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About This Role
Welcome to the intersection of energy and home services. At NRG, we’re driven by our passion to create a smarter, cleaner and more connected future.
Vivint Smart Home, an NRG owned company, is a leading smart home company in the United States, dedicated to redefining the home experience with intelligent products and services. We find purpose in proactively protecting and keeping our customers connected to home, no matter where they are. Join the Smart Home team to create smarter, safer and more sustainable homes.
Vivint pioneers cutting\-edge smart home security solutions, empowering customers to live a safer, smarter, and more sustainable life. We seek passionate, innovative engineers eager to transform the daily lives of millions through groundbreaking solutions. We blend intelligent hardware, real\-time sensor data, and AI to transform how people interact with their homes.
About This Role
We are seeking a Principal AI Platform Engineer to build the shared product\-integrity layer for customer\-facing AI systems. This role will create the evaluation, observability, safety, and launch\-readiness infrastructure needed to ship AI features with measurable quality, reliability, and customer impact. In this role, you will be responsible to:
- Build shared evaluation infrastructure for models, prompts, agents, and multimodal AI systems.
- Own golden datasets, regression suites, offline and online evals, and LLM\-as\-judge governance.
- Develop observability for model quality, latency, cost, drift, safety, and customer impact.
- Create launch\-readiness gates for AI features across camera, agentic, personalization, multimodal, and energy products.
- Partner with Product, Analytics, Privacy, Security, and Engineering on trustworthy AI productization.
- Define reusable standards for AI quality, monitoring, safety, and operational readiness.
Required Qualifications:
- Bachelor's degree in Computer Science, Software Engineering, Data Science, or related field
- 10\+ years of software engineering, ML engineering, or AI platform experience
- Experience evaluating, deploying, or monitoring production AI systems
- Strong Python and data engineering skills
- Experience with offline/online evaluation, data quality, model monitoring, or observability systems
- Familiarity with LLM evaluation, prompt evaluation, model regression testing, or safety guardrails
- Strong communication skills and ability to set standards across teams
Preferred Qualifications:
- Experience with LLM\-as\-judge, agent tracing, multimodal evals, golden datasets, or automated regression suites
- Experience with GCP/AWS, Vertex AI, SageMaker, MLflow, Datadog, OpenTelemetry, Private Cloud or similar tooling
- Experience with privacy\-aware AI systems, customer trust metrics, or launch\-readiness processes
- Experience supporting computer vision, GenAI, recommendation, or edge AI products
- Experience building developer platforms or internal AI tooling
Working at Vivint:
Learn about the Vivint Culture and why it’s a great place to grow your career!
Here are some highlighted perks you should ask us about:
- Free daily lunch and drinks on site
- Paid holidays and flexible paid time away
- Employee/Friends/Family Discounts
- Onsite health clinic, gym, gaming tables
- Medical/dental/vision/life coverage \& 24/7 Medical Hotline
- 401(k) \+ Employer Match
- Employee Resource Groups
- Quarterly Innovation Weeks
WORKING CONDITIONS:
This job operates in a professional office environment. This role routinely uses standard office equipment such as computers, phones, photocopiers, filing cabinets and fax machines.
SAFETY:
Vivint enforces a safety culture whereby all employees have the responsibility for continuously developing and maintaining a safe working environment. Each new employee is responsible for completing all training requirements. Additionally, the employee must accept they have responsibility for maintaining the safety of themselves, their co\-workers, and the public. Employee must adhere to all written and verbal instructions, promptly report and correct all hazards or unsafe conditions, question non\-standard operations or unmitigated hazards, and provide feedback to management on all safety issues.
*We support the use of AI tools to help you prepare for your interview (e.g., practicing responses, researching the role, or refining your resume). However, during interviews and assessments, we expect responses to reflect your own thinking, experience, and communication. Use of AI to generate or read answers in real time, complete assessments, or misrepresent your qualifications is not permitted and may impact your candidacy.*
NRG Energy is committed to a drug and alcohol\-free workplace. To the extent permitted by law and any applicable collective bargaining agreement, employees are subject to periodic random drug testing, and post\-accident and reasonable suspicion drug and alcohol testing. EOE AA M/F/Vet/Disability. Level, Title and/or Salary may be adjusted based on the applicant's experience or skills.
Official description on file with Talent.
We support the use of AI tools to help you prepare for your interview (e.g., practicing responses, researching the role, or refining your resume). However, during interviews and assessments, we expect responses to reflect your own thinking, experience, and communication. Use of AI to generate or read answers in real time, complete assessments, or misrepresent your qualifications is not permitted and may impact your candidacy.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Vivint, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Vivint AI Hiring
Vivint has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lehi, UT, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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