> Markdown version of [/jobs/ext/3783472-machine-learning-engineer-computer-vision](https://www.wearedevelopers.com/jobs/ext/3783472-machine-learning-engineer-computer-vision). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer (Computer Vision)

- **Company:** OAK NETWORK LLC
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Computer Vision, Data as a Services, Data Mining, Distributed Systems, Python (Programming Language), PostgreSQL, Machine Learning, NumPy, PostGIS, Tokenization, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Deep Learning, Model Validation, AI Coding Agents, Convolutional Neural Networks, Fastapi, Discretization, Geospatial Data Abstraction Library (GDAL), ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Mixed Precision, Docker
- **Published:** October 16, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/ps084hz4cb

## About the Role

Machine Learning Foundations

 * Strong ML fundamentals: linear algebra, backprop, optimization, PyTorch, and NumPy

 * Understanding model failure modes (overfitting, leakage, class imbalance, distribution

shift)

Computer Vision Foundations

 * Image geometry, projection, homographies, camera models, and classical image

processing

 * Deep learning for vision: CNNs, segmentation (e.g., U-Net), object detectors, and ViTs

 * Evaluation metrics (IoU, mAP, precision/recall) and custom dataset training experience

NLP & LLM Agents

 * Understanding of transformers, embeddings, and tokenization

 * Experience with LLM agents: tool calling, retrieval, structured output, and context

management

Evaluations & Tracking

 * Experience designing slice-based evals, agent testing benchmarks, and LLM-as-judge

frameworks

 * Proficiency tracking experiments and model versions using tools like W&B or MLflow

Work Style & Approach

 * Strong problem decomposition and clear technical communication

 * Effective leverage of AI coding tools with critical verification

Preferred / Bonus Qualifications

 * Backend engineering: you've built or run APIs and data services (we use Python,

FastAPI and Postgres)

 * Distributed systems: you've spread work across many machines or GPUs and dealt with

what goes wrong, like job queues, retries and partial failures

 * Papers in reputed international conferences or journals, e.g. CVPR, ICCV, ECCV,

WACV, NeurIPS, ICLR, ACL, EMNLP, and remote sensing venues like IGARSS, ISPRS

or IEEE TGRS

 * Observability: instrumenting services and agents with OpenTelemetry, including the

GenAI semantic conventions for model and tool calls, and monitoring models for drift in

production

 * Recent vision foundation models: SAM 3, DINOv3, open-vocabulary detectors, and

VLMs used for zero-shot classification or pre-labeling

 * Remote sensing or geospatial work: multispectral imagery, NDVI, GeoTIFFs, coordinate

systems, GDAL/rasterio, PostGIS, and geospatial foundation models like Prithvi, Clay or

AlphaEarth

 * GPU performance and inference cost: profiling, batching, mixed precision, quantization,

distillation, TensorRT or ONNX Runtime

 * Docker and Kubernetes

## Description

This role focuses on computer vision for extracting insights from imagery at scale, alongside, * Build computer vision models for segmentation, detection, and classification

 * Determine appropriate modeling approaches (deep learning vs. classical image

processing)

 * Develop NLP and LLM agent solutions for structured data extraction

 * Create labeling guidelines and construct ground-truth datasets

 * Establish evaluation suites, test sets, and monitoring for models and agents

 * Conduct quick feasibility studies and research paper implementations

 * Collaborate with engineers to deploy scalable production models