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About This Role
Fieldpiece is a privately held, global leader in professional HVACR tools and test instruments. We're known for developing innovative products that technicians rely on every day - and for investing in our people, our technology, and our future. Namedone of the Best Places to Work in Southern California for four consecutive years, we're looking for talented people who want to make an impact while growing their careers. We're looking for a Senior AI Engineer to help us turn the potential of AI into practical solutions that help teams to increase their efficiency, speed, and accuracy in their everyday jobs. This is a unique opportunity to become our first dedicated AI engineering hire and help shape how AI and automation are used across the organization. You'll partner with teams throughout the business—from Sales, Marketing, Finance, and HR to Product Development, Supply Chain, Operations, Customer Experience, and IT—to understand how they work, identify opportunities for improvement, and build solutions that deliver measurable value. This position is in our Orange, CA office with an exciting move to Tustin, CA in 2028. This role follows a hybrid work schedule, with employees typically working onsite three days per week. Additional in-office days may be required from time to time for onboarding, training, team meetings, project collaboration, or other business needs.Why This Role?* This isn't a role where you'll spend your time simply evaluating AI tools or developing proofs of concept that never make it into production. You'll sit down with business teams, understand what they're trying to accomplish and where their processes are slowing them down, translate those needs into technical requirements, determine the right solution, and then build, deploy, and improve it.
- The projects will vary widely. One may involve automating a manual workflow; another could involve creating an AI agent, improving access to company knowledge, connecting AI to an enterprise system, or building a decision-support application. The common thread is using technology to solve meaningful business problems.
- We're technology agnostic and want someone who can choose the right solution for the problem rather than force every problem into the same technology stack. That said, experience within the Microsoft ecosystem—particularly Dynamics 365 Business Central, Azure, Copilot, Power Platform, Fabric, Dataverse, and Microsoft 365—is highly valuable in our environment.
What You'll Do* Partner with teams across Fieldpiece to understand their workflows, pain points, and business needs and identify opportunities where AI, automation, or better technology can make a meaningful difference.
- Take business requirements from concept to solution—designing, developing, implementing, and supporting production-ready applications and automations.
- Build AI agents, copilots, workflow automations, decision-support tools, and other practical AI-enabled solutions.
- Develop applications using large language models, retrieval-augmented generation (RAG), enterprise knowledge systems, APIs, and agentic workflows.
- Connect AI solutions with ERP, CRM, PLM, customer support, analytics, knowledge management, productivity, and other enterprise platforms.
- Reengineer processes to improve speed, accuracy, quality, service, cost, revenue enablement, and employee productivity.
- Establish testing and evaluation approaches to ensure solutions are accurate, reliable, secure, scalable, and effective.
- Work closely with Information Systems, business leaders, data owners, and security stakeholders to implement appropriate governance and controls.
- Help users successfully adopt new AI tools and continuously improve solutions based on feedback and results.
- Evaluate emerging AI technologies and make practical recommendations based on business value, implementation requirements, cost, risk, and scalability.
- Bachelor’s degree in CS, AI, or related fields.
- 5+ years of professional software engineering experience, including ownership of production applications.
- 2+ years of hands-on experience building solutions using modern AI technologies, including large language models.
- Experience taking ambiguous business problems and translating them into practical technical solutions.
- Hands-on experience with prompt design, retrieval architectures, AI application testing, and model/system evaluation. Experience building AI agents, multi-agent systems, tool-using agents, RAG solutions, or complex agentic workflows.
- Experience integrating APIs, cloud services, enterprise applications, and data sources.
- Strong proficiency in Python or another modern language used for AI application development.
- Experience with data engineering, analytics, BI, MLOps/LLMOps, observability, or cloud deployment.
- Experience with source control, testing, deployment, monitoring, and modern software delivery practices.
- Excellent communication skills with the ability to work directly with both technical and nontechnical stakeholders and explain complex technical decisions in business terms.
- A practical, curious mindset - you enjoy learning how a business works, asking the right questions, and figuring out how technology can make it better.
Even Better If You Have* Experience with Microsoft Dynamics 365 Business Central or integrating solutions with ERP systems, Azure OpenAI, Microsoft Copilot, Power Platform, Fabric, or Dataverse.
- Experience working in a product, manufacturing, distribution, or field-service environment.
- Exposure to Lean, continuous improvement, or business process reengineering.
Why You'll Enjoy Working at Fieldpiece* As a privately held company, we invest for the long term—in our people, our products, and our future. That means we can focus on innovation, continuous improvement, and building lasting careers rather than chasing short-term results.
- Make a meaningful impact with a company where your ideas are heard, your expertise is valued, and your contributions help shape the future of our products and business.
Benefits & PerksWe believe great work starts with supporting our employees. That's why we offer a competitive total rewards package designed to promote your health, financial well-being, work-life balance, and professional growth.* Competitive base salary with annual bonus opportunity
- Comprehensive medical, dental, and vision coverage
- 401(k) with company match and immediate vesting
- Generous paid time off and paid holidays
- Hire to Retire philosophy - Individual Development Plans for everyone and a commitment to support career development
- Flexible Spending Accounts (FSA), Health Savings Accounts (HSA), and Employee Assistance Program (EAP)
- Music program – music rooms in all US offices with onsite guitar, piano, and voice lessons in California (classes can be streamed for other offices)
- Recognition programs, launch parties and team building events to share successes
Our Culture At Fieldpiece, our values shape how we work, collaborate, and serve our customers every day. We believe in:* Teamwork – We collaborate, support one another, and win as a team.
- Urgency – We move quickly, stay agile, and focus on what matters most.
- Respect – We value diverse perspectives and treat everyone with professionalism and integrity.
- Trust – We build strong relationships through accountability and transparency.
- Learning – We embrace curiosity, continuous improvement, and personal growth.
- No Ego – We leave titles and egos at the door, value the best ideas regardless of where they come from, and work together with humility, openness, and mutual respect.
At Fieldpiece, we're building more than innovative products - we're building careers. If you're looking for a place where your work matters and your contributions are recognized, we'd love to meet you. EOE AA M/F/Veteran/Disability
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Fieldpiece Instruments, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Fieldpiece Instruments AI Hiring
Fieldpiece Instruments has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Orange, CA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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