251 to 275 of 1,576 Machine Learning Jobs

Software Engineer supports the development, deployment, and scaling of Python-based backend services for a distributed, cloud-hosted embedded machine learning platform, requiring foundational knowledge in cloud architecture, scalable systems, and applied machine learning, including: Backend Development: Contribute to the refactoring of Python-based backend services … high-volume sensor data processing. Embedded & AI Integration: Support the integration of automated AI-based anomaly detection features within embedded platforms, including exposure to machine learning inference pipelines and environment-specific embedded development using Xcode. Quality Assurance: Participate in maintaining CI/CD pipelines via GitHub Actions ...

Company They are a large, data driven organisation operating at scale, with a strong focus on using advanced analytics and machine learning to inform decision making. The business invests heavily in its data platforms and engineering capability, creating an environment where robust MLOps practices are essential. Teams … engineering within production environments. Hands on capability with Palantir Foundry, including pipelines and operational workflows. Solid Python skills and experience supporting end to end ML lifecycles. Practical knowledge of CI/CD, containerisation and cloud based infrastructure. Confidence working with complex data systems and multiple stakeholders. How to Apply ...

analytical solutions that provide accurate, relevant, and actionable business insights. Apply a range of analytical techniques, from descriptive reporting through to advanced predictive and machine learning models. Coach and develop Data Analysts, enhancing both technical and professional capabilities in partnership with the Team Manager. Support the management … self-service analytics capability, promoting best practices in tools, methodologies, and data governance. Mentor team members in advanced analytics techniques, including: Predictive modelling Supervised machine learning Customer segmentation Scenario simulation and forecasting Collaborate with stakeholders across the business to identify opportunities where advanced analytics can drive business value. ...

scientific R&D capability. This is a rare opportunity to join a rapidly growing, venture-backed business that combines advanced sensor technology, AI/ML, data science and chemical sensing to build products capable of detecting and interpreting biological and environmental signals in ways not previously possible. The company … limitations and deployment suitability Working closely with hardware and engineering teams on sensor integration Collaborating with data scientists to interpret sensor outputs and improve ML models Helping turn early-stage concepts into scalable commercial products Supporting the development of the company's growing sensing and AI platform Influencing future hiring ...

scientific R&D capability. This is a rare opportunity to join a rapidly growing, venture-backed business that combines advanced sensor technology, AI/ML, data science and chemical sensing to build products capable of detecting and interpreting biological and environmental signals in ways not previously possible. The company … limitations and deployment suitability Working closely with hardware and engineering teams on sensor integration Collaborating with data scientists to interpret sensor outputs and improve ML models Helping turn early-stage concepts into scalable commercial products Supporting the development of the company's growing sensing and AI platform Influencing future hiring ...

ML Ops 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 … with clear SLAs, reproducibility guarantees, and observable behaviour. This is not a research role; it is a hands-on engineering role focused on making ML systems work reliably at scale. What Youll Work On ML lifecycle infrastructure Productionizing models: packaging, deployment, versioning, and rollback Designing CI/CD pipelines ...

Applied Data Scientists to join a cutting-edge programme, supporting UK intelligence clients. You will be part of a programme focused on artificial intelligence, machine learning, advanced analytics, and modern cloud-native technologies within a highly secure environment. What You'll Be Doing As an Applied Data Scientist … will: Work on complex challenges across AI, Natural Language Processing (NLP), and Machine Learning Build, train and deploy models using tools such as: PyTorch TensorFlow scikit-learn Apply modern data science techniques to real-world, mission-critical problems Collaborate with engineering teams to deploy scalable solutions using cloud ...

single source of the truth, and underpin all analytics activity. It will support reports and dashboards via the Data Marketplace, with advanced modelling and machine learning as internal capacity and capability allow. What you will do - Develop ETL pipelines using AWS Glue, Kinesis, and Step Functions - Manage … optimise large-scale data lakes with S3, Athena, and Lake Formation - Implement robust data quality processes using Glue Data Quality and machine learning models - Design event-driven architectures with EventBridge and Lambda - Ensure data security through Macie, KMS, and advanced encryption frameworks Key Responsibilities - Design and implement ...

Project Manager (Contract) – AI/Machine Learning Programme Hybrid (London, 2–3 days onsite) Up to £585/day (Inside IR35) Contract until March 2027 (likely extension) Start: ASAP Overview We’re seeking an experienced Project Manager to support the delivery of a machine learning proof … role to grow into wider AI transformation initiatives . What You’ll Be Doing Coordinate multiple stakeholder groups across diverse departments Manage integration between ML models and existing enterprise platforms Work with operational KPIs and dashboards at scale across a nationwide network Bring structure and clarity to environments where requirements ...

debugging non-deterministic AI systems. Advising stakeholders on the risks, limitations, and appropriate use cases for AI technologies. Determining when statistical models, traditional machine learning approaches, or deterministic logic are more suitable than generative AI. Driving engineering best practice across AI development, deployment, governance, and observability. Providing technical … years of technology industry experience with demonstrated technical leadership. Experience integrating AI capabilities into production applications and business workflows. Strong understanding of LLM architectures, machine learning models, and statistical methods. Practical experience building and deploying RAG solutions. Experience evaluating AI systems and improving reliability, safety, and performance. Strong ...

Data Scientists do this by listening to team members, understanding context and challenging business ideas. Data Scientists use diverse techniques - frequentist and Bayesian statistics, machine learning, exploratory and explanatory data analysis, causal inference, data visualization, monte carlo modelling, econometric analysis, etc. Such broad requirements call for the ability … analysis Run exploratory data analysis and turn it into questions which can be answered with analytical techniques Use simple analytics, statistical or causal inference, machine learning or any other techniques to answer questions and address problems Communicate results clearly and effectively Take care of unclear and ambiguous requirements ...

pivotal role in defining how some of the UK's largest organisations adopt and scale AI. From Generative AI and autonomous agents to machine learning and enterprise data platforms, you'll be at the forefront of designing solutions that move beyond proof-of-concepts into real-world impact. … architecture of complex AI solutions, working across discovery, design, implementation and scale. Lead the architecture of enterprise AI, Generative AI and Machine Learning solutions Design intelligent agent frameworks and next-generation AI-powered applications Define AI governance, security and Responsible AI standards Advise executive stakeholders on AI strategy ...

edge of AI compute architectures. This is an opportunity to play a key role in designing high-performance FPGA solutions that will power advanced machine learning and neural network applications. The Role You will be responsible for the design, implementation, optimisation and verification of FPGA-based systems focused … industry-leading performance and efficiency. Key Responsibilities FPGA design and development using VHDL and/or Verilog/SystemVerilog Implementation of neural network and machine learning acceleration architectures RTL design, simulation, synthesis and timing closure Performance optimisation of high-speed digital systems Integration of high-bandwidth interfaces such ...

ML Engineer (Stealth AI Company) About the company We are building a foundational intelligence platform that transforms fragmented, proprietary information into durable institutional intelligence - enabling organisations to reason faster, preserve context, and compound knowledge over time. We are starting with information-dense, judgment-heavy industries where decision-making under uncertainty … organisational knowledge evolves We are intentionally not: a workflow automation tool a chat UI on top of documents a standalone 'knowledge graph product' Graphs, ML, probabilistic reasoning, and human-in-the-loop systems are combined to solve a larger problem: How can organisations reason reliably over their own information ...

Applied machine learning, computer vision, object detection, image classification, model development and evaluation, data annotation and curation, training pipelines, performance optimisation, edge ML, MLOps, deployment and maintenance of ML solutions, Python development, Python-based ML frameworks, PyTorch, TensorFlow, sklearn, OpenCV, NumPy, feature development, code review, pytest, linting … hands-on contributor, working closely with experienced engineers to build reliable, high-performance systems across devices, cloud platforms, and applied ML, contributing to real-world, field-deployed solutions. You will take responsibility for defined components and projects, contributing to system design, implementation, testing, and deployment, while continuing to develop your ...

APPLIED ML SCIENTIST | REINFORCEMENT LEARNING Build Reinforcement Learning Systems For The Real World 📍 £80k – £110k 💰 London, Hybrid Why is this exciting? Because this sits at the convergence of several areas that are becoming increasingly important: Reinforcement Learning Physical AI Digital Twins & Simulation Federated Learning Edge … push the boundaries of what AI can actually do. Experience required: Strong hands-on experience with Reinforcement Learning (RL) Python and modern ML frameworks ( PyTorch, JAX, TensorFlow ) Experience working with time-series or sensor data Ability to turn real-world problems into practical ML solutions Comfortable taking models from ...

actively to codebases and architectural decisions Developing and deploying production-grade AI systems using Azure, Databricks and Generative AI tooling Owning the implementation of ML pipelines, APIs and data integration workflows Enforcing MLOps best practices, coding standards, version control and performance optimisation Collaborating cross-functionally with data scientists, product managers … Python proficiency (async programming, concurrency, multithreading) Expertise in RESTful API design, OpenAPI/Swagger documentation and API lifecycle best practices Hands-on experience with ML libraries: PyTorch, PySpark, scikit-learn, Hugging Face Transformers Proficiency in AI tooling: Azure ML, Databricks, MLflow, LangChain, LangGraph Proven experience with Git, unit testing, containerisation ...

curation, versioning, and synthetic data generation Work with distributed training systems (multi‐GPU/cloud) and integrate models into robotics pipelines Requirements PhD in ML/Robotics/Computer Vision or 2-5+ years of applied ML experience Strong background in deep learning (computer vision), state-estimation … core ML mathematics (probability theory, statistics, optimisation, etc) Proficiency in Python (required) and C++ (preferred for robotics integration) Experience with ML frameworks such as PyTorch (preferred), TensorFlow, or JAX Proven track record building production ML systems, not just research prototypes Strong software engineering fundamentals: modular design, testing, and version control ...

Lead ML Engineer - REMOTE We are Lennar Lennar is one of the nation's leading homebuilders, dedicated to making an impact and creating an extraordinary experience for their Homeowners, Communities, and Associates by building quality homes and providing exceptional customer service, giving back to the communities in which we work … Lennar is seeking a Machine Learning Engineer to own and evolve the infrastructure and surface mechanisms that take our data science and ML models from notebook to production. This is a key role on the Applied AI & Data Science team, sitting at the intersection of software engineering, ML ...

documentation to ensure high-quality deliverables Support model monitoring, debugging, and optimization in real-world applications Stay up to date with emerging AI/ML trends and tools, contributing innovative ideas to the team Your Profile Essential skills/knowledge/experience: (Up to 10, Avoid repetition) Proficiency in Python … applicable) Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field Academic or project experience involving ML models, NLP, or AI systems Exposure to cloud platforms (e.g., AWS, Azure, GCP) is advantageous Understanding of software engineering principles and development lifecycle Internship, coursework ...

research that supports both our applications and the broader scientific community Current areas of interest include large language models (LLMs), optimization methods for deep learning, reinforcement learning (RL), and generative models. Key Responsibilities Shape and communicate the strategic vision for research across the organization Oversee multiple high-impact … research projects and ensure alignment with organizational goals Drive breakthroughs in machine learning and artificial intelligence through innovative re-search Mentor and provide leadership to senior researchers and research teams Represent the team in external partnerships, collaborations, and policy discussions Influence organizational research priorities and contribute to policy ...

research that supports both our applications and the broader scientific community Current areas of interest include large language models (LLMs), optimization methods for deep learning, reinforcement learning (RL), and generative models. Key Responsibilities Shape and communicate the strategic vision for research across the organization Oversee multiple high-impact … research projects and ensure alignment with organizational goals Drive breakthroughs in machine learning and artificial intelligence through innovative re-search Mentor and provide leadership to senior researchers and research teams Represent the team in external partnerships, collaborations, and policy discussions Influence organizational research priorities and contribute to policy ...

Learning, or related field (or equivalent experience). 5+ years of relevant experience (1+ with PhD, 3+ with MS) as a data scientist, ML engineer, or applied AI developer delivering production-ready models and systems. Strong proficiency in Python and SQL, with experience owning the full data science stack … development frameworks (LangChain, Strands, Amazon Bedrock, AgentCore, or equivalent). Experience with experimentation frameworks (A/B testing, uplift modeling, multi-armed bandits, causal ML). Exposure to machine vision techniques (CNNs, transfer learning, embeddings) and NLP techniques (embeddings, transformers, prompt engineering). Understanding AI agent observability (evaluation ...

Lead high-impact data science initiatives end-to-end, including problem framing, methodology selection, experiment development, implementation partnership, and impact measurement. Build and deliver machine learning and reinforcement learning solutions to improve player engagement, retention, monetization, and operational outcomes. Lead the modeling framework for complex systems, guaranteeing … related quantitative field, or equivalent experience in practice. Experience: 5+ years of professional data science experience. You have delivered at least 3 data or ML products from problem definition to production deployment and monitoring. Demonstrated proficiency in clustering, predictive modeling, reinforcement learning, and Bayesian statistics. Hands-on experience ...

ML Ops 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 ...