AI/ML Engineer for Recommendation System

We're a content streaming platform (100K+ users) building a personalized recommendation engine. Need a Senior AI/ML Engineer to design, train, and deploy machine learning models that predict user preferences and increase engagement. You'll work with Python, TensorFlow/PyTorch, and our behavioral data to create intelligent recommendations.

Responsibilities:

Design and implement recommendation algorithms (collaborative filtering, content-based)

Build and train ML models on user behavior data

Develop real-time recommendation API

Optimize model performance and latency

Implement A/B testing framework for models

Monitor model performance and retrain as needed

Handle data preprocessing and feature engineering

Deploy models to production (AWS SageMaker or similar)

Document model architecture and decisions

Required Skills:

5+ years ML engineering experience

Expert Python and ML frameworks (TensorFlow, PyTorch, scikit-learn)

Deep understanding of recommendation systems

Experience with large-scale data processing (Spark, Pandas)

Strong knowledge of ML algorithms and model evaluation

AWS ML services experience (SageMaker, Lambda)

API development for model serving

Understanding of A/B testing and experimentation

What You'll Get:

$4,000-$6,500/month retainer

35-50 hours work per month

Flexible remote schedule

Work on cutting-edge ML problems

Access to substantial user data

Equity options available

Collaborative tech team

Payment Structure:

Standard: $4,000/month (model development, 35 hours)

Advanced: $5,250/month (+ deployment + optimization, 42 hours)

Premium: $6,500/month (full ML ownership + research, 50 hours)

Payment on the 1st of each month

PayPal, Wise, crypto, or bank transfer

Tools Needed:

Python & ML frameworks (TensorFlow/PyTorch)

AWS ML services (SageMaker, EMR)

Jupyter Notebooks

Git/GitHub

Docker for containerization

Slack for communication

How to Apply:

GitHub profile or research papers/publications

Brief summary of your ML/AI experience

Notable recommendation systems you've built

Your approach to cold start problem

Experience with production ML systems

Preferred monthly rate

Your time zone