Enterprise Sales Executive

Wilson Grey
Nottingham
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Manager, Forward-Deployed Data Science

Data Analyst - Fintech SaaS Game Changer. Hybrid

Data Analyst - Fintech SaaS Game Changer.

Staff Data Engineer

Senior Data Engineer/ PowerBI

Senior Data Engineer/ PowerBI

Enterprise Sales Executiveopportunity with a fast-growing AI startup. This is a remote role based in the UK.


  • Have you sold a highly technical SaaS or AI product?
  • Do the personas you've sold to include Data Scientists, Heads of Engineering and other technical stakeholders?
  • Have you exceeded ARR targets of £1mil?


If so, you will be a great fit for this role.


This position requires an experienced enterprise salesperson who has sold highly technical SaaS products or complex platforms, ideally with AI. You are someone who is skilled in guiding clients through cutting-edge technology and smashing your own sales targets.


Our client is an innovative tech company at the forefront of artificial intelligence transformation, empowering enterprises to unlock new possibilities through advanced AI and computer vision solutions.


As an Enterprise Sales Executive, you'll be a critical bridge between technical customers and our client’s AI solutions. You will take on a 360 sales role and directly engage with clients to understand their needs, deliver technical insights, and maximize the value of the platform.


This role combines sales skills, hands-on technical support and proactive customer education, enabling you to create a real impact for businesses adopting AI technology.


About the role:

  • End-to-end sales initially, from identifying prospects, lead generation, through demos, managing the deal and closing (you will supported by an SDR as the team grows)
  • Communicate directly with clients to understand their needs, propose solutions based on our client’s technology, provide technical guidance, and maximize platform benefits
  • Conduct meetings remotely and in person when appropriate
  • Run technical demos tailored to address customer challenges and goals, and provide sales team training on demos
  • Develop resources, conduct webinars, and create presentations and videos to inform customers about the client’s AI platform
  • Collaborate with R&D and Account Executives, and report on customer needs, market trends, and new product opportunities


About you:

  • Several years in technical B2B enterprise sales or a commercial sales engineer role selling to enterprises
  • Bachelor's degree, preferably in a technical field, or equivalent experience
  • Recent experience in a tech startup or high-growth scale-up
  • You enjoy the entire sales process, from initial prospect research through to closing deals
  • Demonstrable track record of closing deals and hitting sales targets of £1mil+ ARR
  • Technical background or have sold a complex product to a technical audience
  • Based in the UK full-time


On offer:

  • Base salary of £85k - £110k + Double OTE
  • Health Insurance
  • Gym membership
  • Flexible, remote working

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.