them into actionable plans.
- Expertise in change management principles to facilitate organizational buy\-in and sustainment of new processes.
- Background in management consulting or business analysis to understand client needs and craft tailored solutions.
- Excellent strategic planning capabilities to align automation projects with broader business goals.
- Familiarity with process improvement methodologies such as Lean or Six Sigma is a plus.
- Effective research skills to stay current on technological advancements and industry trends relevant to AI and automation.
- Strong project management skills to coordinate multiple initiatives simultaneously while maintaining focus on deliverables. This role is ideal for innovative thinkers passionate about leveraging AI technology to revolutionize business operations. We are committed to supporting your growth as you lead transformative projects that make a tangible impact across organizations.
Pay: $110,000\.00 \- $120,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Vision insurance
Work Location: Hybrid remote in Irvine, CA
Salary Context
This $110K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Lynk Diagnostics, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($115K) sits 36% below the category median. Disclosed range: $110K to $120K.
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 ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Lynk Diagnostics AI Hiring
Lynk Diagnostics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Irvine, CA, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 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 ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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