▷ [3 Days Left] Machine Learning Engineer

Human Native Ltd
London
4 days ago
Applications closed

Related Jobs

View all jobs

▷ (3 Days Left) Drone Pilot for AI Training and DataCollection

▷ [Urgent Search] Associate Director, Global ProductMarketing

▷ Immediate Start! Registered Manager

▷ (Only 24h Left) Machine Learning Engineer, EnterpriseResearch London, UK

▷ Urgent! Solutions Architect

▷ [Urgent Search] AI Trainer for Chemistry (College DegreeRequired)

What is Human Native? At Human Native, we’re buildingan AI data marketplace that ensures creators and rights holders arefairly compensated for their work while providing AI developerswith high-quality, responsibly licensed training data. We believein building AI the right way - ensuring transparency, fairness, andaccessibility. This is a hard problem, and we need brilliant mindsto help us solve it. The Opportunity As an ML Engineer, you’ll helpus index, benchmark, and evaluate training datasets at scale. Yourexpertise with data, AI and ML training methodologies andevaluation techniques will advance the state of the art fordeveloping AI. You will work across: - Designing and developingbenchmarks that allow our customers to understand their value ofdata for training ML (quantifying dataset quality and biases). -Deploy these benchmarks by implementing end-to-end data evaluationpipelines to be run on different datasets and ML models. - Tools tovisualise, analyze, and understand the attributes of datasets basedon the evaluations. - Develop ML models to transform, clean andunderstand data. - Collaborating with cross-functional teams,including operations, software engineering, and product management,to integrate data evaluation tools and insights into productdevelopment. Key Responsibilities Engineering and Development -Build scalable, high performance systems to support our AI datamarketplace. - Optimise data pipelines to improve data discoveryand quality evaluation. - Maintain cloud based ML infrastructureand ensure system reliability. Collaboration and Product Thinking -Work cross functionally to translate business needs into technicalsolutions. - Advocate for pragmatic, simple solutions overunnecessary complexity. - Communicate trade-offs and engineeringdecisions clearly. Growth and Impact - Help to define theengineering culture and best practices as we grow. - Improvedeveloper experience by building internal tools and automation. -Ensure AI licensing remains fair, transparent, and responsible. OurIdeal Candidate Must Haves: - Hands on experience developing anddeploying ML models and ML data pipelines in production. - StrongStatistical Analysis & Data Evaluation, you’re comfortabledeveloping or learning to develop custom metrics, identify biases,and quantify data quality. - Strong Python skills for Data &Machine Learning, familiarity with PyTorch and TensorFlow. -Experience with distributed computing and big data — scaling MLpipelines for large datasets. - Familiarity with cloud-baseddeployment (such AWS, GCP, Azure, or Modal). - Experience in fastmoving AI, ML or high growth environments, such as startups,research labs, or AI-driven product teams. - Bachelor’s, Master’s,or PhD in Computer Science, Mathematics or a related field. Nice toHaves: - Experience with LLMs, NLP, or synthetic data generation. -Familiarity with Rust or C++ for high performance ML applications.- Experience working on search, ranking, or large scale dataingestion pipelines. - Experience working with AI data management,responsible AI, or large-scale dataset processing. Our Benefits - Afast-growing company with opportunities for career advancement andlearning. - Competitive salary + stock options. - Private medicalinsurance. - Generous holiday allowance. - Regular team offsites +social events. - A small but mighty team making a real impact. Ifyou don't meet 100% of the qualifications but are excited about therole and feel you could be a good fit, we encourage you to apply.Studies have shown that women and people from underrepresentedgroups are less likely to apply for jobs unless they meet everyqualification. At Human Native AI, we value diversity of thoughtand recognise that skills and experiences can be built in manyways. We look forward to hearing from you. Apply for the job Do youwant to join our team as our new Machine Learning Engineer? Thenwe'd love to hear about you! #J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.

Machine Learning Programming Languages for Job Seekers: Which Should You Learn First to Launch Your ML Career?

Machine learning has swiftly become a cornerstone of modern technology, transforming entire industries—healthcare, finance, e-commerce, and beyond. As a result, demand for machine learning engineers, data scientists, and ML researchers continues to surge, creating a rich landscape of opportunity for job seekers. But if you’re new to the field—or even an experienced developer aiming to transition—the question arises: Which programming language should you learn first for a successful machine learning career? From Python and R to Scala, Java, C++, and Julia, the array of choices can feel overwhelming. Each language boasts its own community, tooling ecosystem, and industry use cases. This detailed guide, crafted for www.machinelearningjobs.co.uk, will help you align your learning path with in-demand machine learning roles. We’ll delve into the pros, cons, and ideal use cases for each language, offer a simple starter project to solidify your skills, and provide tips for leveraging the ML community and job market. By the end, you’ll have the insights you need to confidently pick a language that catapults your machine learning career to new heights.