AI Researcher

About the role

You will join Harmony’s Engineering team to turn large volumes of real-world IT data into insights, models and product features that power our autonomous IT agents.

This role sits between data science, applied AI research and engineering. You will dig into tickets, conversations, knowledge articles and system signals at scale, find what matters, and ship it into the product — not leave it in a notebook.

You will work closely with engineers, product managers and domain experts. We are building a product, and the best research here is the research customers end up using.

What you’ll do

• Analyze large volumes of unstructured and semi-structured text (tickets, chats, emails, knowledge bases, logs) to uncover patterns, intents, topics and gaps

• Build semantic analysis pipelines: embeddings, clustering, classification, entity and intent extraction, similarity and retrieval

• Explore and compare different approaches to complex AI problems, from established NLP techniques to emerging LLM methods, designing experiments to understand what works, why, and under which conditions.

• Design experiments and evaluation frameworks, including offline metrics, human labeling and LLM-as-judge, and use them to make clear go / no-go calls

• Write production-quality code and work with engineers to ship research into live product features, then monitor and improve them

• Partner with product to define the problem, size the opportunity and decide what is worth building

• Share findings with the wider team through clear write-ups, dashboards and demos

Ideal candidate

• M.Sc. or Ph.D. in Computer Science, Statistics, Mathematics or a related field, or equivalent practical experience

• 4+ years as a Data Scientist, ML Researcher or Applied Scientist with a proven track record

• Proven experience analyzing large data volumes, ideally millions of records, with a strong preference for semantic textual analysis (embeddings, topic modeling, clustering, classification, NLP)

• Research and experiment with LLM-based approaches, including semantic understanding, information extraction, retrieval, and agent evaluation, to solve complex product problems.

• Willingness and experience taking research into production: you have shipped models or features, not only prototypes, and you enjoy working with engineers to get there

• Strong coding skills in Python and the data stack around it (e.g. pandas, PySpark, SQL, PyTorch); you write code others can read, test and maintain

• Product mindset: you start from the user problem, think in impact and trade-offs, and know when “good enough and shipped” beats “perfect and late”

• Clear communicator who can explain findings to engineers, product managers and leadership

Advantage

• Experience with IT service management or support data (tickets, incidents, knowledge articles)

• Hands-on work with RAG, vector databases and semantic search at scale

• Experience with LLM evaluation, agent benchmarking or LLM-as-judge methods

• Experience with large-scale data processing (Spark, Databricks, BigQuery or similar) and cloud ML platforms

• MLOps experience: model serving, monitoring, data drift and feedback loops in production

• Startup experience, or having worked as an early data or AI hire