Visual AI Analyst

V-Nova is a London-based technology company specialising in visual data architectures for AI, media and immersive applications. Its hierarchical, compute-aware data formats bring structure and intelligence to video, images and point clouds, enabling systems to access and process only the visual information they need. This can reduce data movement, decoding and compute across demanding visual workflows and make large scale AI processing more efficient.

V-Nova has contributed key IP to internationally standardised technologies including MPEG-5 Part 2 LCEVC and SMPTE VC-6 (ST 2117), and also develops PresenZ for volumetric and 6

DoF experiences. With a portfolio of more than 1,600 international patents, V-Nova combines deep-science R&D with highly optimised software products and integrations that bring its technology into real-world media, cloud and AI systems.

We are committed to the extraordinary and rely on talented, high-performing teams to solve difficult problems and deliver what we promise. Our culture values individual empowerment, excellence, intellectual curiosity and teamwork. Across research, engineering, product and commercial teams, we make active use of the latest AI tools to accelerate analysis, development, writing and experimentation. With ground-breaking technology, a fast-paced collaborative environment and ambitious growth plans, this is an opportunity to make a visible contribution to an emerging category of Visual AI infrastructure.

The Visual AI Analyst will join V-Nova's Strategic Analytics & Business Insights (SABI) team and work at the intersection of analytics, product development and business development. The role will help turn V-Nova's hierarchical visual data technologies into measurable performance advantages and compelling customer propositions for Visual AI workflows. You will work closely with Product, Engineering, Integration and Commercial teams to develop, validate and take to market Visual AI acceleration solutions based on V-Nova technologies such as VC-6, LCEVC and emerging AI solutions.

The work is deliberately varied: one week you may be analysing GPU benchmark logs and isolating a pipeline bottleneck; the next, building an AI-assisted dashboard, assessing caption or embedding quality, researching a new AI framework, or translating technical results into a partner facing business case. This is not a role for somebody who needs a tightly defined playbook. We are looking for a high-learning-velocity analyst who enjoys unfamiliar problems, can structure ambiguity, figures things out with limited supervision and is motivated by getting to a useful result.

Deep prior knowledge of video compression or Visual AI is not required; curiosity, analytical rigour and the ability to learn fast are more important.

• Designing, supporting and analysing benchmarks and experiments across visual AI training and inference workflows, quantifying the impact of V-Nova technologies on quality, latency, throughput, compute, storage and cost.
• Turning raw test artefacts into insight, spotting anomalies and bottlenecks; forming hypotheses; and working with engineering colleagues to validate root causes and optimisation opportunities.
• Building practical internal analytics tools such as dashboards, parsers, report generators, scripts and automations, using modern AI coding agents to extend your own capabilities and make experimentation faster and easier to interpret.
• Working with Product and Engineering to translate evidence into decisions: defining useful test plans and acceptance criteria, identifying optimal configurations, prioritising follow-up work and producing clear recommendations.
• Supporting V-Nova's Visual AI go-to-market and business development through use-case and ecosystem research, competitor analysis, market sizing, pricing and business-case work, and by turning technical evidence into concise partner and customer materials and integration priorities.
• Collaborating with Marketing, other internal teams and external partners to turn technical evidence into high-quality thought-leadership and market-facing content, including blogs, articles, social media communications, scientific publications and event materials.
• Owning the practical discipline around experiments and analysis: coordinating datasets and cloud GPU resources where needed, keeping results and artefacts reproducible and well documented, communicating progress clearly, and following issues through to resolution.

The…