Job Overview
We are looking for a Machine Learning Engineer to design, build, and deploy ML models and AI-powered features at [Company Name]. You will work at the intersection of research and engineering, taking ML models from prototype to production. You care about model quality, latency, reliability, and creating real business impact through AI.
Key Responsibilities
- Design and implement end-to-end machine learning pipelines from data ingestion to model deployment
- Train, evaluate, and optimize ML models for production performance
- Build and maintain model serving infrastructure with low-latency requirements
- Collaborate with data scientists to productionize research models
- Implement feature engineering, data preprocessing, and feature stores
- Monitor model performance, detect drift, and retrain models as needed
- Write high-quality Python code for ML workflows and automation
- Contribute to MLOps practices including model versioning and experiment tracking
Requirements & Qualifications
- 3+ years of ML engineering or applied ML experience
- Strong Python programming skills and ML fundamentals
- Experience with TensorFlow, PyTorch, or scikit-learn
- Knowledge of model deployment using Docker, Kubernetes, or ML platforms (SageMaker, Vertex AI)
- Solid understanding of statistical modeling and evaluation metrics
Core Skills & Technologies
Python
TensorFlow / PyTorch
scikit-learn
MLflow / Weights & Biases
Feature Engineering
Model Deployment (SageMaker / Vertex AI)
SQL & Spark
Docker & Kubernetes
Nice-to-Have Qualifications
- NLP or computer vision specialization
- Experience with LLMs (GPT, BERT)
- Published ML research or Kaggle competition wins
What We Offer
- Premium ML-specialist compensation
- Research time allocation
- GPU compute resources
- Conference & paper publication support
- Equity & health benefits