Hybrid Machine Learning Engineer – Research

at Level AI

• Job title: Machine Learning Engineer – Research
• Job type: Full Time
• Experience level: Junior, Mid level
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Azure, Cloud, Docker, Google Cloud Platform, Kafka, Kubernetes, Numpy, Pandas, Python, PyTorch, Scikit-Learn
• Location requirements: Hybrid, Noida, India

About the role

• Machine Learning Engineer role at Level AI, innovating in speech AI and NLP space. Collaborate with experienced technologists to solve complex problems.

Responsibilities

• Big picture: Understand customers’ needs and innovate and use cutting-edge Machine Learning techniques to build data-driven solutions.
• Work on NLP problems across areas such as text classification, entity extraction, summarisation, generative NLP, and others.
• Collaborate with cross-functional teams to integrate/upgrade AI solutions into company’s products and services Optimise existing machine learning models for performance, scalability and efficiency.
• Build, deploy and own scalable production NLP pipelines.
• Build post-deployment monitoring and continual learning capabilities.
• Propose suitable evaluation metrics and establish benchmarks.
• Keep abreast of SOTA techniques in your area and exchange knowledge with colleagues.
• Desire to learn, implement and apply latest emerging model architectures (like LLMs), inference optimizations, distributed training, using open-source models, etc.

Requirements

• Bachelors in Computer Science or mathematics-related fields with 2+ years of experience in Machine Learning and NLP.
• Proficient in Python, NLP knowledge and practical experience in solving NLP problems in areas such as text classification, entity tagging, information retrieval, question-answering, natural language generation, clustering, etc.
• Knowledge and experience with data engineering, basic machine learning concepts, data mining, feature extraction, pattern recognition, etc.
• Knowledge and hands-on experience with Transformer-based Language Models like BERT, DeBERTa, Flan-T5, GPT, Llama, Gemma, DeepSeek, etc.
• Deep familiarity with Model Training concepts, model inference optimisations, GPUs, etc.
• Experience with Deep Learning frameworks like Pytorch and common machine learning libraries like scikit-learn, numpy, pandas, NLTK, transformers, etc.
• Experience with ML model deployments using REST API, Docker, Kubernetes, etc.
• Knowledge of cloud platforms (AWS/Azure/GCP) and their machine learning services is desirable.
• Knowledge of basic Data Structures and Algorithms.
• Knowledge of Multimodal models is a plus
• Knowledge of real-time streaming tools/architectures like Kafka, Pub/Sub is a plus.