Machine Learning Engineer - NLP

Level AI is a Mountain View, CA-based startup innovating in the Voice AI space. We are backed by top VCs, technologists from Silicon Valley and industry experts. We are on a mission to revolutionize the customer sales experience for businesses. We are innovating in speech AI, NLP and information retrieval systems to bring customers and businesses closer to one another. As one of the critical members of the Level team your work will be new and of the highest impact to shape the future of AI in businesses. You will directly work with a team of experienced technologists to identify and solve a new set of problems. The team has experience from Amazon Alexa, Google, and other leading AI organizations. You will have the freedom to pave a new path to achieve our mission.

What you'll be liable for:

• 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.

We'll love to explore more about you if you have:

• 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 real-time streaming tools/architectures like Kafka, Pub/Sub is a plus.