Interested in this AI/ML Engineer role at Byld?
Apply Now →Skills & Technologies
About This Role
Role Purpose
The Director of Maintenance & Reliability is the enterprise owner of equipment uptime, process stability, and reliability-driven performance across all BYLDPro processing facilities.
This role ensures that machines, systems, and processes operate predictably, safely, and at designed capacity, enabling BYLDPro to meet production commitments while scaling efficiently from Small to Large facilities.
The role integrates Maintenance Strategy, Reliability Engineering, Continuous Improvement, and Quality Systems governance, recognizing that quality outcomes at BYLDPro are primarily driven by equipment stability and process capability.Core Responsibilities
- Enterprise Maintenance & Reliability Strategy
- Define and own BYLDPro’s maintenance philosophy (preventive, predictive, condition-based)
- Establish standard PM programs for all machine types
- Define uptime, MTBF, MTTR, and reliability KPIs
- Ensure consistent maintenance execution across all facilities
- Equipment Uptime & Downtime Reduction
- Own root cause analysis for major downtime events
- Drive permanent corrective actions (not temporary fixes)
- Partner with Production Engineering and OT to eliminate recurring failures
- Ensure reliability is designed into new equipment and layouts
- Preventive & Predictive Maintenance
- Implement predictive maintenance where economically justified
- Standardize spare parts strategies and critical spares lists
- Reduce reactive maintenance through data-driven insights
- Support lifecycle planning for high-capex equipment
- Reliability-Driven Continuous Improvement
- Lead CI initiatives tied to:
+ OEE improvement
+ Scrap and rework reduction
+ Setup stability
- Apply Lean / Six Sigma tools where appropriate
- Ensure CI efforts are measurable and sustained
- Quality Systems Governance (Process-Focused)
- Own enterprise quality systems, including:
+ Non-conformance reporting (NCR)
+ Root cause and corrective action (CAPA)
+ Process audits
- Ensure quality standards are enforced without compromising uptime
- Maintain enterprise-level quality visibility and trend analysis
*Note: This role owns quality systems and process quality, not day-to-day inspection staffing.*6. Facility Launch & Expansion Support
- Support new facility openings (e.g., Ohio) by:
+ Defining maintenance staffing models
+ Establishing PM readiness before go-live
+ Training maintenance leadership
- Ensure reliability standards are embedded from Day 1
- Cross-Functional Collaboration
- Work closely with:
+ Processing Engineering (layouts, equipment selection)
+ Production Planning (capacity realism)
+ OT & Manufacturing Systems (machine data, diagnostics)
+ Plant Managers (execution and accountability)
- Serve as escalation point for systemic reliability issues
- Team Leadership & Development
- Lead and develop enterprise maintenance and reliability resources
- Coach Plant-level Maintenance Managers and Supervisors
- Establish training standards and technical capability expectations
- Build bench strength to support growth
What This Role Does *Not* Own
- Daily shift-level maintenance dispatching
- Local labor scheduling
- Purchasing execution (input only)
- IT infrastructure ownership
This role sets the system and standards; plants execute.Key Success Metrics
- Equipment uptime and availability
- OEE improvement vs baseline
- Reduction in unplanned downtime
- Preventive vs reactive maintenance ratio
- Scrap and rework trends linked to equipment stability
- Readiness and stability of new facility launches
Required Qualifications
- Bachelor’s degree in Engineering or related technical field (Mechanical, Electrical, Industrial preferred)
- 10+ years experience in manufacturing, processing, or industrial operations
- 5+ years in a senior maintenance, reliability, or engineering leadership role
- Strong experience with:
+ Preventive & predictive maintenance systems
+ Root cause analysis
+ Industrial equipment reliability
- Proven ability to scale systems across multiple facilities
Preferred Qualifications
- Six Sigma Black Belt or equivalent CI certification
- Experience with automated or semi-automated production lines
- Exposure to ERP / MES / OEE systems
- Experience supporting greenfield facility launches
- Strong business acumen and KPI-driven mindset
Leadership Expectations (BYLDPro Standard)
- Operates with enterprise-first thinking
- Data-driven, pragmatic, and action-oriented
- Comfortable balancing uptime pressure with quality discipline
- Builds systems that scale, not heroics
- Acts as a partner to Operations, not a gatekeeper
Career Path & Growth
This role is a key pillar of the BYLDPro operating model and a natural feeder into:
- Senior Operations leadership
- VP-level reliability or platform roles
- Broader manufacturing systems leadership as BYLD scales
COMPENSATION PACKAGE:
- Annual Salary (commensurate with experience): $140,000 - $170,000
- Competitive Benefits Package: Medical, Dental, and Vision insurance coverages
- 401(k) retirement savings programs
- PTO program for work-life balance
- Employee Reimburseables
*No visa sponsorship available. Candidates must be eligible to work in the United States. Please note that relocation assistance is not provided for this position. Candidates must be local to the [Facility Location] area or willing to relocate at their own expense.*
We do not accept any and all unsolicited resume submissions and correspondences from agencies, recruiting firms, or staffing groups. Any solicitation to any BYLD team member will be immediately dismissed.
*ABOUT BYLD:*
*BYLD is a construction technology company that provides software and hardware solutions for the design and construction industry. BYLD’s goal is to provide a more efficient and cost-effective framing solution for cold-formed, steel multi-unit structures. BYLD operates in a dynamic environment and strives to support a culture that is collaborative, innovative, and creative. BYLD also offers a flexible work environment.*
VJgxxzoFYP
Salary Context
This $140K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Byld, 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. Disclosed range: $140K to $170K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Byld AI Hiring
Byld has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Aurora, CO, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.