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About This Role
This position cannot provide employment visa sponsorship. To ensure there is no misunderstanding, please confirm that your authorization to work in the United States is not dependent on OPT, CPT, or any other temporary employment authorization that would require a new visa sponsorship prior to applying.
Hygiena provides rapid microbiology diagnostic tests and solutions for mission\-critical, time\-sensitive processes around the world. Hygiena’s proprietary diagnostic technologies enable customers to; prevent illness and save lives, comply with regulations, protect products and brands, run businesses more effectively. Solutions are sold in a wide range of industries globally, but the core focus is food safety.
Headquartered in Camarillo, California and with offices around the world, we are actively looking for talented individuals to help grow the business! At Hygiena we believe:* In providing the highest quality products \& service
- Being a leader in innovation
- Having a compelling desire to improve and win in the marketplace
- In contributing positively not only in the workplace, but in our community and environment!
With rapid growth comes opportunity. We are looking for an Applied AI Engineer (LLM Applications Engineer) to join our team onsite in our Camarillo, CA office! This is a full\-time / exempt position. Responsibilities:* Design, develop, and deploy machine learning models and AI features using Python (scikit\-learn, PyTorch, TensorFlow, or equivalent frameworks).
- Collaborate with Product and Engineering to identify AI/ML use cases with measurable customer and business impact (e.g., anomaly detection, predictive alerts, NLP for compliance documents).
- Build and maintain ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment in Azure cloud environments.
- Integrate AI/ML capabilities into the SureTrend platform via APIs, working with .NET/C\# and React\-based services.
- Monitor model performance in production; design retraining pipelines and drift detection strategies.
- Document model methodologies, data requirements, and performance benchmarks; communicate findings to technical and non\-technical stakeholders.
Qualifications:* Bachelor’s or Master’s Degree in Computer Science, Machine Learning, Data Science, or a related field required.
- Minimum of 3\-5 years of applied ML or AI engineering experience, including:
+ Strong Python development skills (pandas, NumPy, scikit\-learn; PyTorch or TensorFlow preferred)
+ End\-to\-end ML pipeline development: data preparation, feature engineering, model training, evaluation, and deployment
+ Cloud\-based ML infrastructure (Azure ML, Azure AI Services, or equivalent)
+ RESTful API development for exposing model outputs to product surfaces
- Experience working in Agile teams and managing ML workstreams in Azure DevOps or Jira.
- Familiarity with LLMs, prompt engineering, or Retrieval\-Augmented Generation (RAG) architectures is a strong plus.
- Knowledge of the food safety or life sciences domain is advantageous but not required.
Salary Range:
$137,000 \- $162,000 annually \+ bonus Why you’ll want to join Hygiena:*Teamwork as a core value*
Our emphasis on teamwork and cross\-functional communication enables us to build stronger bonds within our business.*Be a part of something big*
We play a critical role in helping to prevent global health crisis such as foodborne illness, healthcare\-associated infections, and other outbreaks.*Giving back to our communities*
We believe in giving back by supporting local organizations committed to improving the lives of children and youth in our communities. Benefits and Perks:* 15 days of PTO \& 10 paid company holidays
- Medical with HSA employer contribution, Dental, Vision available 1*st* of the month after start date
- Company paid Life Insurance, Short\* and Long\-Term Disability and an Employee Assistance Program
- 401(k) with Safe Harbor and Profit\-Sharing employer contributions
- Tuition Reimbursement program
- Charitable Contribution matching
- Employee Referral bonus opportunities
- *State paid short\-term disability for California based employees*
We are an equal opportunity employer and value diversity. All employment is decided on the basis of qualifications, merit and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Hygiena participates in E\-Verify.
Salary Context
This $137K-$162K 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 Hygiena, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($149K) sits 32% below the category median. Disclosed range: $137K to $162K.
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.
Hygiena AI Hiring
Hygiena has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Camarillo, CA, US. Compensation range: $162K - $162K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 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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