101 to 121 of 121 Machine Learning Engineer Jobs

White Collar Factory (95009), United Kingdom, London, LondonStaff Software Engineer - Machine LearningAbout this roleWe re on a mission to transform the way we use data and AI to service our customers and drive efficiency across the business.Do you love shaping the technical landscape and driving innovation across ...

Overview The Job title: Machine Learning Prototyping Engineer The Project duration: 6 months The Project location: UK ( London ) Working pattern of the role (Hybrid , Remote, full time onsite): Hybrid ( 3 days WFO ) Responsibilities Take loosely defined problems and turn them into proofs of concepts (PoCs) within days. ...

innovation by joining a leading AI research firm, building ML models that power products and drive decision-making at scale. Build, train, and deploy machine learning models using Python, TensorFlow, PyTorch Work on supervised, unsupervised, and reinforcement learning problems Clean and process large datasets to extract insights ...

About Mistral At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life. We democratize AI through high-performance, optimized, open-source and cutting-edge models, products and solutions. … pipelines and building robust tools. Interface cutting‐edge research with production: integrate checkpoints, streamline evaluation, and expose APIs. Conduct experiments on the latest deep‐learning techniques (sparsified 70 B + runs, distributed training on thousands of GPUs). Design, implement and benchmark ML algorithms; write clear, efficient code ...

Engineer Role Overview Were hiring an ML Ops Engineer to own the reliability, scalability, and operational integrity of our machine-learning systems in research & production. This role sits at the intersection of data engineering and ML infrastructure: youll design and operate data pipelines that feed models ...

task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. As Technical Lead, Machine Learning, you own the execution layer of A1’s intelligence. You translate research direction into reliable, scalable, production-grade ML systems. This role … Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. Make pragmatic trade-offs and ship improvements quickly, learning from real usage. Work under real production constraints: latency, cost, reliability, and safety Outcomes Research and models reliably translate into production-ready solutions with ...

APIs, data pipelines, SQL, and cloud-based data handling. Experience developing lightweight applications such as APIs, dashboards, notebooks, or simple frontends. Good understanding of machine learning and statistical/mathematical fundamentals. Experience working independently in fast-paced and rapidly changing environments. Strong problem-solving, analytical thinking, and experimentation ...

underrepresented groups, and neurodivergent candidates. We offer reasonable adjustments throughout the hiring process and are dedicated to creating a supportive, accessible environment for everyone. Machine Learning Ops Engineer Location: Ersk... WKCL1_UKTJ ...

underrepresented groups, and neurodivergent candidates. We offer reasonable adjustments throughout the hiring process and are dedicated to creating a supportive, accessible environment for everyone. Machine Learning Ops Engineer Location: Ersk... ZIPC1_UKTJ Company DXC Qualifications Senior (5+ years of experience) #J-18808-Ljbffr ...

environment where creativity meets technical challenge, take pride in craft, and collaborate closely with a small team building frontier systems. We are seeking a Machine Learning Systems & Infrastructure Engineer to build and own the systems that turn raw real‐world data into trained world models and reliable ...

architectural decisions. Support senior team members in identifying and addressing data science opportunities. Required Skills & Experience Proven experience in MLOps or DevOps roles within machine learning environments. Strong programming skills in Python, with hands‐on experience in PySpark and SQL. Deep understanding of ML lifecycle management … Join Us? Work on impactful AI projects with real‐world applications. Be part of a collaborative and forward‐thinking team. Access to continuous learning and development opportunities. Flexible working arrangements and a supportive work culture. Benefits Competitive compensation. Pension scheme. DXC Select – Our comprehensive benefits package (includes private health ...

Role: ML Engineer Type: 12 months Contract Location London, UK(2 days per week) We are looking for a high-caliber AI Engineer to join a global leader in the Insurance and Fintech space. You won't just be building chatbots; you will be designing and orchestrating Autonomous ...

Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. Make pragmatic trade-offs and ship improvements quickly, learning from real usage. Work under real production constraints: latency, cost, reliability, and safety Requirements Strong background in deep learning and transformer-based architectures. … optimization, including memory efficiency, quantization, and mixed precision. Comfort owning ambiguous, zero‐to‐one ML systems end‐to‐end. A bias toward shipping, learning fast, and improving systems through iteration. Ideal Experience Experience with LLM inference frameworks such as vLLM, TensorRT‐LLM, or FasterTransformer. Contributions to open‐source ...

What you'll be doing: Have a critical role in architecting, implementing, and maintaining production‐grade, low‐latency ML services for ranking models, recommendation algorithms, and forecasting methods. Collaborate with data scientists, product managers and ...

Highly desirable salary at a quickly scaling startup A well-backed deep tech startup is hiring an ML Engineer to work on some genuinely unsolved problems in applied computer vision. The team is small, senior, and comes from a mix of top-tier research labs, global tech companies … clean benchmark” and more “figure it out from 50 examples and make it work on-device.” What they need Strong PyTorch and clean deep learning engineering habits Solid understanding of modern architectures – transformers, VLMs, and beyond Grounding in classical signal processing and CV fundamentals Experience owning projects ...

expertise and new ideas, maintaining the high standards required to build and scale a national healthcare communication platform. As a Staff ML/AI Engineer, you'll operate across multiple product and platform teams – diving deep into selected ones to help them achieve great results with AI and ML. … path forward is unclear. You should apply if... You have deep expertise across a range of ML techniques (e.g. Transformer‐based NLP, Deep Learning, Tree‐based methods, Bayesian modelling) and the judgment to select solutions that balance theoretical soundness against engineering practicality. You have a proven track record ...

Our Mission Human time and effort is the rarest thing in the universe. The fact that so many people hate what they do for work is a disaster on so many levels. How many people ...

confidence systematically. Current role demand (expressed with variety) SynergisticIT commonly supports hiring pipelines for roles such as junior software programmer, Java full stack engineer, Python/Java developer, DevOps/cloud engineer, plus data-track roles like data analyst, BI analyst, data engineer, data scientist ...

Turn a Tech Layoff or a Career Gap Into a Reset for a Better Career or Laid Off in Tech? Rebuild Momentum With a Placement Process or Returning to Tech After a Break? Worried About ...

You have built real AI systems that ship, break, and get fixed under pressure. You care less about model demos and more about decisions that move capital. You want your work used daily by professional ...

You think in time series, signals, and regimes. You care about insight quality, not academic purity. You want your models tested by markets, not papers. If you dislike messy data and real-world constraints, this ...