▷ 3 Days Left! Machine Learning Engineer (UK)

Coram AI
London
8 months ago
Applications closed

Started in 2021, Coram.AI is building the bestbusiness AI video system on the market. Powered by thenext-generation video artificial intelligence, we deliverunprecedented insights and 10x better user experience than theincumbents of the vast but stagnant video security industry. Ourcustomers range from warehouses, schools, hospitals, hotels, andmany more, and we are growing rapidly. We are looking for someoneto join our team to help us scale our systems to meet the userdemand and to ship new features. Team you will work with Founded byAshesh (CEO) and Peter (CTO), we are serial entrepreneurs andexperts in AI and robotics. Our engineering team is composed ofindustry experts with decades of research and experience from Lyft,Google, Zoox, Toyota, Facebook, Microsoft, Stanford, Oxford, andCornell. Our go-to-market team consists of experienced leaders fromVerkada. We are venture-backed by 8VC + Mosaic, revenue-generating,and have multiple years of runway. Being part of our team meanssolving interesting problems at the intersection of userexperience, machine learning and infrastructure. It also meanscommitting to excellence, learning, and delivering great productsto our customers in a high-velocity startup. The role We are hiringa Machine Learning engineer. - Take an existing open-source Pytorchmodel, fine-tune, productionize them in C++ runtime, and optimizefor latency and throughput. - Take an open-source model andfine-tune them on our in-house data set as needed. - Designthoughtful experiments in evaluating the tradeoffs between latencyand accuracy on the end customer use case. - Integrate the modelwith the downstream use case and fully own the end metrics. -Maintain and improve all existing ML applications in the product. -Read research papers and develop ideas on how they could be appliedto video security use cases, and convert those ideas to workingcode. Requirements - You should be a good software engineer whoenjoys writing production-grade software. - Strong machine learningfundamentals (linear algebra, probability and statistics,supervised and self-supervised learning). - Keeping up with thelatest in deep learning research, reading research papers, andfamiliarity with the latest developments in foundation models andLLMs. - (Good to have) Comfortable with productionizing a Pytorchmodel developed in C++, profiling the model for latency, findingbottlenecks, and optimizing them. - Good understanding of dockerand containerization. - (Good to have) Experience with Pytorch andPython3, and comfortable with C++. - (Good to have) Understandingof Torch script, ONNX runtime, TensorRT. - (Good to have)Understanding of half-precision inference and int8 quantization.What we offer - Company equity % in an early-stage startup.#J-18808-Ljbffr

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.

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

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

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.