Machine Learning Engineer II

About Demandbase

Machine Learning and 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 or RAG techniques.
• Develop ML solutions for classification, enrichment, information extraction, ranking, and data quality.
• Build LLM applications using RAG, semantic retrieval, tool or function calling, and agentic workflows.
• Develop reusable AI services, APIs, and orchestration components.

AI Evaluation and ML Engineering

• Build scalable data and feature pipelines for large structured, semi-structured, and unstructured first-party and third-party data.
• Develop evaluation datasets, automated evaluation frameworks, and feedback loops for AI and ML features.
• Define metrics for accuracy, relevance, grounding, latency, reliability, and cost.
• Analyze failure patterns and production performance to improve data, models, prompts, retrieval, and agent behavior.
• Apply guardrails, grounding, monitoring, and data-quality controls.

Production AI Engineering

• Build clean, scalable, and maintainable production-grade AI and ML systems.
• Own features from design and evaluation through deployment, monitoring, and support.
• Operate cloud-native AI services using Docker, Kubernetes, CI/CD, and observability.
• Maintain versioning for models, prompts, configurations, and evaluations.
• Apply engineering principles across distributed systems, concurrency, performance, testing, debugging, and code reviews.

Basic Qualifications

• 5–7 years of experience in machine learning engineering, applied ML, data science engineering, or a related area.
• Strong programming experience in Python and good software engineering fundamentals.
• Hands-on experience building and productionizing machine learning models or ML-driven applications.
• Experience with generative AI, LLM APIs or open-source models, embeddings, prompt engineering, and RAG.
• Strong understanding of model training, feature engineering, evaluation, experimentation, and inference.
• Experience with NLP, transformers, embeddings, or related unstructured-data techniques.
• Strong SQL knowledge and experience working with large datasets.
• Working knowledge of 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.
• Experience with large-scale first-party or third-party datasets and heterogeneous enterprise data.
• Experience with Kubernetes, Docker, CI/CD, and ML deployment workflows.