Senior Machine Learning Engineer

About the Company

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.

About the Role

We require candidates with 2-5+ years of applied ML experience or strong Kaggle competition history. Ideal applicants are proficient in Python and have experience designing realistic ML problems, evaluating dataset quality, and building reproducible ML pipelines. Familiarity with benchmarking, scoring methodologies, and ML evaluation best practices is strongly preferred.

Responsibilities

• Frame unique ML problems for enhancing ML capabilities of LLMs.
• 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.
• 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.

Qualifications

• 3+ 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 fundamentalsstatistics, 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).

Required Skills

• 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.

Pay range and compensation package

Competitive hourly compensation with potential for future collaboration or full-time roles. ($20-$50)

Equal Opportunity Statement

We are committed to diversity and inclusivity in our hiring practices.