Machine Learning Engineer, Video Quality Analysis

Menaalliances
London
2 months ago
Applications closed

Related Jobs

View all jobs

Embedded Linux Engineer

Machine Learning Engineer

Machine Learning Engineer - Defence Sector - Cambridge

Machine Learning / Computer Vision Engineer – Data Scientist

Lead Machine Learning Engineer

Lead Machine Learning Engineer

Machine Learning Engineer, Video Quality Analysis

London, United Kingdom | Posted on 11/25/2024

We're thrilled to present an incredible career opportunity! We're recruiting on behalf of a renowned multinational company based in the United Kingdom, seeking a Machine Learning Engineer, Video Quality Analysis.

What you’ll do:

As a key contributor, you will help develop innovative solutions by designing and implementing advanced algorithms to detect defects and evaluate overall video quality. This role involves leveraging the latest technologies, including foundational models, transformer-based architectures, masked autoencoders, image processing, image analysis, computer vision, and machine learning. A primary focus will be on optimizing these algorithms to ensure they deliver accurate, efficient, and reliable results in near real-time.

Key responsibilities:

  • Develop detectors using advanced computer vision and machine learning (ML) techniques.
  • Optimize solutions to ensure low latency and cost-effective operation at scale for customers.
  • Apply deep knowledge of the Machine Learning lifecycle, including model training, optimization, experimentation, and maintenance.
  • Leverage core SDE computer science skills combined with a strong understanding of statistics and math to analyze algorithmic performance.

Requirements

BASIC QUALIFICATIONS

  • Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.
  • Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design.
  • Master's degree in Machine Learning, Applied Mathematics, Operations Research or a related field, or equivalent work experience.

PREFERRED QUALIFICATIONS

  • Bachelor's degree in computer science or equivalent.
  • Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
  • Experience with developing and deploying Machine Learning Operations (MLOps) at scale.
  • Experience with large scale foundational models and transformer-based architecture (GenAI).

Immigration support:The company provides support for your immigration process to the United Kingdom.

Competitive Salary:Enjoy a competitive salary package reflective of your skills and experience.

Global Experience:Gain international experience by working with a diverse team in a dynamic region.

#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.

Shadowing and Mentorship in Data Science: Gaining Experience Before Your First Full-Time Role

How to Find Mentors, Build Industry Connections, and Hone the Skills Needed in a Fast-Evolving Field Introduction Over the past decade, data science has grown from a niche academic discipline to a pivotal function driving decision-making in businesses of all sizes. With an array of applications—from predictive analytics and natural language processing to recommender systems and computer vision—data science offers an enticing career path for analytically minded professionals. However, as the field expands, so too does the level of competition. Employers seek not just theoretical knowledge but also real-world experience and robust problem-solving skills. That’s where shadowing and mentorship become game-changers for early-career data scientists. These hands-on learning opportunities provide an unmatched window into the workflows, tools, and soft skills you’ll need to excel in a professional environment. Whether you’re still completing your studies, fresh out of a bootcamp, or transitioning from another career, working closely with experienced data scientists can significantly shorten your learning curve and help you stand out when applying for your first full-time position. This article explores how to find mentors who align with your goals, the best ways to engage in shadowing opportunities, and practical tips for showcasing your growth as a mentee. From clarifying the nature of data science roles to leveraging online networks, you’ll discover how to position yourself as a candidate poised to solve complex challenges and drive data-driven innovation.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.