Interested in this AI/ML Engineer role at Renew home?
Apply Now →Skills & Technologies
About This Role
Who We Are
Renew Home is on a mission to change how we power the world by making it easier for customers to save energy and money at home as part of the largest residential virtual power plant in North America.
We partner with industry\-leading brands to better manage residential energy for users by prioritizing efficiency, savings, and comfort — and cleaner energy for everyone.
We are an Equal Opportunity employer striving to create a diverse, equitable, and inclusive work environment where everyone feels that they have a voice that is heard.
We strongly encourage candidates to check out our website at www.renewhome.com to learn more about the world\-changing work we are doing.
Role Summary
At Renew Home, we are embracing AI\-assisted development as a core part of how we work — and we are looking for a Staff Engineer to lead that effort from the inside. This role will help define how our engineers interact with AI tools and agents, shape the standards and guardrails that make adoption safe and effective, and continuously improve the inner loop of software development: the daily experience of writing, building, testing, and iterating on code.
You will be embedded directly within the application development teams, giving you firsthand exposure to their workflows and friction points. While other engineers across the organization already contribute meaningfully to DevX, you will bring dedicated ownership, drive prioritization, and serve as the primary point of accountability for making progress. You won't be starting from scratch, and you won't be working alone.
The application engineering teams are your primary customers. Success here means earning their trust, understanding their daily workflows, and shipping improvements they notice and value. We want someone who brings expertise and fresh ideas, and who also knows how to build buy\-in, incorporate feedback, and make durable change through iteration.
This is a high\-impact individual contributor role with meaningful technical leadership responsibility. You will act as a force multiplier across the engineering organization by mentoring engineers, incorporating feedback, influencing engineering culture, and helping shape how we work.
What You Will Do
- Lead the evolution of our AI Developer Experience, enabling engineers to safely and effectively leverage AI throughout the software development lifecycle.
- Drive the evaluation, adoption and governance of AI development tooling (code generation, in\-editor assistance, agentic workflows), ensuring that adoption is practical, secure and measurable.
- Measure and improve the speed of common developer actions across our TypeScript/Node and Python codebases. This includes build times, pre\-push hooks, linting, type checking, conformance checks, and Nx task orchestration.
- Own our devcontainer strategy, both local and cloud\-based, to ensure a secure, consistent and reproducible development experience that scales across multiple concurrent projects.
- Maintain and improve local testing frameworks and developer\-facing test infrastructure.
- Own and evolve shared internal libraries and configuration systems used across application teams, ensuring they are well\-documented, versioned, and easy to adopt.
- Contribute to related areas including CI/CD and delivery pipelines, SDLC definition, language and library upgrade cycles (Node/Python, NPM, PyPI), developer onboarding and documentation, and observability.
Requirements
- 7\+ years of software engineering experience, with meaningful time spent on developer tooling, platform engineering, or developer productivity.
- Deep familiarity with TypeScript/Node and Python ecosystems — you know the package management landscape, common build tooling, and where things tend to break.
- Experience with monorepo tooling (Nx or similar) and an intuition for how to make large codebases fast to work with.
- Hands\-on experience designing, configuring, and improving devcontainer\-based development environments.
- Practical experience evaluating and integrating AI development tools (GitHub Copilot, Claude, OpenAI, Cursor, Codex) including thinking carefully about standards and guardrails, not just rollout.
- Strong listening, communication, and leadership skills — you seek out feedback from the engineers you serve and know when to lead and when to follow.
- Bonuses:
- + Experience with agentic development workflows, MCP (Model Context Protocol), tool orchestration, or AI workflow automation.
+ Experience relevant to AI security including designing secure execution environments, sandboxing, secrets management, or credential isolation for AI systems.
+ Experience with cloud\-based or remote development environments.
Benefits What You'll Get
- A full\-time position, with a competitive salary based on experience. The base salary for this role is $170k \- $220k. In addition to base compensation, this role is eligible for a target annual bonus of 15% of base salary, and participation in long\-term incentive programs tied to company growth and performance. We use market data and consider your job family, background, skills, experience, and U.S. work location to determine compensation within our established pay range.
- Fully remote work environment with home office set\-up allowance.
- Real and lived work\-life balance \- Company perks include no pre\-set vacation limits (with a top\-down culture of taking meaningful PTO every year!), parental leave benefits, and a corporate value of working sustainably and putting families first.
- Competitive benefits package that includes numerous health and wellness benefits.
- 401(k) plan, with employer contributions to the same.
- Opportunity to work with amazing people who are passionate about their mission, thriving in a fully\-remote work environment, and learning and growing every day.
At this time, Renew Home is unable to sponsor or take over sponsorship of employment visas. Candidates must be authorized to work in the United States without current or future immigration from the company, including training plans for foreign students.
EQUAL OPPORTUNITY EMPLOYER
Individuals seeking employment at Renew Home are considered without regard to race, color, religious creed, sex, gender identification, national origin, citizenship status, age, physical or mental disability, sexual orientation, marital, parental, veteran or military status, unfavorable military discharge, or any other status protected by applicable federal, state or local law.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Salary Context
This $170K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Renew home, 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. This role's midpoint ($195K) sits 11% below the category median. Disclosed range: $170K to $220K.
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.
Renew home AI Hiring
Renew home has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $220K - $220K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.