Staff Applied Machine Learning Engineer

zaimler; San Mateo (CA); On-site; USD 100,000 - 130,000; Full time

Job summary

A forward-thinking tech company is seeking a Machine Learning Engineer to join their team in San Mateo, California. The ideal candidate should have a strong background in machine learning, natural language processing, and fine-tuning language models. Responsibilities include building advanced models, developing AI data graphs, and collaborating with engineers to create robust platforms. The position requires a Master's degree and a proven track record in handling complex ML projects.

Qualifications

• 3+ years experience in Machine Learning or Knowledge Extraction.
• 3+ years experience working with text.
• Strong background in the fundamentals of machine learning.
• Experience working with and fine-tuning language models such as BERT, LLMs, or SLMs.
• Deployed and maintained ML, NLP or LLM models in production.

Responsibilities

• Build and/or use best-in-class models to extract knowledge from heterogeneous sources.
• Develop methods to build and evaluate AI Data Graphs.
• Fine-tune LLMs with domain-specific context.
• Collaborate with data infra engineers for platform development.

Skills

Knowledge Extraction

Natural Language Understanding

Unsupervised Learning

Information Retrieval

Fine-tuning LLMs

Data manipulation using numpy

Data manipulation using pandas

Communication skills

Education

MS degree in Computer Science or equivalent

Tools

spacy

openNLP

openNER

GLiNER

docker

k8s

Job description

About zaimler

zaimler is building the semantic platform that links fragmented enterprise data and extracts meaning with knowledge-distilled models. We’re creating the foundation for AI systems that don’t just generate, but retrieve, link, and reason over enterprise knowledge.

In just over a year, we’ve begun partnering with Fortune 500 design partners in insurance, travel, and technology, deploying semantic AI infrastructure into some of the world’s most complex data ecosystems. Our platform enables enterprises to make data AI-ready from the start: automating ontology creation, data mapping, and retrieval-augmented reasoning at scale.

Our team comes from LinkedIn, Visa, Meta, and Branch, and has spent decades solving data and infrastructure challenges at scale. Backed by top VCs, we’re building the next foundational layer for enterprise AI.

About the job

We are looking for a Machine Learning Engineer to join our team who is based in the Bay Area or willing to move. The ideal candidate should have expertise in one or more of the following areas: Knowledge Extraction, Natural Language Understanding, Unsupervised Learning, Information Retrieval, and Fine-tuning LLMs. In this role, you’ll play a critical part in developing and training the models, pipelines, and methodologies that power our semantic graph systems. We’re looking for someone with a strong background in machine learning, natural language processing, LLMs, and semantic technologies, with a proven track record of tackling complex, large-scale machine learning projects.

What You Will be Doing

• Build and/or use best-in‑class models to extract knowledge from heterogeneous sources
• Develop methods to build and evaluate AI Data Graphs
• Fine‑tuning LLMs with domain‑specific context
• Work with data infra engineers to develop the best platform for your needs

Prior Experience

• MS degree in CS or equivalent
• Startup experience is highly preferred
• 3+ yrs experience in Machine Learning or Knowledge Extraction
• 3+ yrs experience working with text
• Experience working with and fine‑tuning language models such as BERT, LLM, SLMs
• Experience with NLP tools such as spacy, openNLP, openNER, GLiNER, etc.
• Experience with embedding‑based retrieval
• Strong background in the fundamentals of machine learning
• Deployed and maintained ML, NLP or LLM models in production
• Strong data manipulation skills using tools such as numpy and pandas
• Great communication skills and a team player

Nice to Have

• Familiar with LLM ecosystem and best practices of fine‑tuning and prompt‑engineering
• Experience working on ML and data in the cloud
• Experience with GPU optimization
• Experience working with docker, k8s
• Experience working with ray, vllm