About the Role Data is our fuel, but you are the engine. We are looking for a Machine Learning Engineer who treats model development as an engineering discipline, not just an experiment.
In this role, you will bridge the gap between data science and production software. You will design predictive models, build automated ML pipelines, and ensure our algorithms run efficiently at scale. You are perfect for this role if you love Python, live in Linux, and dream in vectors.
### What You Will Do
Model Development: Design, train, and validate machine learning models (Regression, Classification, Clustering, Deep Learning) to solve business problems.
Production Engineering (MLOps): Build scalable pipelines using tools like Kubeflow, MLflow, or Airflow to automate model training and deployment.
Optimization: Improve model inference time and reduce latency for real-time applications.
Data Wrangling: Write complex SQL queries and build ETL processes to prepare massive datasets for training.
Feature Engineering: Identify and extract key features from raw data to improve model accuracy.
Collaboration: Work closely with Data Scientists to take research prototypes and turn them into production-grade code.
Requirements
What We Are Looking For
Experience: 2+ years in Machine Learning Engineering or a heavy Data Science role with production responsibility.
Core Languages: Advanced proficiency in Python (Pandas, NumPy) and familiarity with C++ or Java is a plus.
ML Frameworks: Deep experience with TensorFlow, PyTorch, Scikit-learn, or XGBoost.
Math & Stats: Strong foundation in probability, statistics, and linear algebra.
Cloud Native: Hands-on experience with AWS SageMaker, Google Vertex AI, or Azure ML.
Big Data: Familiarity with Spark, Hadoop, or Kafka for handling large-scale data streams.
### Preferred Tech Stack (Keywords)
Frameworks: PyTorch, TensorFlow, Scikit-learn, Keras
MLOps: Docker, Kubernetes, MLflow, Jenkins
Data: SQL, NoSQL, Spark, Databricks
Benefits
Compensation & Benefits
Salary Range: $50,000 – $200,000 USD / year (Based on experience and location)
Flexible Work: 100% Remote or Hybrid options.
Tools: Access to high-performance compute clusters and GPUs.
Growth: Budget for conferences (NeurIPS, ICML) and certifications.
