Machine Learning Engineer - Onsite

Only Coders
Oxford
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Title:Machine Learning EngineerOnsite

Location:Oxford

Workform:Fullyonsite(5x aweek onsite)

Salary:

  • Entrylevel:35000 to 50000 per year.

  • Midlevel:50000 to 70000 per year.


Ourclientspecializes in automating salesengagement through AIdriven tools that research prospects and craftpersonalized messages significantly enhancing outreach efficiency.Byleveragingadvanced algorithms it helps sales leadersby allowing them tofocus on closing deals rather thanspending time on tedious tasks like email and LinkedIn outreach.The AI continuously learns and improves its performance based onuser feedback aiming to drive higher engagement and conversionrates.

Responsibilities:

  1. Developingand deploying MLmodels: Buildingtraining and deploying machine learning models toproduction.

  1. Softwaredevelopment:Writing clean efficient and maintainable code to support MLapplications.

  1. Datapreprocessing:Cleaning and organizing data to make it usable for machine learningmodels.

  1. Modeloptimization:Finetuning algorithms for better performance andscalability.

  1. Collaborationwith crossfunctionalteams: Workingwith data scientists software engineers and product managers tointegrate ML models intoapplications.




Requirements

RequiredSkills:

  • Programming:Proficiencyinlanguages such as Python Java orC.

  • MLFrameworks:Experience with frameworks like TensorFlowPyTorchorScikitlearn.

  • SoftwareEngineering:Strong foundationin software engineering principles andpractices.

  • DataManagement:Skills in handling large datasets and using databases like SQL orNoSQL.

  • StatisticalAnalysis:Understanding of statistical methods and their application in modeldevelopment.



Responsibilities: Furthering the development and evolution of ReactNative mobile apps. Aiding the existing development team inadvancing main SaaS products. Producing code for change requests tothe main SaaS applications and mobile apps. Collaborating with thetech workforce and the wider commercial team. Essential Skills andRequirements: Bachelor's degree in Computer Science or a relatedfield such as Software Engineering, Information Technology, orsimilar. Fluency in English language (Must be native speaker or C1or C2 level). Certifications in web and mobile app development,programming languages (like JavaScript and PHP), and softwaredevelopment methodologies (like Agile) would be beneficial. Highliteracy in React Native mobile app development (Android and iOS)Strong back-end PHP (enable seamless integration and collaborationwith the existing development team's expertise in Laravel andPHP-based SaaS products). Additional: Laravel, MySQL, JavaScript,jQuery, HTML 5, CSS, Git. Other beneficial skills: Linuxenvironments, UI Frameworks (e.g., Bootstrap), MongoDB experience.Excellent communicator and problem solver, time management, andcollaborative approach.

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.