V-Nova
Integration Engineer - AI Pipelines
Get notified of more jobs like this · No spam, ever
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Overview
Joining V-Nova's Integration Team as a Software Engineer will give you the opportunity to work at the intersection of video engineering and artificial intelligence. You will help integrate V-Nova's video technologies multimodal workflows using Python to turn new ideas into practical, reliable systems.
The work will span offline video curation and large-scale dataset preparation through to real-time and cloud-based video inference. This will include building workflows, integrating codecs with AI and media frameworks, and exploring how LCEVC and VC-6 can reduce the amount of video data, decoding and processing required by AI applications.
The Integration Team's day-to-day work is dynamic. You will collaborate with codec engineers, AI specialists and external engineering teams, investigating unfamiliar systems and developing solutions that make V-Nova technology straightforward to evaluate and adopt. You are proactive, curious and comfortable contributing to wide-ranging technical discussions.
Responsibilities
- Develop and maintain production-quality Python integrations, tools and sample applications for V-Nova's LCEVC and VC-6 technologies within AI video workflows.
- Integrate codec SDKs into video curation, data preparation and inference pipelines, including workflows using technologies such as NVIDIA NeMo Curator, with either native integrations or via frameworks such as FFmpeg and GStreamer.
- Build and maintain video processing pipelines covering ingest, frame and clip extraction, transcoding, timestamps, keyframes, metadata and audio/video synchronisation.
- Connect video processing stages to computer-vision and multimodal AI models for tasks such as classification, detection, captioning, embeddings, retrieval and structured output generation.
- Prototype and benchmark codec-aware inference workflows, including the selective use of resolutions or regions of interest, and evaluate their impact on accuracy, latency, throughput, scalability and cost.
- Create automated tests, performance measurements and clear technical documentation that help colleagues, customers and partners reproduce and evaluate integrations.
- Collaborate with colleagues and external engineering teams to scope work, communicate progress and deliver integrations to production.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Qualifications
- Commercial software development experience using Python, including writing maintainable, tested and documented code.
- Practical video engineering knowledge and a basic understanding of how video streams work, including codecs and containers, frame extraction, timestamps, keyframes, audio/video synchronisation, transcoding and streaming fundamentals.
- Good problem-solving skills and confidence working across unfamiliar codebases, third-party libraries and APIs.
- Clear written and verbal communication, including concise progress updates and technical documentation.
- A degree in Computer Science, Engineering or a related discipline, or equivalent practical experience.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Desirable Experience:
- Experience with virtualisation and containerisation technologies, including Docker, and an understanding of building and managing portable, scalable environments.
- Experience working with AWS or similar services and deploying, managing or supporting applications in cloud-based environments.
- Experience with video curation or data preparation frameworks, or with building scalable batch and streaming data pipelines.
- Experience with multimodal AI, including LLM or VLM APIs, image and video understanding, embeddings, structured outputs, tool or function calling, and retrieval-augmented generation.
- Experience deploying and optimising AI inference workloads, with an understanding of performance considerations such as latency, throughput, scalability and cost.
- Direct development experience with FFmpeg or GStreamer.
- Experience with GPU-accelerated video or AI technologies such as CUDA, PyTorch, TensorRT, Triton Inference Server or NVIDIA DeepStream.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location