Junior Computer Vision / ML Engineer

🚀 Project Description

Facie is an early-stage AI product working with face image analysis.

We are looking for a strong Junior Computer Vision / ML Engineer to build a reliable mechanism for measuring facial geometry from photos.
 

The first challenge is straightforward to describe but non-trivial to solve: given a face photo, accurately measure numerical parameters of different facial features, such as eye dimensions, mouth dimensions, nose geometry, jaw proportions, distances between facial landmarks, and other geometric characteristics.
 

This is a greenfield CV problem.

You will not receive a predefined model or a detailed list of experiments to execute. We expect you to explore several possible approaches, quickly validate them, discard what does not work, and find a solution that is good enough to move into the product.
 

At phase 1 - our engineering philosophy is simple:

20% of effort that delivers 80% of the result is often better than spending weeks searching for theoretical perfection.
 

✅ You are:
 

• Confident with Python
• Understand core Machine Learning concepts
• Have practical experience with Computer Vision OR can demonstrate serious interest in the field
• Can work with existing open-source models and libraries
• Comfortable reading unfamiliar code and quickly running existing projects
• Understand basic model inference and API concepts
• Able to turn an unclear problem into several testable hypotheses
• Comfortable when the first approach does not work
• Able to explain how you would validate whether an experiment actually works
• Focused on finding a practical solution rather than endlessly optimizing one approach
• Comfortable using Git and working with an engineering team

Commercial Computer Vision experience is not required.
 

If your primary background is general ML or software engineering, we would still like to talk if you can demonstrate genuine interest in CV through things such as:

• university or online CV courses
• pet projects
• GitHub projects
• Kaggle
• research or thesis work
• reproduced papers
• personal experiments
   

➕ Will be a plus:
 

• OpenCV
• PyTorch or TensorFlow
• MediaPipe or other face landmark solutions
• Experience with face detection, landmarks or segmentation
• Understanding of 2D / 3D geometry
• Experience reading and implementing research papers
• Docker
• Model serving or inference pipelines
• Experience exposing ML functionality through an API
• Experience designing reproducible ML experiments
• Experience with Cursor, Claude Code, Codex or similar AI development tools
   

♟️ What We Expect from You:
 

• Experiment-first thinking - generate several possible approaches instead of betting everything on the first idea
• 80/20 mindset - optimize for time-to-market and find the simplest solution that provides enough accuracy for the product
• Ownership - do not wait for someone to define every next experiment for you
• Validation mindset - always ask how we know that a result is actually correct, especially when perfect ground truth does not exist
• Fast iteration - prototype, measure, learn, discard weak approaches and move forward
• Engineering judgment - understand when ML is needed and when deterministic geometry or conventional code is a better solution
• AI adoption - actively use modern AI coding tools when they help you iterate faster
• AI skepticism - understand and verify AI-generated code instead of blindly trusting it
• Clear communication - explain what you tried, what failed, what worked and what you recommend trying next
   

🔑 Responsibilities:
 

• Research different approaches for measuring facial geometry from images
• Build quick prototypes and experiments
• Evaluate existing Computer Vision models and open-source solutions
• Compare multiple approaches using measurable criteria
• Design practical validation methods when perfect ground truth is unavailable
• Analyze failure cases and identify why particular approaches fail
• Select solutions based on accuracy, complexity and time-to-market
• Build a reproducible facial geometry measurement pipeline
• Document experiments, conclusions and technical decisions
• Prepare successful solutions so they can be integrated by Software Engineers
• Ideally, help turn successful prototypes into an inference service or API
• Proactively suggest the next experiments instead of waiting for detailed tasks
   

🤖 AI-assisted Engineering
 

We actively use modern AI development tools.

You are welcome to use Cursor, Claude Code, Codex or similar tools to write code, explore libraries, integrate models and accelerate experiments.

We do not care whether every line of code was written manually.

We do care whether you understand:

• what you are testing
• why you selected a particular approach
• whether the result can be trusted
• how you validated it
• what the generated code actually does
• what you would try next if the approach fails
   

🍩 What We Offer:
 

• Part-time or Full-time depending on the candidate
• Remote work
• A real Computer Vision problem with direct impact on the product
• Significant ownership of the CV direction
• Freedom to experiment with different technologies and approaches
• Access to paid AI development tools where needed
• Direct communication with the founder and engineering team
• Opportunity to build a Computer Vision system from the greenfield stage into production
• Opportunity to grow from Strong Junior toward independently owning an ML / CV product area
   

🗣 Recruitment Process:
 

Intro Interview → Live Problem-Solving Session → Small Practical Experiment → Final Interview
 

💻 Live Problem-Solving Session (Optional)
 

This is not LeetCode and not a theoretical ML exam.

You will receive a previously unseen Computer Vision problem and will be asked to think aloud and visualize your approach using a whiteboard, Paint, FigJam or a similar tool.

We want to understand:

• how you decompose an unclear problem
• how many reasonable approaches you can generate
• what you would test first
• how you prioritize experiments
• how you validate results
• what you do when the first few approaches fail
• when you decide that a solution is good enough to ship
   

🧪 Practical Experiment (Optional)

Selected candidates will receive a small Computer Vision experiment.

You can use:

• AI coding tools
• Google
• documentation
• open-source models
• existing libraries
• research papers

We are not testing your ability to code without assistance.
 

We are testing your ability to turn an ambiguous CV problem into a working and validated solution quickly.
 

The expected output is a small working prototype plus a short explanation of:

• approaches considered
• approach selected
• why it was selected
• how the result was validated
• discovered limitations or failure cases
• what you would try next with one additional day