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About This Role
Usercentrics is a global leader in consent management, and now we're bringing that same mission of trust and control into the AI era. MCP Manager (MCPM) is our newest product: a governance and management platform for Model Context Protocol (MCP), the emerging standard that connects AI agents to business systems like Salesforce, Confluence, Slack, and GitHub.
As AI adoption accelerates, enterprises face a new and urgent challenge: how do you give teams the power of AI agents while maintaining visibility, security, and control over what those agents can access and do? MCP Manager is the answer, serving as the trust and control layer for AI agent connections.
This is a category-creation opportunity. MCP is being compared to the invention of the USB connector for AI: a universal standard that changes everything. We're looking for a Senior Sales Engineer who wants to be on the ground floor of defining that category and to become the technical voice customers trust when they put AI agents into production.
As our Senior Solution Engineer, you will own the technical side of every MCP Manager evaluation, from the first discovery call through a successful proof of concept and into onboarding. After the sale, you will partner with our Customer Success Manager to provide post-sales engineering support as customers expand their use of MCP Manager. You will partner with each of our Account Executives to win deals across a shared pipeline, lead security and architecture reviews with CISOs and IT teams, and build the demo environments, solution plans, and technical materials that help prospects see MCP Manager working against their own AI stack. When customers run into technical issues, you will investigate and resolve them yourself with modern AI coding tools, bringing in engineering only when needed.
You will sit within the MCP Manager engineering team, which gives you direct access to the people building the gateway and a clear path for turning field feedback into product improvements. The role exists now because buyers are moving from AI experiments to production agent rollouts, and they need a trusted technical guide who can show them how to govern those connections with confidence.
Your Tasks:
Technical Discovery & Demos
- Lead technical discovery with AI leads, engineering managers, and IT teams to map each prospect's AI clients, MCP servers, identity provider, and governance requirements.
- Deliver tailored product demonstrations that show MCP Manager governing the prospect's own tools and workflows.
- Verify compatibility between a prospect's AI ecosystem and MCP Manager before demos and trials, and raise any gaps with product and engineering early.
Proofs of Concept & Trials
- Support Account Executives as they scope each proof of concept with the prospect by identifying the technical blockers that need to be resolved before the PoC begins, such as compatibility of the MCP servers and clients in play, workspace access, and identity setup.
- Support customers through each proof of concept as they configure gateways, identities, and configure policies themselves, answering their questions in real time over Slack or in-product chat.
- Drive each evaluation to a clear technical decision on an agreed timeline and document the outcome for the deal team.
Security & Compliance Reviews
- Lead security questionnaires, architecture reviews, and vendor due diligence with CISOs, security teams, and compliance reviewers.
- Explain how MCP Manager handles authentication, data flow, logging, and access control in terms that security and audit stakeholders can act on.
- Maintain a library of reusable answers and security documentation, working with engineering and Legal to keep it accurate.
Post-Sales Support & Enablement
- Build and maintain demo environments, solution plans, reference architectures, and technical guides for the sales team and for prospects.
- Partner with our Customer Success Manager to onboard new customers with a documented configuration, and provide ongoing post-sales engineering support as they roll MCP Manager out more widely.
- Investigate and resolve prospect & customer technical issues as they arise, using Claude Code and other AI coding assistants to reproduce problems, inspect configurations and logs, and propose fixes before escalating to engineering.
Product Feedback & Documentation
- Log feedback, feature ideas, technical objections, and competitive signals from every customer conversation so product and engineering can act on them.
- Work closely with the Product Manager to prioritize customer needs, clarify requirements, and validate new features with the customers who asked for them.
- Update product documentation, guides, and troubleshooting articles based on what you learn from customer evaluations and support cases.
Requirements
- 3 or more years of experience in sales engineering, solutions engineering, solutions architecture, or a similar customer-facing technical role at a B2B software company.
- A track record of running technical evaluations and proofs of concept that convert into closed business, in close partnership with Account Executives, and experience supporting customers after the sale alongside Customer Success.
- Ability to support several Account Executives at once, running multiple evaluations in parallel and prioritizing your time across a shared pipeline.
- Strong working knowledge of APIs, authentication and authorization (OAuth, SSO, and identity providers), and SaaS configuration. You should also be comfortable reading technical documentation and troubleshooting integrations.
- Basic coding experience, and comfort using Claude Code and other AI coding assistants to investigate and resolve customer issues as they arise. You don't need to be a software engineer, but you should be able to read code, write simple scripts, and debug problems with modern tools.
- Experience supporting security reviews and vendor due diligence, and comfort discussing frameworks such as SOC 2 and HIPAA with security and compliance teams.
- Hands-on experience using AI assistants and agents in your own work, and real curiosity about how enterprises are adopting them.
- Ideally, experience with Model Context Protocol and AI gateways. Must have experience with AI agent tooling and a background in developer tools, security, or governance software.
- Excellent verbal and written communication, with the ability to explain complex technical ideas to engineers and business stakeholders alike.
- Comfort operating in an early-stage, category-creation environment where the playbook is still being written.
- Willingness to travel occasionally for customer meetings and industry events.
Don't meet every single requirement? Studies have shown that women and people of colour are less likely to apply to jobs unless they meet every single qualification. At Usercentrics we are dedicated to building a diverse, inclusive and authentic workplace, so if you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.
Salary Range: $194,400
The annual base salary range is determined based on a variety of factors, including geographic location, role-specific skills, relevant experience, and internal equity. Usercentrics is committed to transparent and equitable compensation practices.
Benefits
- 401(k) plan contribution with employer match
- 18 paid vacation days (PTO)
- Health insurance
- Personal development budget for every employee
- Hybrid workplace
- Online wellness sessions
- Team events including team buildings, happy hours, company breakfasts/lunches, in-person and virtual
- Refreshments: snacks, fruit, coffee, and drinks in the office
Why join usercentrics?
- Joining Usercentrics means becoming part of a fast-growing, diverse, and international team of tech enthusiasts and entrepreneurially-minded people who build our success story together.
- Company culture is important to us. We strive to continuously develop a positive, vibrant, and inspiring environment that enables everyone to thrive both personally and professionally.
- Get involved! We have plenty of initiatives and love seeing people from all departments enthusiastically participating in cross-department projects and shaping our future.
- Your work-life balance matters. We offer flexible working hours, hybrid working, and the possibility of workcations (in accordance with our company policy).
- We always remember to have fun, both in our day-to-day work and at our regular team events in our offices across Munich, Prague, Lisbon, Copenhagen, Odense, Buenos Aires, and New York, or online.
- You are our most valuable asset. We offer a personal development budget, job-related language courses, and much more to support your wellbeing and growth.
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Usercentrics, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Usercentrics AI Hiring
Usercentrics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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