Mid-Level Machine Learning Engineer

Cork

1 year ago

Applications closed

Related Jobs

View all jobs

Assistant Professor in Actuarial Data Science (T&R)

Assistant Professor in Statistical Data Science

Data Scientist/AI Engineer

Founding Machine Learning Engineer

Senior Dataops Engineer

Senior DataOps Engineer: Cloud Data Pipelines (Hybrid)

Hybrid

Reperio has partnered with one of the world's largest FinTech companies who are seeking a talented Mid-Level Machine Learning Engineer to develop and deploy ML models for real-world problems. The company have plans to expand their Data and AI team to supplement their ever-growing levels of global demand. The successful candidate's duties will include integration of ML solutions into products and optimisation of model performance.

Requirements:

Bachelor's or Master's in Computer Science, Engineering, or related field
2-4 years of ML experience
Proficiency in Python and ML libraries (TensorFlow, PyTorch)
Strong problem-solving skills
Experience with cloud platforms (AWS, GCP, Azure)
Knowledge of deep learning frameworks

Benefits:

Pension
Generous bonus scheme
Healthcare
Inclusive work environmentIf this role as a Mid-Level Machine Learning Engineer interests and suits you, then apply using the link below. If you require any further information, get in touch with Jamie Sadlier at Reperio.

Reperio Human Capital acts as an Employment Agency and an Employment Business

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

Industry Insights

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

Jan 18, 2026

Jobs Careers

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

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

Jan 13, 2026

Jobs

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Jan 5, 2026

Education Jobs

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

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.