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About This Role
We are a global team of innovators and pioneers dedicated to shaping the future of observability. At New Relic, we build an intelligent platform that empowers companies to thrive in an AI-first world by giving them unparalleled insight into their complex systems. As we continue to expand our global footprint, we're looking for passionate people to join our mission. If you're ready to help the world's best companies optimize their digital applications, we invite you to explore a career with us!
Your opportunity
As a GTM AI Engineer, you will contribute to our "Revenue Velocity" engine. You won't just be managing tools; you will identify operational friction and help build the AI-powered infrastructure that allows our Sales (SLG), Expansion, and PLG teams to focus on high-value selling. You will help build and maintain the stack that converts product signals into automated sales actions.
What you'll do
- Systems & Automations: Assist in building and maintaining the systems that power our opportunity management, sales handoffs, forecasting, and post-sales workflows.
- Process Optimization: Spot gaps, solve edge cases, and ensure the funnel, from first meeting to renewal, runs smoothly and intelligently.
- AI-Native Automation: Deploy automations that surface "next-best actions," trigger timely follow-ups, and improve deal velocity using Clay, Boomi, Claude and Gemini.
- Data Hygiene & Enrichment: Execute routine data hygiene and enrichment workflows across CRM records, ensuring data accuracy and completeness.
- Workflow Testing & Monitoring: Test, monitor, and troubleshoot automated GTM workflows to identify edge cases and minimize operational drag.
- Cross-Functional Collaboration: Work side-by-side with Sales, Accounts, and Marketing teams to translate operational needs into technical solutions.
- Stack Evolution: Help connect disparate GTM tools into a cohesive stack and continuously improve that stack as we scale.
- Execution: Turn high-level GTM goals into reliable, scalable workflows while balancing speed with quality.
This role requires
- 2+ years of experience in GTM systems, revenue operations, or sales architecture at a fast-moving company
- Built or supported systems that power sales pipelines, post-sale processes, and customer lifecycle ops
- Hands-on experience building basic automations using webhooks, APIs, or low-code platforms
- Proven experience designing and executing marketing campaigns, personalized outreach, and lead nurturing strategies
- Enjoyment of working closely both with technical and non-technical teams to turn operational problems into clean system design
- Innate curiosity, a hands-on approach to experimentation, and strong analytical problem-solving skills
- Deep familiarity with key platforms across the modern GTM ecosystem including Salesforce, Hubspot, Salesloft, Marketo, Clay
- Proficiency in analytics and working with SQL to derive insights from data
- Experience maintaining database integrity across sales and marketing platforms through data validation, deduplication, and list hygiene
- A strong passion for applying AI to automate and enhance customer and sales workflows
Bonus points if you have
- Hands-on experience designing AI-native products — MCP server, agentic workflows, autonomous systems, AI confidence visualization, human-in-the-loop flows, or building user trust in AI-generated outputs
- 1+ years of experience with iPaaS tools including but not limited to Boomi, Workato, Zapier, Celigo, n8n, Gong
- Experience with observability, incident response, or developer-facing infrastructure products
Please note that visa sponsorship is not available for this position.
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The pay range below represents a reasonable estimate of the salary for the listed position. This role is eligible for a corporate bonus plan. Pay within this range varies by work location and may also depend on job-related factors such as an applicant’s skills, qualifications, and experience.
New Relic provides a variety of benefits for this role, including healthcare, dental, vision, parental leave and planning, and mental health benefits, a 401(k) plan and match, flex time-off, 11 paid holidays, volunteer time-off, and other competitive benefits designed to improve the lives of our employees.
Estimated Base Pay Range
$95,000 - $119,000 USD
Fostering a diverse, welcoming and inclusive environment is important to us. We work hard to make everyone feel comfortable bringing their best, most authentic selves to work every day. We celebrate our talented Relics’ different backgrounds and abilities, and recognize the different paths they took to reach us – including nontraditional ones. Their experiences and perspectives inspire us to make our products and company the best they can be. We’re looking for people who feel connected to our mission and values, not just candidates who check off all the boxes.
If you require a reasonable accommodation to complete any part of the application or recruiting process, please reach out to [email protected].
We believe in empowering all Relics to achieve professional and business success through a flexible workforce model. This model allows us to work in a variety of workplaces that best support our success, including fully office-based, fully remote, or hybrid.
Our hiring process
In compliance with applicable law, all persons hired will be required to verify identity and eligibility to work and to complete employment eligibility verification. Note: Our stewardship of the data of thousands of customers means that a criminal background check is required to join New Relic.
We will consider qualified applicants with arrest and conviction records based on individual circumstances and in accordance with applicable law including, but not limited to, the San Francisco Fair Chance Ordinance.
Headhunters and recruitment agencies may not submit resumes/CVs through this website or directly to managers. New Relic does not accept unsolicited headhunter and agency resumes, and will not pay fees to any third-party agency or company that does not have a signed agreement with New Relic.
New Relic develops and distributes encryption software and technology that complies with U.S. export controls and licensing requirements. Certain New Relic roles require candidates to pass an export compliance assessment as a condition of employment in any global location. If relevant, we will provide more information later in the application process.
Candidates are evaluated based on qualifications, regardless of race, religion, ethnicity, national origin, sex, sexual orientation, gender expression or identity, age, disability, neurodiversity, veteran or marital status, political viewpoint, or other legally protected characteristics.
Review our Applicant Privacy Notice at https://newrelic.com/termsandconditions/applicant-privacy-policy
Salary Context
This $95K-$119K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At New Relic, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($107K) sits 50% below the category median. Disclosed range: $95K to $119K.
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.
New Relic AI Hiring
New Relic has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Bengaluru, Karnataka, India, Arlington, VA, US. Compensation range: $119K - $119K.
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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