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About This Role
Welcome to the intersection of energy and home services. At NRG, we’re all about propelling the next generation of leaders forward. We are driven by our passion to create a smarter, cleaner and more connected future. We deliver innovative solutions that make our customers’ lives easier—helping them power, protect, and intelligently manage their homes and businesses. To do this, we need creative and talented people to join our company.
We offer a dynamic work environment and a unified and inclusive culture. NRG fosters a strong sense of belonging that leads to better collaboration and business performance. Our company programs are designed to help employees develop the skills they need for success now and in the future. In everything we do, we aim to champion our employees and bring value to our customers, investors and society.
More information is available at www.nrg.com. Connect with NRG on Facebook, Instagram, LinkedIn and X.
About This Role
We are seeking a Sr 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, AI/ML, or a related technical field, and 5\+ years of professional experience in software development, applied science, or ML engineering; or
- Master’s degree in Computer Science, Software Engineering, AI/ML, or a related technical field, and 2\+ years of professional experience in software development, applied science, or ML engineering
- 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:
- Paid holidays and flexible paid time away
- Employee/Friends/Family Discounts
- Medical/dental/vision/life coverage \& 24/7 Medical Hotline
- 401(k) \+ Employer Match
- Employee Resource Groups
The base salary range for this position is: $150K to $180K\* \*The base salary range above represents the low and high end of the salary range for this position. Actual salaries will vary based on several factors including but not limited to location, experience, and performance. The range listed is just one component of the total compensation package for employees. Other rewards may include annual bonus, short\- and long\-term incentives, and program\-specific awards. In addition the position may be eligible to participate in the benefits program which include, but are not limited to, medical, vision, dental, 401K, and flexible spending accounts.
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.
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.
Salary Context
This $150K-$180K range is below the median for AI Software Engineer roles in our dataset (median: $190K across 219 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Vivint, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($165K) sits 29% below the category median. Disclosed range: $150K to $180K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Vivint AI Hiring
Vivint has 5 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, AI Software Engineer. Based in WA, US. Compensation range: $180K - $345K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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