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The Opportunity
Sitetracker is looking for a Senior Product Marketing Manager to help scale the go-to-market motion for Scout, our Agentic AI solution. Scout is gaining momentum in the market, and this role will sit at the intersection of Product, Sales, and Marketing to ensure we are positioning the platform effectively, supporting sales opportunities, and driving adoption across our customer base.
This is a highly technical and commercially focused Product Marketing role. You’ll need to understand the AI technology stack and translate complex capabilities into clear customer value—whether you’re helping position Scout in an active sales opportunity, developing messaging for a new capability, enabling the sales organization, or responding to a rapidly changing competitive landscape.
What You’ll Do
You’ll work directly with Sales to support active Scout opportunities, helping teams position the platform, communicate its value proposition, navigate objections, and engage executive stakeholders. At any given time, you may be partnering on 5–10 opportunities, giving you a direct connection to what customers are asking for and where our messaging is resonating.
You’ll own and evolve Scout’s messaging pillars as the product and AI market continue to move quickly. Working closely with Product, you’ll lead product marketing for new capabilities and translate technical functionality into differentiated messaging, sales enablement, and customer-facing value propositions.
You’ll partner across Marketing to develop campaigns that create demand and expand Scout adoption within our existing customer base. You’ll monitor execution and outcomes, identify where we can improve, and make recommendations based on what you’re seeing in the market and from customers.
You’ll stay close to the AI landscape, emerging competitors, and changing market sentiment to ensure our positioning remains differentiated. You’ll develop competitive and counter-positioning that equips Sales and provides actionable market insights back to the broader Product Marketing organization. You’ll also represent Scout at relevant customer and industry events, including speaking opportunities.
The Skills You’ll Have
Sales Opportunity Support & Enablement
- You have experience directly supporting Sales teams on active opportunities for AI, technical SaaS, or similarly complex technology
- You can translate technical capabilities into clear business value and confidently support objection handling
- You’re comfortable communicating with executive-level stakeholders and helping sellers navigate complex customer conversations
- You’ve helped create a repeatable sales motion around an emerging or rapidly evolving product
- You use insights from active deals to improve messaging, enablement, and the broader GTM strategy
AI Messaging, Positioning & NPI
- You understand the AI technology stack and can communicate technical concepts effectively to both technical and business audiences
- You have experience developing messaging, positioning, and value propositions for technical products
- You have partnered closely with Product to bring new products or capabilities to market and enable Sales around those launches
- You have helped shape the market narrative for a new or emerging technology category
- You can rapidly evolve positioning as the product, customer needs, and broader AI market change
GTM Campaigns & Adoption
- You have a track record of developing GTM campaigns designed to create demand or increase product adoption
- You understand how different GTM channels work together and can recommend the right approach based on the audience and objective
- You can monitor campaign execution and outcomes and use those insights to make recommendations
- You have built programs specifically designed to expand adoption of new products or capabilities within an existing customer base
- You connect campaign performance back to pipeline, adoption, and broader commercial outcomes rather than focusing solely on marketing activity
Competitive & Market Intelligence
- You understand the AI landscape and stay current on emerging technologies, competitors, and changing market dynamics
- You have experience developing competitive messaging and counter-positioning that clearly differentiates a product’s value proposition
- You can turn market and competitive insights into actionable information for Sales and Product Marketing
- You’ve operated in a fast-moving technology category where competitors and customer expectations evolve quickly
- You proactively identify shifts in the market and use those insights to influence positioning and GTM strategy
Within 90 Days, You’ll
- Build a strong understanding of Scout, its underlying technology, target customers, use cases, value proposition, and competitive landscape
- Establish strong working relationships across Product, Sales, and Marketing and begin directly supporting active Scout opportunities
- Assess existing Scout messaging, sales enablement, competitive positioning, and GTM programs to identify immediate opportunities to strengthen the motion
Within 180 Days, You’ll
- Become a trusted partner to Sales on active Scout opportunities, helping teams confidently position the platform and navigate customer objections
- Own and evolve Scout messaging and positioning as new capabilities are released and market dynamics change
- Establish a repeatable approach for bringing new Scout capabilities to market and enabling the field
- Partner across Marketing on campaigns designed to drive new opportunities and increase Scout adoption within the customer base
Within 365 Days, You’ll
- Built a scalable product marketing motion that enables Sitetracker to position and sell Scout effectively as the business grows
- Have established clear, differentiated Scout messaging that evolves alongside the product and the broader Agentic AI market
- Be a trusted source of customer, competitive, and market intelligence across Product, Sales, and Product Marketing
- Have contributed directly to increased Scout adoption and a stronger pipeline of sales opportunities through effective positioning, enablement, and GTM execution
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 Sitetracker, 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. Senior-level AI roles across all categories have a median of $229,300.
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.
Sitetracker AI Hiring
Sitetracker has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span London, England, UK, Bengaluru, Karnataka, India.
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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