Senior Applied Researcher

Job Description

Senior Applied Researcher

Want to build vision-language models that understand complex real-world environments

Youll join a small highly technical team working on foundational problems in multimodal AI focused on training models that can interpret reason and act on large-scale first-person video data.

Youll work directly with the Chief Science Officer shaping how models are designed trained and evaluated. The work sits at the intersection of VLMs long-context reasoning and real-world deployment.

The focus is on building systems that move beyond static perception towards temporal understanding activity recognition and higher-level reasoning across dynamic environments.

Your work will centre on:

• Designing and training VLMs on large-scale video datasets
• Developing post-training approaches including SFT RLHF and parameter-efficient tuning
• Building scalable training and evaluation pipelines
• Exploring long-context and temporal modelling
• Designing efficient systems across edge and server-side inference
• Defining benchmarks for spatial and behavioural understanding

Youll bring strong experience training deep learning models ideally transformer-based along with hands-on work in vision language or multimodal systems.

Experience with large datasets model optimisation or deploying models into production environments will be valuable. Exposure to video data or long-context modelling is particularly relevant.

This is a team that values speed ownership and first-principles thinking. Youll be working on open-ended problems with real-world impact with the freedom to explore and define approaches.

Compensation: Highly competitive salary equity
Location: San Francisco onsite

If youre interested in building multimodal systems that operate in real-world settings and want to join a well-funded highly skilled research team please apply now!

All applicants will receive a response.