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About This Role
Location
Remote (US)
Employment Type
Full time
Department
Product R&D
Compensation
- Total Earnings (Base + Bonus) $260K – $320K • Offers Equity • Offers Bonus
At G2, we’re committed to building an equitable and inclusive workplace for all. That's why our compensation program is rooted in market data, transparency, fairness, and individual performance.
Compensation in an offer is determined by factors such as the candidate’s experience, relevant skills, and job-related knowledge.
We’ve built a company-wide framework to provide employees clarity on how compensation and success is measured at each level. This career framework supports our employees' professional growth and guides them to their PEAK.
We also support our employees by offering generous benefits, such as flexible work, ample parental leave, and unlimited PTO. Click here to learn more about our benefits.
About G2 - The Company
G2 is the world's largest and most trusted software marketplace. When you join G2, you’re joining the industry’s leading team that helps businesses reach their peak potential by powering decisions and strategies with trusted insights from real software users.
Now, we have joined forces with Capterra, SoftwareAdvice, and GetApp to create the largest source of online data and software insights to fuel intelligent buying in the age of AI. With 200M+ combined annual visitors and 6M verified reviews, we are now the centralized place to enable software buyers to make better and faster decisions with confidence.
And we are just getting started! We are setting out to transform the global B2B software industry and become the most trusted data foundation for buyers and sellers of software for the age of AI.
Does that sound exciting to you? Come join us as we try to reach our next PEAK!
About G2 - Our People
At G2, everything we are and what we do is grounded in our PEAK values— (*P*erformance + *E*ntrepreneurship + *A*uthenticity + *K*indness. Working at G2 means you are part of a value-driven, growing global community that climbs PEAKs together. We cheer for each other’s successes, learn from our mistakes, and support and lean on one another during challenging times. With ambition and entrepreneurial spirit we push each other to take on challenging work, which will help us all to grow and learn.
You will be part of a global, diverse team of smart, dedicated, and kind individuals - each with unique talents, aspirations, and life experiences. At the heart of our community and culture are our people-led ERGs, which celebrate and highlight the diverse identities of our global team. As an organization, we are intentional about our DEI and philanthropic work (like our G2 Gives program) because it encourages us all to be better people.
About The Role
Summary of Responsibilities:
As a Software Engineering Director, Agentic Evaluations, your responsibilities will center around delivery of useful and credible evaluations of agents offered by software vendors. You’ll enhance the system that performs validation of agent performance, and lead the core team behind it. You will leverage “agent-first” ways of working, applying agentic engineering techniques to deliver your work to production.
Detailed Responsibilities:
Build and enhance agent evaluations that run against live agents [80%]:
- Help scope out the feasibility and effort involved in evaluating an agent offered by a software vendor, identifying the most practical path to reaching a credible evaluation
- Own the full stack of the core evaluation system, from admin & API surface to eval workflow dispatch
- Design evaluation system primitives that generalize across software verticals, so onboarding a new category benefits from reuse
- Distill the appropriate and repeatable processes in onboarding and maintaining integrations into repeatable AI skills or agents to increase evaluation velocity
- Keep tabs on emerging frameworks and techniques for agent evaluation which offer improvements to evaluations, and provide industry-accepted means to distribute eval results
- Collaborate with data science peers in their pursuit to develop rich and proprietary benchmarks
Lead a group of engineers and be a source of wider influence [20%]:
- You will facilitate the growth and mentorship of a core team of engineers, and be a source of technical leadership and subject matter expertise in evals for them
- You will also help socialize the use of evals in agent-oriented products built by the wider organization.
Minimum Qualifications:
We realize applying for jobs can feel daunting at times. Even if you don’t check all the boxes in the job description, we encourage you to apply anyway.
- 10+ years of professional programming experience in backend or full-stack environments
- 2+ years of experience directly managing engineers
- Expert-level proficiency in backend development using languages like Python, Java/Kotlin, Typescript/Javascript or Go; strong proficiency in associated backend frameworks like FastAPI or Node.js
- Direct experience creating evaluations (evals) against a customer-facing agent, leveraging agent trajectory trace data and rubrics to measure an agent’s task completion rate, accuracy, correctness, or policy adherence
- Direct experience using frontier models from OpenAI, Anthropic, or Google in LLM-as-a-judge applications
- Regular use of coding agent harnesses like Claude Code, Codex, Opencode, or Pi as part of the daily development workflow
- BS/BA degree in related field
What Can Help Your Application Stand Out:
- Experience with agent tool use via direct integration or as mediated by MCP servers
- Knowledge of STATE-Bench, tau2-bench or similar benchmark frameworks
- Experience using Playwright, browser-use, Chrome DevTools MCP or others to drive user flows in the browser
- Experience with durable execution frameworks & workflow solutions, like Temporal, DBOS, Cloudflare / Vercel Workflows, or similar; or experience with agent sandboxing using AWS E2B, Daytona, Cloudflare / Vercel Containers or similar
Our Commitment to Inclusivity and Diversity
At G2, we are committed to creating an inclusive and diverse environment where people of every background can thrive and feel welcome. We consider applicants without regard to race, color, creed, religion, national origin, genetic information, gender identity or expression, sexual orientation, pregnancy, age, or marital, veteran, or physical or mental disability status.
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How We Use AI Technology in Our Hiring Process
G2 incorporates AI-powered technology to enhance our candidate evaluation process. These tools may assist with initial application screening, skills assessment analysis, and identifying candidates whose qualifications align with specific role requirements. While AI technology supports our recruitment workflow, all final hiring decisions remain under human oversight and judgment.
Your Choice Matters: If you would prefer that your application be reviewed without AI assistance, you can opt out by entering your email address in the email entry field at the bottom of the Automated Processing Legal Notice. Choosing to opt out will not disadvantage your application in any way—we will ensure your materials receive a thorough manual review by our hiring team.
For additional details about how we handle your information throughout the application process, please review G2's Applicant Privacy Notice.
Compensation Range: $260K - $320K
Salary Context
This $260K-$320K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At G2, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($290K) sits 37% above the category median. Disclosed range: $260K to $320K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
G2 AI Hiring
G2 has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $175K - $320K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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