Machine Learning Engineer
Remote · full time
Practice the Machine Learning Engineer interview with an AI interviewer: voice, live coding and a scorecard.
Skills the interview covers
- Python
- Tensorflow
- pytorch
- scikit-learn
- sql
- Data Engineering
- MLOps
- Nlp
- Deep Learning
About the role
We are seeking a talented and motivated Machine Learning Engineer to join our growing AI/ML team. As part of this role, you will design, build, and optimize machine learning models that power intelligent features across our interview simulation platform. You will work closely with data scientists, backend engineers, and product teams to deploy scalable, production-ready ML solutions.
Key Responsibilities:
Design and implement ML models for NLP, recommendation, and prediction tasks.
Build scalable training pipelines using large datasets.
Optimize model performance, accuracy, and efficiency.
Collaborate with engineering teams to integrate ML solutions into production systems.
Monitor, maintain, and continuously improve deployed ML models.
Research and experiment with state-of-the-art ML and deep learning techniques.
Write high-quality, maintainable, and efficient code.
Required Skills & Qualifications:
Strong proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn).
Solid understanding of machine learning algorithms, optimization, and statistics.
Experience with NLP, computer vision, or recommendation systems.
Hands-on experience with data preprocessing, feature engineering, and model deployment.
Familiarity with SQL, NoSQL databases, and big data tools (Spark, Hadoop).
Strong problem-solving and analytical skills.
Experience with cloud platforms (AWS/GCP/Azure).
Good to Have:
Knowledge of MLOps practices (MLFlow, Kubeflow, Airflow).
Exposure to deep learning architectures (transformers, CNNs, RNNs).
Familiarity with APIs and microservices for serving ML models.
Understanding of Docker, Kubernetes, and CI/CD pipelines.
Behavioral Skills:
Excellent communication and collaboration skills.
Ability to thrive in a fast-paced, agile environment.
Curiosity to explore new technologies and research papers.
Strong ownership and accountability.