Machine Learning Engineer

zaimler•San Mateo, CA

15h•Onsite

About The Position

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.

Requirements

Roughly two years of real production experience is the shape this usually takes, but show us the work and we'll judge the work.
You've put an ML system into production and watched it survive contact with real data
Strong Python and PyTorch (or TensorFlow). You write code other people can maintain
You understand transformers, embeddings, and tokenization well enough to reason about them, not just call them
You've worked with LLMs somewhere real: retrieval, fine-tuning, structured extraction, or evaluation
You're suspicious of your own metrics. When a number looks good you want to know why before you celebrate
You learn fast and out loud, and you'd rather ask a blunt question than quietly stay stuck
You want to be near customers, not shielded from them

Nice To Haves

fine-tuning with LoRA, QLoRA, or adapters
vector databases and hybrid retrieval
knowledge graphs or graph learning
pipelines on Ray, Spark, or Kafka
vLLM
contrastive or self-supervised learning
multi-modal or long-context work
anything data-heavy at enterprise scale

Responsibilities

Build and improve the NLP and retrieval systems that extract structured knowledge from large, unstructured enterprise data
Work on the LLM layer: prompting, fine-tuning, RAG architectures, and figuring out which one the problem actually calls for
Improve the retrieval stack, including semantic search, vector storage, hybrid approaches, and reranking
Build evaluation. Define what good looks like for a given domain, then build the harness that measures it and catches regressions before customers do
Keep the pipelines that ingest, process, and serve this data running well at production scale
Sit with customers and with our product and platform engineers, so what you build solves the problem the business actually has