Location: United Kingdom (Remote or Belfast, NI Office based)Position OverviewWe are seeking a Senior Machine Learning Researcher with deep expertise in computer vision and hands-on experience developing and training large scale models (cluster-based or distributed training). Your primary focus will be on object sensitive neural compression techniques for low-bandwidth streaming and robust video applications. In this role, you will have the autonomy to push the state of the art, with ample opportunities to publish in leading conferences and journals.This position offers the flexibility to work remotely anywhere in the United Kingdom, with periodic visits to our Belfast office if desired.Key ResponsibilitiesNeural Compression Research
- Conceptualize and prototype neural compression architectures (e.g. autoencoders, generative models) to enable high-fidelity image/video streaming under strict bandwidth constraints.
- Evaluate and refine models based on reconstruction quality, bitrate, and real-time performance metrics.
- Develop models that enable high fidelity server-side reconstruction of objects of interests like text, vehicles etc.
Computer Vision Model Development
- Build and test downstream vision applications on reconstructed streams like
object detection, classification, segmentation, scene text recognition etc. * Conduct end-to-end experiments, from data preprocessing to model deployment, ensuring solutions meet performance and reliability targets.Cluster-Based Training & Optimization
- Train large-scale CV models on multi-GPU or distributed setups, optimizing hyperparameters and resource usage.
- Profile training pipelines for efficiency gains (e.g., mixed precision, caching strategies), ensuring timely experiment cycles.
Edge Collaboration & Deployment
- Partner with edge engineers to tailor model architectures for resource-limited devices (ARM, Jetson, etc.).
- Support integration of on-device inference and adaptive streaming logic, balancing accuracy, latency, and energy constraints.
Research & Publication
- Stay current with cutting-edge ML and CV advancements, proposing novel research directions aligned with our project roadmap.
- Publish and present findings in top-tier journals/conferences (e.g., CVPR,
NeurIPS, ICCV), showcasing both scientific and practical contributions.Team Collaboration & Leadership
- Work closely with product management, data engineering, and fellow researchers to drive innovation and ensure project alignment.
- Mentor junior team members, promoting knowledge-sharing and best practices in research methodologies and code reviews.
Qualifications
- PhD in Computer Science, Electrical Engineering, or a related field, with a strong specialization in Machine Learning or Computer Vision.
- Hands-on experience training and deploying large CV models, including some cluster-based or multi-GPU experience.
- Solid background in neural compression, autoencoders, or related techniques; familiarity with real-time streaming constraints is a plus.
- Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow) and GPU-accelerated computing.
- Track record of peer-reviewed publications demonstrating innovative work in ML/CV.
- Strong communication and collaboration skills, with the ability to translate research findings into clear, actionable insights.
- Self-driven and comfortable taking initiative in a dynamic research environment.
What We Offer
- Competitive Salary
- Comprehensive Benefits: Private health insurance, employer-matched pension, generous paid leave, and more.
- Work Flexibility: Fully remote (within the UK) or hybrid at our Belfast office, supported by modern collaboration tools.
- High-Impact Research Environment: Access to advanced GPU clusters and opportunities to publish in top-tier venues.
- Innovative Culture: Be part of a multidisciplinary team tackling challenging problems in neural compression and computer vision, with real-world deployments.
Application ProcessInterested candidates should submit the following to [email protected] (via the via the ‘Apply’ button above) * A CV/Resume highlighting relevant expertise.
- A list of publications (or Google Scholar link).
- (Optional) Code samples or links to open-source contributions.
Competitive Salary
Read More