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About This Role
Job Description:
This position is responsible for orchestrating advanced, agentic workflows and engineering the surrounding quality infrastructure for enterprise\-grade software. The role balances high\-velocity startup execution with rigorous software craftsmanship, utilizing continuous refactoring, stacked architectures, and advanced testing methodologies to control non\-deterministic AI outputs and systematically minimize the long\-term cost of change.
Essential Duties \& Responsibilities
- Agentic System Development: Write production\-grade code alongside advanced agents, engineer systems that orchestrate agentic workflows, and construct the surrounding quality machinery to ensure reliability under heavy enterprise loads.
- Architectural Oversight: Design and implement robust, stacked architectures that employ algorithmic guardrails to successfully bound and constrain stochastic LLM outputs.
- Software Craftsmanship: Champion continuous refactoring, micro\-modular design, and extreme code readability as standard daily practices to keep the long\-term cost of change low.
- Quality Infrastructure: Adapt 2020\-era quality methodologies (e.g., TDD, real Devops, BDD, etc. ) to modern AI paradigms by implementing characterization testing, mutation testing, property\-based testing, observability frameworks, and related.
- C ollaborative Execution: Engage in heavy, direct technical collaboration with a peer group of senior engineers; actively participate in high\-level trade\-off debates while maintaining a strict "disagree and commit" shipping philosophy.
Qualifications \& Skills
Enterprise Software Expertise (Historical Track Record)
- Substantial, proven experience architecting and maintaining production\-grade enterprise software systems.
- Deep understanding of complex integration surface areas, the long\-term life cycle of architectural decisions, and designing for decade\-long maintainability.
Modern Agentic Capability
- Hands\-on, deep engagement with current\-generation development agents (e.g., Claude Code, Codex, or equivalent tools) within the current calendar year.
- Demonstrated experience moving past basic prompt engineering into building tools, pipelines, and stacked architectures to tighten developer feedback loops.
- An empirical, evidence\-based understanding of the current limitations, failure modes, and strengths of LLM\-driven development.
Core Technical Proficiencies
- Fluency in at least one mainstream programming language, with a demonstrated ability to learn and switch languages fluidly based on project needs.
- Working depth with Git, CI/CD pipelines, containerization, and modern testing frameworks.
Key Behavioral Competencies
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- Startup Velocity: Thrives in a highly iterative environment ("build today, release tonight, refactor tomorrow").
- Empirical Tinkering: A proactive investigator who utilizes continuous experimentation and strict metrics to discover technical truths.
- High\-Conviction, Low\-Ego: Ability to confidently articulate and defend a complex technical viewpoint while remaining fully supportive of executing a peer’s differing strategy to high standards.
- Tradeoff Fluency: Rejects simple "A \> B" binaries, instead evaluating options through a lens of relative costs and alignment with core objectives (e.g., "Given goal Q, option A costs X and option B costs Y)
Minimum Requirements
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Education: Bachelor’s degree in Computer Science, Software Engineering, or a related field (or equivalent professional experience).
Experience: Minimum of 10 years of professional software engineering/code\-writing experience. Proven track record working in both agile Startup environments and scaled Enterprise organizations.
Systems \& Technical Expertise: Deep, hands\-on proficiency with modern, cutting\-edge AI tools and workflows (as of 2026\). Demonstrated commitment to continuous learning and staying current with rapid advancements in the AI space.
Additional Requirements
Managerial Requirements: N/A
Physical Requirements: Prolonged periods sitting at a desk and working on a computer.
Travel Requirements: May be required to travel between local Cambria locations
Cambria’s starting salary range for this position is $100,000 \- $150,000 Salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the target for new hires for the position. Individual pay is determined by several factors, including work location, job\-related experience, and relevant education and/or training.
Cambria offers a competitive benefits package that encompasses Health and Dental Insurance, Paid Time Off, 7 paid Holidays , 401(k) plus matching, Discretionary Profit Sharing, Flexible Spending Account; Life, Supplemental Life, and Disability Insurance; Referral Program, Tuition Reimbursement, Employee Assistance Program, Employee Discount and Professional Development Assistance.
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice.
At Cambria, dedication to philanthropy and our people is at the heart of who we are. We strive to make a meaningful difference in the world every day by prioritizing employee well\-being and fostering inclusivity and empowerment. Under the banner of CambriaCares , our philanthropic endeavors show our commitment to our people and our community to uplift kids, support education, and contribute to a sustainable future.
For additional company information, please visit www.CambriaUSA.com
An offer of employment is contingent upon the successful completion of a background verification check, subject to applicable laws and regulations. The results will be reviewed based on the individual's record, and the specific duties and requirements of the job.
Salary Context
This $100K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Cambria, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($125K) sits 31% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Cambria AI Hiring
Cambria has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $150K - $150K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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