San Mateo, California, United States · On-site
About Us We are on a mission to bridge the gap between enterprise business knowledge and data, democratizing data discovery and curation to prepare organizations for the era of generative AI. Today's data tools are overl…
Skills: Python, PyTorch, TensorFlow, NLP, Retrieval Systems
Bengaluru, Karnataka, India · On-site
About zaimler AI agents can't reason over data they don't understand. Enterprise data today is fragmented across dozens of systems with no shared context, meaning, or structure, and that's why most enterprise AI is faili…
Skills: Power BI, Microsoft Fabric, SQL, DAX, Cypher
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You will build and improve NLP and retrieval systems to extract structured knowledge from unstructured enterprise data. You will also work on the LLM layer, including fine-tuning and RAG architectures, while defining evaluation metrics for domain-specific correctness.
The role requires approximately two years of production experience with ML systems and strong proficiency in Python and PyTorch or TensorFlow. Candidates must have practical experience with LLMs, retrieval, or structured extraction and a desire to work closely with customers.
We are on a mission to bridge the gap between enterprise business knowledge and data, democratizing data discovery and curation to prepare organizations for the era of generative AI. Today's data tools are overly complex, poorly integrated, and siloed, forcing AI Practitioners and data scientists alike to spend more time wrestling with tools, relying on tribal knowledge, and navigating data lakes rather than doing meaningful data science work. The current landscape of data tools and processes is heavily manual and needs to catch up with the vast amount of data generated daily. With the advent of Gen AI and multi-modality, this challenge has only grown more complex and broken.
Backed by top VC funds, we are committed to making enterprise data AI-ready faster, more reliably, and with a stronger foundation of factual semantic knowledge. This leads to more accurate models, superior outcomes, and better business results. Our team of seasoned data infrastructure and machine learning experts (from LinkedIn, Visa, Truera, Hive, and Branch) has spent the past two decades building bespoke systems to solve these very challenges.
Join our growing team of ML research and data infrastructure experts. We're committed to empowering AI and data scientists to seamlessly integrate semantic learning with generative AI. Be part of our journey to shape the future of enterprise AI.
We're signing enterprises faster than we can model their data.
Every new customer arrives with a data estate nobody has ever mapped. Undocumented tables. Columns named by someone who left in 2019. Business rules that exist only in a sales director's head. Turning that into a semantic model an agent can act on without being wrong is not a pipeline you run. It's a person deciding what correct means in an unfamiliar domain and then proving it.
\nThe roleYou'll work on the systems that read messy enterprise data and turn it into a semantic model an agent can actually reason over. Knowledge extraction, natural language understanding, retrieval, and the evaluation that proves any of it is working.
The hard part is not calling a model. It's that "correct" is genuinely difficult to define here. A knowledge graph that looks right and is subtly wrong is worse than no graph at all, because an agent will act on it. Most of the interesting work is figuring out what correctness means for a customer's domain and then proving you hit it.
You'll be close enough to learn from all of them, and the team is small enough that nobody is going to hand you a well-scoped ticket.
If you want a research seat with a publication target, this is the wrong role. If you want clear specs and a defined lane, that's also wrong. You'll be reading unfamiliar customer data, forming your own opinion about what's broken, and shipping the fix.
What you'll doWe care about what you've built, not how long you've been building. Roughly two years of real production experience is the shape this usually takes, but show us the work and we'll judge the work.
Send us the ML system you're proudest of shipping at [email protected]. Tell us how you knew it was working, and what you'd do differently now.
We value builders over résumés. If this role excites you but you don't check every box, apply anyway and show us the work.
