Machine Learning Engineer II

About Demandbase

Demandbase is the pipeline AI platform that empowers go-to-market teams to automate growth at scale. By bringing together data, insights, actions, and outcomes in one platform, Demandbase helps B2B enterprises align and execute account-based GTM strategies with confidence.

Thousands of businesses trust Demandbase to maximize revenue, reduce waste, and consolidate their data and technology stacks. We are equally committed to building world-class technology and growing meaningful careers. Demandbase has been recognized as one of the Best Places to Work in the San Francisco Bay Area by Fortune and one of the 60 Best Companies to Sell For by Selling Power. Our offices are located in San Francisco, New York, Austin, Seattle, India, and the United Kingdom.

Key Responsibilities

Machine Learning & GenAI

• Design, develop, and productionize Machine Learning and GenAI solutions for Company and Domain intelligence.
• Build data pipelines and solve data problems using LLMs, transformers, and retrieval/RAG techniques where appropriate.
• Develop ML solutions for problems such as classification, enrichment, information extraction, ranking, and data quality.
• Experiment with models, prompts, embeddings, retrieval techniques, and ML approaches and evaluate them using well-defined quality metrics.
• Collaborate with engineers, analysts, and product managers to translate requirements and data challenges into scalable, production-grade AI/ML solutions.
• Build LLM applications using RAG, semantic retrieval, tool/function calling, and agentic workflows, integrating enterprise APIs and internal data sources.
• Develop reusable AI services, APIs, and orchestration components.

AI Evaluation, Quality & ML Engineering

• Build scalable data and feature pipelines to process and derive intelligence from large volumes of structured, semi-structured, and unstructured 1P/3P data.
• Develop and maintain evaluation datasets, automated evaluation frameworks, and feedback loops for AI/ML features.
• Define quality metrics and acceptance criteria to assess accuracy, relevance, grounding, latency, reliability, and cost.
• Analyze failure patterns and production performance to continuously improve data, models, prompts, retrieval strategies, and agent behavior.
• Apply guardrails, grounding, monitoring, and data-quality controls to reduce incorrect or unsupported outputs and improve system reliability.
• Build solutions using Python, SQL, Spark/Pandas, vector search, traditional ML, and LLM-based approaches based on the problem's requirements.

Production AI Engineering & Operational Excellence

• Build clean, scalable, and maintainable production-grade AI/ML systems.
• Own features end to end from design and evaluation to deployment, monitoring, and support.
• Operate cloud-native AI services using Docker, Kubernetes, CI/CD, and observability.
• Monitor and improve quality, reliability, latency, scalability, and cost.
• Maintain versioning for models, prompts, configurations, and evaluations.
• Apply strong engineering principles across architecture, distributed systems, concurrency, performance, unit and integration testing, debugging, and code reviews.

Basic Qualifications

• 5–7 years of experience in Machine Learning Engineering, Applied ML, Data Science Engineering, or related areas.
• Strong programming experience in Python and good software engineering fundamentals. Experience with Scala is a plus.
• Hands-on experience building and productionizing machine learning models or ML-driven applications.
• Experience with modern Generative AI and LLM technologies, including LLM APIs or open-source models, embeddings, prompt engineering, and RAG.
• Strong understanding of core ML concepts including model training, feature engineering, model evaluation, experimentation, and inference.
• Experience with NLP, transformers, embeddings, or other techniques for working with unstructured data.
• Strong working knowledge of SQL and experience working with large datasets.
• Working knowledge of at least one cloud platform: AWS, GCP, or Azure.

Good to Have

• Experience with Spark, Kafka, Airflow, or similar large-scale data processing technologies.
• Experience with entity resolution, record linkage, classification, data mining, or data enrichment problems.
• Experience working with large-scale 1P/3P datasets or heterogeneous enterprise data.
• Experience with Kubernetes, Docker, CI/CD, and ML deployment workflows.