AI / MLSMBEnterprise

Computer Vision Engineer

[Company Name] is hiring a Computer Vision Engineer to design and deploy systems that extract meaningful information from images and video. You will work on the full lifecycle from data collection and model training to production deployment and optimization. This role is for engineers who are passionate about pushing the boundaries of visual AI and delivering real-world impact.

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Key Responsibilities

Design, train, and evaluate computer vision models for detection, segmentation, and classification tasks
Build and manage data pipelines for image and video preprocessing and annotation
Optimize model performance for latency, accuracy, and resource constraints
Deploy models to production using containerized services or edge devices
Stay current with state-of-the-art research and evaluate new architectures for applicability
Collaborate with product teams to define requirements and success metrics for vision features
Build monitoring and evaluation systems to track model performance in production

Required Skills & Experience

3+ years of experience in computer vision or related ML fields
Strong proficiency with Python and deep learning frameworks (PyTorch or TensorFlow)
Experience with CNN architectures (ResNet, EfficientNet, YOLO, Vision Transformers)
Hands-on experience with image processing libraries (OpenCV, PIL/Pillow)
Familiarity with model training workflows: data augmentation, transfer learning, hyperparameter tuning
Experience deploying ML models to production (ONNX, TensorRT, or TorchServe)
Strong foundation in linear algebra, probability, and optimization

Nice-to-Have

Experience with 3D vision, depth estimation, or point cloud processing
Familiarity with generative models (GANs, diffusion models) for image synthesis
Background in video analysis (tracking, action recognition, optical flow)
Experience with edge deployment (NVIDIA Jetson, mobile, or browser inference)
Publications or contributions to computer vision research

Tech Stack

PythonPyTorchOpenCVCUDAONNX / TensorRTDockerAWS SageMaker / Vertex AILabel Studio / CVATWeights & Biases

What We Offer

Competitive salary and equity package
Flexible remote or hybrid work arrangement
Health, dental, and vision insurance
Annual learning and development budget
Generous PTO policy

Interview Process

1Recruiter phone screen (30 min)
2Technical screen: ML fundamentals and computer vision concepts (45 min)
3Take-home project: train and evaluate a model on a provided dataset (4-5 hours)
4Deep dive on take-home project and system design for a vision pipeline (60 min)
5Team culture and collaboration interview (30 min)

Hiring for this role? You might also need:

Interview Scorecards

Phone Screen

Recruiter Phone Screen (Universal)

General-purpose recruiter screen covering motivation, experience fit, and logistics.

Hiring Manager

Hiring Manager Final Round

Final evaluation by hiring manager: team fit, role alignment, and leadership potential.

Culture & Values

Culture & Values Interview

Behavioral interview scorecard covering collaboration, ownership, and growth mindset.

Email Templates

Sourcing

Cold Outreach — Passive Developer

A personalized first-touch email to engage passive developers who aren't actively job hunting.

Sourcing

Cold Outreach Follow-up

A concise follow-up email for candidates who didn't respond to your initial outreach.

Sourcing

Cold Outreach — Break-up Email

A final follow-up email that politely closes the outreach sequence while leaving the door open.

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Common Screening Mistakes

Assuming all ML engineers can do computer vision — CV requires specialized knowledge of visual architectures and image processing
Overlooking candidates from robotics or autonomous vehicles backgrounds who have strong CV skills
Requiring publications when practical production experience is often more valuable

Red Flags

Cannot explain the difference between object detection and image classification at a high level
Claims deep learning experience but has never trained a model from scratch or fine-tuned one
Has no awareness of deployment considerations like model size, latency, or inference optimization

What Good Looks Like

A strong CV engineer can walk through an end-to-end project: data collection — annotation strategy, model selection, training, evaluation metrics, and deployment.
They understand trade-offs between accuracy and speed — and can explain their model choices in simple terms.
Look for hands-on experience with real datasets and production systems — not just academic papers.

Key Tech to Listen For

PyTorch
YOLO
Vision Transformers
OpenCV
ONNX
TensorRT
transfer learning
data augmentation