Interested in this AI/ML Engineer role at GLG Srl?
Apply Now →About This Role
This is the technical lead on go-to-market for AI Solutions, GLG's new human-data business. You build the researcher relationships, own every technical conversation, and design work we can deliver.
The role
AI Solutions puts GLG's expert network to work on AI training data and expert evaluations for frontier labs. Our aim is to lead the top of the market: high-stakes professional domains.
You are the GM's partner in the market and share one revenue target. The GM runs the commercial side of each deal. You run the technical side: the relationship with the researcher, the quality and measurement discussion, and the design of the program. You are also a hunter, building pipeline through the research community.
What you'll own
- Researcher relationships. Build direct relationships with researchers, post-training teams and eval leads before there is an RFP. Bring back what labs are buying and where current vendors fall short.
- Source opportunities through those relationships and in the field: events, conferences and dinners where researchers spend time.
- Technical discovery. Find which capability a lab is trying to improve. Use GLG experts to surface where a model fails in a domain and turn that finding into a pilot.
- Program design. Define the task, rubric, output format and difficulty target so the work measures what the lab cares about.
- Panel design. Decide with the Strategic Project Lead who qualifies for the expert panel and how it is calibrated. You own it together and sign off with each other.
- Quality methodology. Own the QA conversation and the math behind it: gold sets, agreement metrics, sampling and acceptance thresholds. Go head-to-head with researchers on it.
- Pilot to program. Set the success criteria for each pilot and help convert it into a production contract.
- Write the technical sections of bids and build reusable materials so every sale is easier than the last.
- Credibility strategy. Decide with the GM and Delivery which benchmarks, evals and other proof GLG needs to win its domains.
How you work with the team
The GM owns pricing, terms and the close. You own the technical promise we make to the client. A Strategic Project Lead co-owns scoping with you, then sources the experts, staffs the panel, pressure-tests the collection approach, and owns delivery with the VP of Operations.
As we add sellers, you become their technical counterpart on every deal, or move into a selling role of your own.
Who we're looking for
- Technical depth first. Credible with a frontier-lab researcher, well beyond "AI fluent" in the enterprise-sales sense.
- Has designed, scoped or delivered human-data, evaluation or benchmark work for AI labs.
- Knows the work: expert-authored evals, rubric design, preference data and post-training, in domains such as financial services, life sciences, trust & safety, etc.
- Strong on the statistics of quality: agreement metrics, gold sets, sampling and acceptance thresholds.
- Comfortable getting hands-on with model outputs and eval results.
- A hunter's instinct: you build your own relationships, want to share a revenue number, and like being in the field.
- Comfortable building in ambiguity on a founding team.
Likely backgrounds: a solutions engineer at a human-data vendor, a technical project lead who ran lab programs and wants to move to the front of the deal, or an evaluation specialist who wants to work with customers.
Why this role
- The supply others lack. Sourcing senior experts is the hard part of this market. GLG's expert network is the business we are built on.
- A founding seat. You write the playbook, shape the offering, and set the standard for who we hire next.
- Direct access. You work with lab researchers and with GLG leadership, with no layers in between.
What we offer
Compensation: This role offers a competitive compensation package, consisting of base salary and a performance-based bonus.
The anticipated hiring base salary range for this role is: $150,000 - $200,000 USD
GLG is committed to fair and equitable compensation practices. Actual compensation is based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. Certain roles may also be eligible for incentive compensation.
Benefits: All U.S. GLGers also have access to benefits such as:
- Comprehensive medical, dental and vision coverage effective on your first day of employment
- Flexible paid time off. No pre-determined limits on vacation time, plus 10 company holidays
- 401(k) and Roth 401(k) plans with an employer match (subject to annual limits & vesting)
- Tuition reimbursement program for eligible courses including language skills courses
- Paid parental leave, adoption and surrogacy reimbursement
- Free wellbeing support with the Calm app, Maven and EAP, and free long-term therapy & counselling assistance through Pathways
- Other work perks and benefits available based on final job location
About GLG / Gerson Lehrman Group
GLG is the world's leading platform for trusted human expertise. We connect global decision-makers—from hedge fund managers and private equity partners to strategy leaders at Fortune 500s—with the specific, authoritative voices required to answer their most critical questions.
At GLG, you are an extension of our clients' core teams. You will work at the intersection of industries and global markets, navigating high-stakes challenges across the full spectrum of their strategic initiatives. Operating within the industry's most trusted research environment, you'll help our clients capture the nuanced perspectives that drive smarter, faster business outcomes.
We are a global team of pragmatic problem-solvers who mirror the intensity of the markets we serve. If you are driven by intellectual curiosity and a bias toward action, join us in reinventing the industry we invented.
Gerson Lehrman Group, Inc. ("GLG") is an equal opportunity employer and will not discriminate against any employee or applicant on the basis of age, race, religion, color, marital status, disability, gender, national origin, sexual orientation, veteran status, or any classification protected by federal, state, or local law.
Salary Context
This $150K-$200K range is below the median 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 GLG Srl, 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 in Demand for This Role
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 ($175K) sits 17% below the category median. Disclosed range: $150K to $200K.
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.
GLG Srl AI Hiring
GLG Srl has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.