Applied-AI Engineer

Overview

We need an Applied-AI Engineer who thrives at the boundary between research and production. You will take models from papers and prototypes and turn them into reliable, observable, production-grade systems.

Responsibilities

• Implement and productionise models for classification, extraction, generation, and prediction tasks
• Build data pipelines for training, evaluation, and continuous model improvement
• Develop monitoring and alerting systems for model performance and data drift
• Conduct systematic evaluations to compare model architectures and deployment strategies
• Document production patterns, failure modes, and operational runbooks

Basic Qualifications

• 3+ years in applied machine learning engineering
• Strong Python skills and proficiency with ML frameworks (PyTorch, scikit-learn)
• Experience deploying models in production environments with API endpoints
• Understanding of MLOps practices: experiment tracking, model registry, canary deployments
• Comfortable with cloud infrastructure for ML workloads

Preferred Qualifications

• Experience with computer vision or multimodal models
• Background in time-series forecasting or anomaly detection
• Contributions to ML engineering communities or open-source projects
• Experience with model interpretability and explainability tools

Technologies

Compensation & Benefits

• Competitive salary commensurate with experience and location
• Remote-first work environment with flexible scheduling
• Annual professional development budget for conferences, courses, and tools
• Health, dental, and vision insurance coverage
• Equity participation in the firm's growth
• Equipment stipend for your home office setup