AI Researcher (Computer Vision)
Applied-ML research seats open for whoever can take a messy real dataset and get a model to do something useful with it.
How to read this page - source, method & limits
Where this comes from
A self-reported, first-person account of one real role, authored by the person who held it. There are no automated data sources, scores, or predictions on this page - every statement is a human claim. Each role is checked by an “honesty lint” before it ships: it must name the part of its success you cannot copy (the unfair advantage) alongside the part you can, plus at least one fake wall and one concrete first step.
How it's meant to be used
Intended: as one honest worked example of how a hard-looking role was reached, to copy the replicable lever and the first move. Not intended: as a checklist, a guarantee, or a claim that this is the only way in. It is a sample size of one.
Assumptions & limitations
Written in hindsight, so it can over-credit what happened to work and under-count luck and timing. It's also survivorship-biased - you're reading the paths that worked. Treat the prerequisites as “what mattered here,” not “what is required everywhere.”
If an AI coach discusses this role
A local coach can talk through this page using a hidden brief. It is instructed to separate the replicable lever from the unfair advantage and to never promise the role or any outcome. Verify anything time-sensitive (deadlines, named programs, contacts) yourself - those drift.
What it really is
Building computer-vision workflows for automated strabismus assessment - gaze-tracking data, CNNs and ResNet, image classification in a clinical screening setting.
What you actually needed
- Can train and evaluate a vision model on a real, messy dataset
- Will learn just enough of the clinical domain to be useful
Fake walls (looked required, weren't)
- A medical or ophthalmology background - the lever was the ML and the data work
The proof-of-work
Working CV pipelines on real clinical gaze data.
The move
Brought a concrete computer-vision skill to a medical group that had the data but needed the modeling.
⚖️ The unfair advantage (named honestly)
Access to a Stanford medical group and its proprietary clinical datasets - a starting point most ML learners don't get.
The replicable lever underneath it
The lever is the modeling skill on messy real data, which you can prove on any public medical-imaging dataset (there are many) and then take to a domain group that has data but no modeler.
The climb
- 1
If you're you know some ML in theory
ship one CV project on a public real-world dataset
→ leaves behind: a model and an honest write-up
- 2
If you're you've trained on real data
find a domain group with data but no modeler and offer the skill
→ leaves behind: an applied research collaboration
- 3
If you're you're modeling real data for a group
own a pipeline end to end
→ leaves behind: an applied-ML research role
🌱 Do this week
Train an image classifier on a public medical-imaging dataset and write up what worked and what didn't.
Ask the coach
Dig into how this role actually gets reached: the proof-of-work, the move, and what to do if you don't have the unfair advantage.
I'll answer honestly about how this role gets reached. I will not promise an outcome, and I'll always separate the part you can copy from the part you can't. Tap a question or ask your own:
Runs on your own machine. No outcome is promised; this is guidance, not a guarantee.
No outcome is promised. This is the lever and the move, told honestly - the rest is the work.