Machine Learning Engineer

Huxley; India; On-site; INR 1,800,000 - 3,600,000; Full time

Job summary

Huxley is seeking a Machine Learning Engineer to design and implement intelligent systems for personalization, content optimization, and user engagement on a large-scale platform.

You will work with data engineers and product teams to build ML models, run experiments, and deploy solutions via scalable APIs, driving measurable impact.

Qualifications

• Master’s degree or higher in a quantitative field.
• Strong knowledge of ML fundamentals and evaluation methods.
• Hands-on experience with ML/deep learning models (NLP/vision/recommendation).
• Strong Python programming skills.
• Experience with PyTorch or TensorFlow and GPU training.

Responsibilities

• Translate business problems into ML frameworks and designs
• Implement and optimize ML/deep learning models
• Conduct controlled experiments (A/B tests, bandits) to compare approaches
• Analyze results using statistical methods to assess impact
• Collaborate with data engineers, backend engineers, and product teams
• Contribute to improving data pipelines, feature engineering, and deployment

Skills

Python programming

Experimentation

ML fundamentals

NLP

Computer vision

Recommendation systems

Education

Master’s degree or higher in CS/Math or related field

Tools

PyTorch

TensorFlow

GPU-based training

Job description

Role Overview

We are seeking a Machine Learning Engineer to help design and implement intelligent systems that enhance user experiences across a large-scale digital platform. This role focuses on applying data science and machine learning techniques to solve real-world problems in areas such as personalization, content optimization, and user engagement.You will collaborate with both technical and business teams to translate complex challenges into scalable ML-driven solutions, with a strong emphasis on experimentation and measurable impact.

What You’ll Work On

• Build machine learning models that power personalization features such as recommendations and targeted content delivery
• Develop solutions for areas including natural language processing, image-based optimization, and user behavior modeling
• Partner with stakeholders to define problems, success metrics, and data-driven strategies
• Design and run experiments to validate model performance and business impact
• Deploy models into production environments through APIs and scalable systems
• Continuously iterate on solutions using insights derived from data and experimentation

Key Responsibilities

• Translate business problems into machine learning frameworks and technical designs
• Implement and optimize models using modern ML and deep learning techniques
• Conduct controlled experiments (e.g., A/B testing, multi-armed bandits) to evaluate different approaches
• Analyze experimental results using statistical methods to assess effectiveness
• Work cross-functionally with data engineers, backend engineers, and product teams
• Contribute to improving data pipelines, feature engineering, and model deployment processes

Core Requirements

• Strong interest and curiosity in machine learning and data science
• Advanced degree (Master’s or higher) in a quantitative field such as computer science, mathematics, or related discipline—or equivalent practical experience
• Solid understanding of machine learning fundamentals, including model types, optimization techniques, and evaluation methods
• Hands-on experience building ML or deep learning models (e.g., NLP, computer vision, or recommendation systems)
• Strong programming skills in Python
• Experience using deep learning frameworks such as PyTorch or TensorFlow
• Familiarity with GPU-based model training workflows
• Experience designing and analyzing experiments, including hypothesis testing

Preferred Expertise

• Machine Learning & Theory
  • Knowledge in advanced topics such as:
  • Counterfactual or off-policy learning
  • Transformer architectures and attention mechanisms
  • Optimization and probabilistic modeling techniques
  • Mathematics & Statistics
  • Strong foundation in:
  • Linear algebra and calculus
  • Probability theory and statistical inference
  • Exposure to advanced mathematical concepts (e.g., stochastic processes, geometric methods) is a plus
• Experimentation & Analytics
  • Experience with:
  • Classical A/B testing frameworks
  • Bayesian experimentation methods
  • Optimization techniques such as bandit algorithms
• Nice to Have
  • Experience deploying machine learning models into production systems
  • Track record of research contributions or publications in recognized conferences or journals
  • Experience working in multilingual or international environments
• Additional language skills are a plus