At a Glance
- Tasks: Join a startup to develop models predicting human attention and interaction in visual environments.
- Company: Exciting startup at the forefront of human-computer vision research.
- Benefits: Competitive salary, equity options, and remote-first work culture.
- Other info: Ideal for curious minds eager to tackle open-ended research challenges.
- Why this job: Make a real impact in cutting-edge research while shaping the future of AI.
- Qualifications: PhD in relevant field and strong Python skills required.
The predicted salary is between 60000 - 75000 £ per year.
Location: UK‐based, remote‐first (occasional London visits, 1 day a month)
Employment type: Full‐time
Salary: £60,000–£75,000 base
Equity: 0.1%–3.0% options
Visa sponsorship: Not available (UK Graduate Visa accepted)
We are helping a small startup hire a Research Scientist to work on applied research at the intersection of human attention, perception and spatial AI. Founding style role, join as their 3rd hire!
The role focuses on building models that predict, interpret and simulate how people attend to and interact with complex visual and spatial environments. You will work on real‐world problems that connect machine learning, computer vision and cognitive modelling, with a strong emphasis on hands‐on experimentation and practical impact.
This position is well suited to researchers interested in spatial AI, embodied AI, XR/VR, simulation, automotive systems, robotics or interactive 3D environments. Prior experience across all of these areas is not required.
What you will work on:
- You will help design and build models that reason about where people look, what they notice and how attention changes with task, context, motion, layout and semantics.
- Example work includes:
- Predicting human attention and gaze in 2D and 3D scenes
- Modelling attention during search, navigation, inspection and decision‐making
- Connecting attention with language, goals, objects and scene context using multimodal models
- Inferring human intent or priorities from gaze, movement and interaction data
- Developing evaluation frameworks for human‐like perception
- Adapting foundation models for spatial and human‐centred AI tasks
- Working with synthetic and real‐world data such as images, video, depth, 3D scenes, gaze and interaction logs
- Collaborating with engineers to move research prototypes towards robust systems
Responsibilities:
- Develop machine learning models for attention, perception, behaviour or intent prediction
- Design experiments, baselines, metrics and validation protocols
- Train and evaluate models using visual, spatial, temporal and multimodal data
- Read, reproduce and extend relevant research
- Work with 2D and 3D data representations including images, video, depth, point clouds and simulated environments
- Communicate research decisions clearly through notes, demos, presentations and code
- Contribute to research direction and technical roadmap
Essential requirements:
- PhD in a relevant field (e.g. machine learning, computer vision, robotics, HCI, cognitive science, neuroscience or graphics)
- Strong research track record (top‐tier publications, patents or substantial open‐source work)
- Hands‐on deep learning experience
- Strong Python skills and experience with PyTorch
- Ability to independently design, run and evaluate ML experiments
- Experience working with complex datasets and rigorous evaluation
- Genuine interest in human attention, perception or behaviour
- Clear communication skills and comfort working in a small, fast‐moving team
Useful experience (not required):
- Human attention, gaze modelling, visual saliency or cognitive modelling
- 3D computer vision, spatial AI, simulation, XR/VR or robotics
- Multimodal or temporal modelling
- Egocentric video, eye‐tracking or behavioural data
- Foundation model fine‐tuning or evaluation
- Research engineering and reproducible ML pipelines
You are likely to enjoy this role if you:
- Are curious about how humans perceive and interact with the world
- Are comfortable with ambiguity and open‐ended research problems
- Are technically strong and able to build and debug your own models
- Care about rigour, evaluation and evidence
- Enjoy working in a startup‐style, research‐led environment
- Want to turn research ideas into real systems
Founding Research Scientist - Human Computer Vision in Oxford employer: Harnham
Join a pioneering startup as a Founding Research Scientist in Human Computer Vision, where you'll be at the forefront of innovative research that merges human attention with spatial AI. Enjoy a remote-first work culture that values collaboration and hands-on experimentation, alongside competitive salary packages and equity options. With opportunities for personal and professional growth, this role is perfect for those eager to make a tangible impact in a dynamic and supportive environment.
StudySmarter Expert Advice🤫
We think this is how you could land Founding Research Scientist - Human Computer Vision in Oxford
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend relevant meetups or conferences, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, research, or any relevant work you've done. This is your chance to demonstrate your expertise in human attention, perception, and spatial AI, so make it shine!
✨Tip Number 3
Prepare for interviews by brushing up on common questions related to machine learning and computer vision. Be ready to discuss your past experiences and how they relate to the role. Practice makes perfect, so consider mock interviews with friends or mentors.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Tailor your application to highlight how your skills align with the role of Founding Research Scientist and let your passion shine through.
We think you need these skills to ace Founding Research Scientist - Human Computer Vision in Oxford
Some tips for your application 🫡
Show Your Passion:Let us see your enthusiasm for human attention and spatial AI! In your application, share why you're excited about this field and how your background aligns with our mission. A genuine passion can really make you stand out.
Tailor Your CV:Make sure your CV is tailored to the role. Highlight relevant experience, especially in machine learning, computer vision, or cognitive science. We want to see how your skills fit into the exciting work we’re doing!
Craft a Compelling Cover Letter:Your cover letter is your chance to tell your story. Explain how your research experience connects to the responsibilities of the role. Be clear and concise, and don’t forget to mention any hands-on projects you've worked on!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this amazing opportunity. Plus, it’s super easy!
How to prepare for a job interview at Harnham
✨Know Your Stuff
Make sure you brush up on the latest research in human attention, perception, and spatial AI. Familiarise yourself with key concepts and recent advancements in machine learning and computer vision. This will not only help you answer technical questions but also show your genuine interest in the field.
✨Showcase Your Experience
Prepare to discuss your past projects and how they relate to the role. Highlight any hands-on experience you've had with deep learning, Python, or working with complex datasets. Be ready to explain your thought process and the impact of your work, as this will demonstrate your ability to contribute to their research direction.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about the company's research goals, team dynamics, and how they envision the role evolving. This shows that you're not just interested in the position but also in how you can fit into their vision and contribute meaningfully.
✨Communicate Clearly
Since clear communication is essential in a small team, practice explaining complex ideas in simple terms. Whether it's discussing your research or presenting your findings, being able to articulate your thoughts clearly will set you apart from other candidates.
