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At Forrester, we’re trusted to work on trailblazing, mission critical problems that business and technology leaders face today. That’s why we’re always looking to empower talented individuals to perform at their best every single day. We’re proud of our community of smart people and vibrant voices who come together to do what’s right by our clients and each other. Our success is driven by curiosity, courage and customer obsession. The confidence and drive to be bold at work. Join us and build an extraordinary future.
About This Role:
We’re seeking an entrepreneurial, customer\-facing technologist to help bring Forrester AI to market across our North America end\-user organization. Reporting into and partnering closely with the vice president of sales, end user, the AI Solutions Partner works shoulder\-to\-shoulder with sales managers, account executives, and account managers to build comprehensive client solutions that pair Forrester AI with our broader research, advisory, and consulting portfolio. This individual is the technical and commercial connective tissue between Forrester’s AI products and the buyers who need them: They demonstrate the technology, translate it into business value, identify client needs, package solutions, and help close deals.
The AI Solutions Partner is equally at home in front of a client and in cross\-functional working sessions with sales enablement, customer success, and product. This individual will help build the playbook, training, and field assets that allow Forrester’s go\-to\-market organization to sell AI confidently and at scale, and will help existing clients realize value from Forrester AI to drive retention. Success in this role requires someone who comes to life in front of customers, is genuinely fluent in modern AI, and has the drive to take a new technology service to market and bring its value to life.
Job Description:
- Partner with the vice president of sales, end user, and frontline sales managers to embed Forrester AI into account strategies across the North America book of business.
- Accompany account executives and account managers on early\-stage sales calls, discovery conversations, and executive meetings to demonstrate Forrester AI and articulate its value to business and technology buyers.
- Deliver compelling demos of Forrester AI to a wide range of audiences, from individual practitioners to C\-level executives, tailoring the narrative to each buyer’s role, industry, and maturity.
- Explain the technical underpinnings of Forrester AI\-related solutions, including how the underlying research corpus, large language models (LLMs), retrieval, and integration patterns work in an enterprise context.
- Uncover client needs through structured discovery and connect those needs to the right combination of Forrester offerings — both AI and non\-AI — including Forrester Decisions, advisory, consulting, and event services.
- Help account executives and account managers package Forrester AI into compelling, well\-priced solutions that reflect client value and Forrester’s commercial model.
- Assist sales teams during negotiations by addressing technical objections, clarifying scope, and helping structure deals that close.
- Partner with sales enablement to develop and continuously refresh sales training, demo scripts, objection\-handling guides, battlecards, and certification content focused on Forrester AI and the broader Forrester portfolio.
- Partner with customer success to develop client\-facing training and adoption materials that drive usage, value realization, and retention across the existing client base.
- Serve as a structured feedback loop into product leadership: Document field experiences, win/loss learnings, common objections, feature gaps, and competitive signals, and bring them back to the AI product team.
- Maintain a high cadence of internal coordination through standing meetings with sales, product, and customer success leaders, and document learnings in a way the broader organization can use.
- Stay current on the generative AI (genAI) landscape, including LLMs, the Model Context Protocol (MCP), agentic architectures, and machine learning techniques, and translate that fluency into client\-ready conversations.
Job Requirements:
- Proven experience in a customer\-facing technical sales, solutions consulting, sales engineering, or presales role, ideally in research, SaaS, or enterprise technology.
- Experience in designing and delivering software training, enablement content, or technical workshops for both internal teams and clients.
- Working fluency in genAI fundamentals, including LLMs, prompt design, retrieval\-augmented generation, and the MCP.
- Working knowledge of machine learning techniques sufficient to discuss them credibly with technical buyers and to identify the right Forrester response.
- Working knowledge of AI security, data privacy, and emerging AI regulatory considerations relevant to enterprise buyers.
- Familiarity with enterprise systems integration patterns and the deployment models that AI solutions typically follow within large organizations.
- Strong consultative discovery skills and the ability to translate technical capability into measurable client outcomes.
- Excellent presentation, demo, and storytelling skills, with the ability to adapt to audiences ranging from individual practitioners to C\-level executives.
- Strong organization, documentation, and time management skills, with the ability to operate across multiple deals and internal workstreams at the same time.
- A fast learner who is at ease with new technology and motivated to take a new product to market.
- A collaborative, team\-first orientation and a high degree of comfort operating cross\-functionally with sales, product, sales enablement, and customer success.
- Familiarity with enterprise procurement, security review, and vendor onboarding processes is a plus.
- Willingness to travel as needed to support client meetings, internal sessions, and Forrester events.
Skills And Competencies:
- Comes to life in front of customers; energized by demo, discovery, and executive conversations.
- Demonstrates urgency, a strong sense of ownership, and the drive to bring a new offering to market.
- Adapts communication style to the audience and listens actively to uncover real needs.
- Leverages data, examples, and proof points to secure support and commitment from clients and internal partners.
- Anticipates challenges, adapts to setbacks, and responds well to coaching and feedback.
- Pursues continuous learning, particularly across a fast\-moving AI landscape, and embraces diverse perspectives.
- Prioritizes work effectively to align with organizational goals and follows standard sales and enablement processes.
Builds trust through consistency, reliability, and strong rapport with both clients and colleagues.
Please note that the base salary range indicated here is inclusive of all applicable US geographies listed in this requisition. This salary range is based upon the position as described in the job listing. The offered compensation may vary within this range and is dependent upon the successful candidate’s primary work location, experience, training, education, and credentials.
Base salary range: $148,000 \- $222,000
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Let’s be bold, together.
Here at Forrester, we welcome people from all backgrounds and perspectives. Our aim is for all candidates to be able to fully participate in Forrester’s recruitment process. If you would like to discuss a reasonable accommodation, please reach out to [email protected] .
Forrester Research, Inc. is an Equal Employment Opportunity Employer. As a federal contractor, Forrester encourages veterans and individuals with disabilities to apply for employment.
Salary Context
This $148K-$222K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Forrester, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $148K to $222K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Forrester AI Hiring
Forrester has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, US. Compensation range: $222K - $222K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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