ML Engineer III

6sense's mission is to multiply what matters: growth, retention, and efficiency.  We envision a future where companies, teams and people reach their full potential.

People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging.  Every 6sensor plays a part in defining the future of our industry-leading technology.  6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers.  We want 6sense to be the best chapter of your career. 

We are looking for a ML Engineer III to join the Data Science team at 6sense. This role is ideal for someone with a strong foundation in machine learning, NLP, and applied AI who can independently solve business problems and deliver production-ready solutions.

As a ML Engineer III, you will work closely with senior ML engineers, product managers, engineers, and business stakeholders to build intelligent systems that improve customer outcomes and drive business impact. You will contribute across the full machine learning lifecycle, from problem formulation and experimentation to deployment and monitoring.

The ideal candidate is technically strong, curious, execution-focused, and eager to work on modern AI systems including LLM-powered applications, retrieval systems, and intelligent automation workflows.

What You’ll Do : 

• Design, develop, and deploy machine learning models and AI solutions for business and customer-facing applications.
• Build and optimize NLP and transformer-based models for classification, ranking, recommendation, prediction, and retrieval use cases.
• Contribute to GenAI and Agentic AI initiatives, including RAG pipelines, prompt engineering, tool usage, and workflow orchestration.
• Perform data exploration, feature engineering, model training, evaluation, and performance analysis.
• Develop scalable data pipelines and production workflows for model training and inference.
• Work with structured and unstructured data sources to generate actionable insights and build predictive systems.
• Partner with Product, Engineering, and Analytics teams to translate business requirements into technical solutions.
• Monitor model performance in production and contribute to model retraining, evaluation, and continuous improvement processes.
• Participate in design reviews, experimentation, and technical discussions to improve system quality and reliability.
• Document solutions, communicate findings, and present recommendations to both technical and non-technical stakeholders.
• Contribute to best practices in machine learning development, testing, deployment, and observability.

What We’re Looking For :

• 4–6 years of experience building and deploying machine learning systems in production environments.
• Strong foundation in machine learning, statistics, experimentation, and applied data science.
• Experience developing predictive models, recommendation systems, classification models, ranking models, or related ML applications.
• Hands-on experience with NLP techniques, embeddings, transformer models, and retrieval systems.
• Experience working with Python and common machine learning libraries such as Scikit-learn, PyTorch, TensorFlow, XGBoost, or similar.
• Experience working with distributed data processing frameworks such as Spark or Databricks.
• Familiarity with model deployment, monitoring, and ML lifecycle management.
• Ability to independently execute projects with moderate ambiguity and deliver high-quality solutions.
• Strong problem-solving, analytical thinking, and communication skills.
• Ability to collaborate effectively across Data Science, Product, and Engineering teams.

• Experience working with LLMs, prompt engineering, RAG systems, or agentic AI workflows. 
• Familiarity with LangGraph, LangChain, Amazon Bedrock, OpenAI, Anthropic, or similar AI platforms. 
• Experience with vector databases, embeddings, and semantic search systems. 
• Experience with cloud platforms such as AWS. 
• Experience in B2B SaaS, MarTech, AdTech, or customer-facing AI products. 
• Contributions to technical blogs, open-source projects, research publications, or internal technical communities.