Machine Learning Engineer (Computer Vision)

About Us

Oak Network builds decentralized infrastructure for crowdfunding and commerce, enabling

creators to run campaigns and shops with seamless blockchain-backed trust and payments.

We are a fully remote global team where members take ownership and collaborate across time

zones. (https://oaknetwork.org/)

The Role

This role focuses on computer vision for extracting insights from imagery at scale, alongside

work on NLP/LLM agents and evaluation frameworks.

A key focus is reasoning about real-world data feasibility, handling missing labels, and validating

model reliability beyond standard benchmarks.

Key Responsibilities

● 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

Qualifications & Requirements

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

Benefits:

● Competitive salary determined based on experience and interview performance.

● Hybrid role with remote work flexibility, with one or two potential in-person weekly

meetings.

● Paid vacation days, sick days, and holidays for full-time employees.

● Knowledge-sharing sessions on the latest in tech, engineering, and design.

Application Process

● For application submit the form below.

• https://forms.gle/tibLZVPEFsGGHJtQ8