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About This Role
Location
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Remote
Employment Type
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Full time
Location Type
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Remote
Department
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Engineering
Compensation
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- Total cash compensation includes base and annual performance bonus. Please note that salary and compensation may vary based on skills, experience, performance, and location. $180K – $220K • Offers Equity • Offers Bonus
OverviewApplicationAbout NewtonX
NewtonX is a B2B insights company trusted by the world's most innovative companies to make high\-stakes decisions with confidence. We combine a verified network of business professionals with AI\-powered research tools to deliver research intelligence faster, more precise, and more defensible than traditional methods.
Our clients include Google, Microsoft, TikTok, DoorDash, Stripe, and Coinbase. Our research has been cited by Fortune, Forbes, TechCrunch, Adweek, and the Wall Street Journal.
NewtonX has raised $47M from investors including Two Sigma Ventures, Third Prime, XFund, and Citi Ventures.
About The Role
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In this role, you'll own the core LLM infrastructure powering two products redefining B2B research:
Hub – The central cockpit for B2B research. Build self\-serve features that compress weeks into days: question expert insight follow\-up, powered by RAG and adaptive workflows.
Prime – Syndicated intelligence at scale. Architect automated systems that continuously capture expert opinions—creating longitudinal datasets and refreshable dashboards that compound in value.
The technical challenge: fusing structured survey data with unstructured expert knowledge, building semantic search across proprietary corpora, and creating AI pipelines that maintain research\-grade quality at scale. If you love building novel data systems that turn raw signals into defensible, high\-margin products, this is where you’ll do the most important work of your career.
What We're Looking For
Required
- You write exceptional code, fast. 3\-4 years of experience shipping production code in a fast\-paced environment
- Full\-stack expertise: Moderate proficiency in React, TypeScript, and modern frontend frameworks. Backend experience with Python, Node.js, or similar
- AI/ML implementation experience: Hands\-on experience integrating LLMs, building with OpenAI/Anthropic APIs, or implementing ML models in production. We care more about a demonstrated eagerness to learn and an understanding of complex systems than specific years.
- Cloud and infrastructure: Experience with AWS, Docker, and modern deployment practices
- Quality mindset: Experience with testing, code reviews, and maintaining high code quality standards
- Customer focus: Ability to translate user needs into technical solutions while maintaining engineering best practices
Nice To Have
- RAG systems, embeddings, semantic search
- Real\-time data processing or streaming architectures
- Open\-source contributions in AI/ML
What Actually Matters
We care less about credentials and more about demonstrated ability. If you've built something interesting with LLMs\- a side project, an open\-source contribution, a blog post that shows how you think\- put that at the top of your application.
Why Join NewtonX?
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- Frontier technology: Work with cutting\-edge AI systems, pushing the boundaries of what's possible in enterprise AI applications
- Exceptional traction: Strong product\-market fit with the world's largest investment banks, hedge funds, and consulting firms
- World\-class team: We take talent density seriously. Work alongside incredibly smart, driven people who are passionate about their craft
- High velocity: Ship features that directly impact how billion\-dollar decisions are made
- Growth opportunity: Join during a hypergrowth phase with clear paths for technical and career advancement
- Impact: Your work will directly shape how the world's leading organizations make critical business decisions
What We Offer
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- Excellent medical, dental, and vision insurance.
- 401k match with immediate vesting.
- Generous Paid time off, holidays, and parental leave.
- A diverse, collaborative, and positive culture where we invest in and celebrate each other’s success.
Flexible work environment where we work hard but have fun (happy hours, team projects, and retreats).
NewtonX is proud to be an equal opportunity workplace. We do not discriminate based upon race, religion, color, national origin, sex, sexual orientation, gender identity/expression, age, status as a protected veteran, status as an individual with a disability, or any other applicable legally protected characteristics.
Salary Context
This $180K-$220K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At NewtonX, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($200K) sits 20% above the category median. Disclosed range: $180K to $220K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
NewtonX AI Hiring
NewtonX has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $220K - $220K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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 (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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