Senior Machine Learning Engineer - Research

LevelAI • Noida • 1d ago

Why This Job is Featured on The SaaS Jobs

Voice AI is becoming a defining application layer in SaaS, particularly in revenue and customer-interaction workflows where speech, intent, and retrieval quality directly shape product outcomes. This Senior Machine Learning Engineer role sits in that intersection, focused on applied research that can be productionised into voice agents and agentic systems. The remit spans NLP, information retrieval, and LLM-based components, signalling work that influences both model capability and the end-user experience of a SaaS product.

For a long-term SaaS career, the role offers exposure to the full lifecycle that matters in subscription software: building pipelines, deploying models behind APIs, and owning monitoring and continual learning after release. That combination is increasingly valuable as SaaS companies move from prototypes to reliable AI features, where evaluation metrics, scalability, and inference optimisation become differentiators. Experience here tends to transfer well across AI-first SaaS teams working on automation, support, sales tooling, and conversational interfaces.

This position is best suited to engineers who prefer hands-on ownership from experimentation through deployment, and who enjoy collaborating across product and engineering boundaries. It will appeal to candidates motivated by measurable model quality, operational robustness, and staying close to state-of-the-art methods without losing sight of production constraints.

The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.

Job Description

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 revolutionise 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 organisations. 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 voice agents, agentic applications, text classification, entity extraction, and summarisation, using LLMs.
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.
Help implement and evaluate reasoning, planning, and memory modules for agents.
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:

B.Tech/M.Tech/PhD in computer science or mathematics-related fields from tier-1 engineering institutes with 3+ years of industry experience in Machine Learning and NLP.
Strong coding skills in Python and Pytorch with familiarity with libraries like Transformers and LangChain/LangGraph.
Strong practical experience in NLP problems in areas such as text classification, entity tagging, information retrieval, question-answering, natural language generation, clustering, etc.
Knowledge and hands-on experience with Transformer-based Language Models like BERT, Llama, Qwen, Gemma, DeepSeek, etc.
In-depth familiarity with LLM training concepts, model inference optimizations, GPUs, etc.
Experience with ML and Deep Learning model deployments using REST API, Docker, Kubernetes, etc.
Good problem-solving skills involving data structures and algorithms.
Knowledge of cloud platforms (AWS/Azure/GCP) and their machine learning services is desirable.
Knowledge of multimodal models is a plus
Knowledge of real-time streaming tools/architectures like Kafka and Pub/Sub is a plus.

Bonus points – Past research experience and accepted papers in the field of NLP or Machine Learning