Machine Learning Engineer II
Confirmed on the employer's own hiring board on Oct 16, 2026. First seen by Alion on Aug 20, 2026.
Introduction to Demandbase:
Demandbase is the only pipeline AI platform that empowers GTM teams to automate growth at scale. With a unified view of data, insights, actions, and outcomes, B2B enterprises can seamlessly align and execute their account-based GTM strategies with confidence. Thousands of businesses trust Demandbase to maximize revenue, minimize waste, and consolidate their data and tech stacks - all in one platform.
As a company, we’re as committed to growing careers as we are to building world-class technology. We invest heavily in people, our culture, and the community around us. We have also continuously 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.
About the Role
We are looking for a Machine Learning Engineer II to join Demandbase and build intelligent data and AI/ML capabilities for our Company and Domains Data teams. This is a hands-on ML engineering role requiring a combination of strong ML fundamentals, GenAI/LLM experience, data engineering, and production software engineering. You will build ML solutions from experimentation through production and operate them reliably at enterprise scale.
You will work with large-scale first-party (1P) and third-party (3P) data, applying machine learning, NLP, Generative AI, and modern data-processing techniques to improve data intelligence, enrichment, web-signal extraction, and other ML-driven capabilities.
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.
Familiarity with MLOps/LLMOps, experiment tracking, model/prompt versioning, and production ML monitoring.
What You’ll Gain
Build ML and GenAI capabilities that directly power Demandbase's Company and Domain intelligence.
Solve challenging ML problems across large-scale 1P and 3P datasets.
Work across traditional ML, NLP, LLMs/GenAI, and large-scale data systems.
Take ML ideas from experimentation all the way to reliable production systems.
Work alongside experienced ML engineers, software engineers, and data experts to build the next generation of Demandbase’s data intelligence capabilities.
Benefits
Our benefits include Group Medical, Personal Accident, and Term Life Insurance for comprehensive protection. Preventive healthcare covers dental, vision, and OPD needs, complemented by strong mental health support. We also provide a fitness benefit, car lease policy, and gratuity for long-term financial well-being.
Our Commitment to Diversity, Equity, and Inclusion at Demandbase
At Demandbase, we believe in creating a workplace culture that values and celebrates diversity in all its forms. We recognize that everyone brings unique experiences, perspectives, and identities to the table, and we are committed to building a community where everyone feels valued, respected, and supported. Discrimination of any kind is not tolerated, and we strive to ensure that every individual has an equal opportunity to succeed and grow, regardless of their gender identity, sexual orientation, disability, race, ethnicity, background, marital status, genetic information, education level, veteran status, national origin, or any other protected status. We do not automatically disqualify applicants with criminal records and will consider each applicant on a case-by-case basis.
We recognize that not all candidates will have every skill or qualification listed in this job description. If you feel you have the level of experience to be successful in the role, we encourage you to apply!
We acknowledge that true diversity and inclusion requires ongoing effort, and we are committed to doing the work required to make our workplace a safe and equitable space for all. Join us in building a community where we can learn from each other, celebrate our differences, and work together.
Unsolicited Submissions
At Demandbase, we value thoughtful partnerships and direct connections with candidates. We’re not accepting unsolicited resumes or outreach from third-party recruiting agencies. Any unsolicited submissions will not be reviewed, and no fees will be paid.