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About This Role
Who we're looking for
As a Mid-Level AI & Machine Learning Engineer, you will be joining a team of talented and passionate engineers focused on creating the next generation of AI features to make Fieldwire the World's best Construction Management Platform! Your role will focus on adapting existing models, running models in our own infrastructure, as well as extracting and categorizing meaningful information from complex documents, PDFs and images. Your work building core AI features and services will directly impact all of our 1 million end users. If you have strong experience in delivering impactful AI solutions while ensuring applications remain secure, scalable, and performant, Fieldwire wants to talk to you!
What you'll be doing
The primary responsibilities of our next Mid-Level AI & Machine Learning Engineer will be to:
- Architect, build, and maintain AI foundations across the full stack, embedding them into core platform capabilities
- Evaluate & select open-source models, adapt them to our domain specific cases through fine-tuning, transfer learning and lightweight training
- Deploy and operate models within our own infrastructure, including GPU-backed environments with a strong focus on inference performance, resource utilization, throughput & latency
- Develop solutions for image understanding, visual object detection, classification and segmentation
- Extract and interpret information from PDFs, scanned documents, images, and other complex visual content, combining OCR, document layout analysis, and multimodal models
- Apply AI and computer vision techniques to surface relevant, context-aware information within users' workflows, leveraging context to power smarter product experiences
- Debug production issues across services and multiple levels of the stack
- Work to improve our Engineering standards, tooling, and processes
At Fieldwire, we're looking for our next Mid-Level AI & Machine Learning Engineer to have the following skills and experiences:
- Bachelor's or Master's degree in Computer Science or equivalent work experience
- 3+ years experience in a Fullstack, Backend, AI Engineering, or Machine Learning role
- Strong practical experience in machine learning, including training, evaluating, and deploying models
- Hands-on experience with computer vision and image-processing techniques
- Knowledge of modern toolchain used for developing high-quality applications
- Expert at writing clean, well-tested, and modular code
And if you have any of the following, we REALLY want you to apply today!
- Experience working with pretrained models and adapting them to domain-specific tasks through fine-tuning and transfer learning.
- Understanding modern model architectures, including transformers and vision-language models.
- Experience with image and document processing, including OCR, document layout analysis, or extracting structured information from PDFs and images.
- Experience building production systems preferably in Rust, Ruby on Rails or Angular
Compensation:
The estimated pay ranges for this role are as follows: $170,000.00 - $195,000.00
The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors such as your skills, qualifications, and experience. In addition to the salary, you may be eligible for a corporate bonus which can range up to 30%.
Why Fieldwire?
The field-first construction platform for less busywork and more building. Trusted on 4M+ projects worldwide, Fieldwire by Hilti gives crews reliable access to plans, tasks, and updates on one simple platform. It unites the field and office around a single, transparent source of truth, keeping teams aligned from start to finish. Designed for easy adoption, crews gain real-time visibility into progress, clear ownership of work, and the context to make confident decisions. As part of the Hilti Group, Fieldwire combines deep construction expertise with modern digital innovation to deliver better outcomes on every job. Learn more at Fieldwire.com.
Where is the job located?
This role is hybrid in San Francisco, California. Our headquarters are based in beautiful San Francisco and our office is centrally located right off of Embarcadero Bart station.
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Fieldwire is proud to be an Equal Opportunity Employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status or any other characteristic protected by law.
Salary Context
This $170K-$195K range is above the median 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 Fieldwire, 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 ($182K) sits 14% below the category median. Disclosed range: $170K to $195K.
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.
Fieldwire AI Hiring
Fieldwire has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $195K - $195K.
Location Context
AI roles in San Francisco pay a median of $260,550 across 2,766 tracked positions. That's 22% 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 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.
Frequently Asked Questions
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