Machine Learning Engineer — Niche but massively sought -after in 2025.

BoxNCase-posted 3 days ago
$50,000 - $200,000/Yr
Full-time • Mid Level
Hybrid • Commack, NY
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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.

  • Design, train, and validate machine learning models (Regression, Classification, Clustering, Deep Learning) to solve business problems.
  • Build scalable pipelines using tools like Kubeflow, MLflow, or Airflow to automate model training and deployment.
  • Improve model inference time and reduce latency for real -time applications.
  • Write complex SQL queries and build ETL processes to prepare massive datasets for training.
  • Identify and extract key features from raw data to improve model accuracy.
  • Work closely with Data Scientists to take research prototypes and turn them into production -grade code.
  • 2+ years in Machine Learning Engineering or a heavy Data Science role with production responsibility.
  • Advanced proficiency in Python (Pandas, NumPy) and familiarity with C++ or Java is a plus.
  • Deep experience with TensorFlow, PyTorch, Scikit -learn, or XGBoost.
  • Strong foundation in probability, statistics, and linear algebra.
  • Hands -on experience with AWS SageMaker, Google Vertex AI, or Azure ML.
  • Familiarity with Spark, Hadoop, or Kafka for handling large -scale data streams.
  • Frameworks: PyTorch, TensorFlow, Scikit -learn, Keras
  • MLOps: Docker, Kubernetes, MLflow, Jenkins
  • Data: SQL, NoSQL, Spark, Databricks
  • $50,000 – $200,000 USD / year (Based on experience and location)
  • 100% Remote or Hybrid options.
  • Access to high -performance compute clusters and GPUs.
  • Budget for conferences (NeurIPS, ICML) and certifications.
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