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About This Role
Job Title: Principal Machine Learning Engineer
Department: Product & Technology
Classification: Exempt
Duration: Direct Hire
Location: BILT - Global Brand Support Center, Grapevine, Texas 76051
Overview
BILT Incorporated is a fast-growing software-as-a-service company revolutionizing instructions and training for consumers and professional technicians alike. BILT creates official 3D Intelligent Instructions® for thousands of products from hundreds of innovative brands and hosts them on a free mobile app. Users can manipulate interactive animations on a touchscreen, providing a superior training differentiator for products that require assembly, installation, maintenance, programming, or repair. BILT’s mission is to create an experience so enabling and empowering it turns users into promoters of the brands we serve. Our Global Brand Support Center is located in the Dallas-Ft. Worth metroplex between DFW airport and the Southlake Town Square.
About the Job
BILT is building the future of spatial instruction—transforming how people learn to assemble, install, and repair the physical world. We sit at the intersection of AI and spatial computing, with a unique dataset of rich 3D instructional content that positions us to lead in multimodal understanding and generation. We're looking for a Principal Machine Learning Engineer to be a foundational technical leader on our AI team.
This is not a role where you'll fine-tune some models and call it a day. You'll be tackling open research questions, exploring novel architectures, and building systems that don't have established playbooks. You'll work at the frontier of multimodal AI—bridging language, vision, and spatial understanding in ways that haven't been done before. If you thrive in ambiguity and want to shape the technical direction of an AI team from the ground up, this is your role.
What You'll Do
Serve as the technical anchor for the AI team, driving architectural decisions and setting the standard for engineering excellence
Tackle ambiguous, research-oriented problems—from initial exploration through production deployment
Design and build novel ML systems spanning multimodal understanding, generation, and spatial reasoning
Fine-tune, adapt, and extend foundation models to our unique domain, developing robust evaluation frameworks and training pipelines
Own critical infrastructure including training pipelines, evaluation frameworks, and model serving
Mentor and elevate other engineers through technical guidance, code review, and knowledge sharing
Partner closely with the Director of AI and CTO to shape technical strategy and roadmap
Stay current with the rapidly evolving AI landscape and identify opportunities to apply emerging techniques
What We're Looking For
- 8+ years of experience in machine learning, with a track record of shipping complex systems to production
- Deep expertise in modern deep learning frameworks (PyTorch strongly preferred)
- Experience with LLMs, multimodal models, or related areas—not just fine-tuning, but understanding architectures deeply enough to extend and adapt them
- Comfort with research-oriented work: reading papers, prototyping novel approaches, and navigating problems without clear solutions
- Strong software engineering foundations—you write clean, maintainable, production-quality code
- Ability to work autonomously and drive projects from ambiguous problem statements to working systems
Preferred
- Experience with Vision-Language Models, VLA architectures, or embodied AI
- Background in 3D understanding, CAD, spatial reasoning, computer graphics, or geometric deep learning
- Expertise in model optimization, efficient inference, and deployment at scale
- Publications, open-source contributions, or demonstrated research impact
- Experience as a technical lead or foundational engineer at a high-growth startup
Work Environment
- This role is based at BILT’s Global Brand Support Center in Grapevine, Texas, near DFW Airport and Southlake Town Square.
- Routine use of standard office equipment (computers, tablets, phones, email, and Microsoft Teams).
- Physical demands include prolonged computer use and occasional lifting of up to 25 pounds.
- Office hours: 8:30 AM–5:30 PM, Monday through Friday, with convenient parking and nearby amenities.
Other Duties
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.
About BILT
BILT provides 3D Intelligent Instructions, creating and delivering interactive content for iOS, Android, and now turnkey guided work instructions for visionOS. The award-winning platform revolutionizes the DIY experience as well as professional training and operational enablement. BILT is accessible, sustainable, and efficient.
For more information go to: www.biltapp.com/careers and submit a profile resume in our applicant tracking system.
- This position is located in the Grapevine Brand Support Center in Texas.
BILT is a veteran friendly company and an Equal Opportunity Employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, gender expression, national origin, protected veteran status, or any other basis protected by applicable law, and will not be discriminated against on the basis of disability.
We do not accept unsolicited agency resumes. We will not pay fees to any third-party agency, outside recruiter or firm without a mutually agreed-upon contract and will not be responsible for any agency fees associated with unsolicited resumes. Unsolicited resumes will be considered our property and will be processed accordingly.
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 BILT, 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.
BILT AI Hiring
BILT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Grapevine, TX, 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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