Machine Learning Engineer

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About BespokeLabs

BespokeLabs is a venture-backed startup founded by seasoned IIT and Ivy League alumni. We specialize in building cutting-edge, AI-driven systems and next-gen digital products. Our mission is to harness advanced machine learning to solve real-world problems with speed, precision, and scale.

Machine Learning Engineer

Role Overview

You will be responsible for building ML models end-to-end—from problem framing, exploratory analysis, and feature engineering to experimentation, training, evaluation, and deployment support. You’ll work closely with our product and engineering teams to translate high-level goals into robust ML solutions that can be integrated into real-world applications.

Key Responsibilities

• Design, build, and optimize machine learning models for classification, prediction, NLP, recommendation, or generative tasks.
• Run rapid experimentation cycles, evaluate model performance, and iterate continuously.
• Conduct advanced feature engineering and data preprocessing.
• Implement adversarial testing, model robustness checks, and bias evaluations.
• Fine-tune, evaluate, and deploy transformer-based models where necessary.
• Collaborate with engineering teams to prepare models for production use.
• Maintain clear documentation of datasets, experiments, and model decisions.
• Stay updated on the latest ML research, tools, and techniques to push our modeling capabilities forward.

Required Qualifications

• 5+ years of hands-on experience

  in machine learning model development.
• Proven expertise in building and deploying ML models in a

  production ML product company

  .
• Strong proficiency in

  Python

  ,

  PyTorch/TensorFlow

  , and common ML/NLP frameworks.
• Solid understanding of ML fundamentals—statistics, optimization, model evaluation, architectures.
• Experience with distributed training, ML pipelines, and experiment tracking.
• Strong problem-solving and algorithmic thinking.
• Experience with cloud environments (AWS/GCP/Azure).

Preferred Qualifications

• Published research papers (conference or journal).
• Strong track record in

  Kaggle competitions

  (medals or high-ranked submissions).
• Experience with LLM fine-tuning, vector databases, or generative AI workflows.
• Familiarity with MLOps tools (Weights & Biases, MLflow, Airflow, Docker, etc.).
• Experience optimizing inference performance and running models at scale.

What We Offer

• Opportunity to work with a world-class founding team and cutting-edge ML projects.
• Flexible remote contract (20–40 hours/week).
• A fast-paced environment with room for innovation.
• Competitive hourly compensation with potential for future collaboration or full-time roles.