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Senior ML Engineer — GenAI | Databricks | SF · Ganloss

AI role · Open offer

Senior ML Engineer — GenAI lakehouse

Databricks

Full-time
Senior
GenAI
Hybrid
160 Spear Street, San Francisco, CA 94105, United States · Hybrid
San Francisco, United States
$185k–$250k + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

3 skills1 tool

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: 160 Spear Street, San Francisco, CA 94105, United States · Hybrid
Compensation: $185k–$250k + equity
Experience level: Senior
Team: GenAI
Benefits & perks: Equity refresh, wellness, hybrid.
How to apply: Enterprise RAG or fine-tuning delivery story.
Company site: www.databricks.com
Listed: Jun 8, 2026
Updated: Aug 3, 2026

About this role

What the team is looking for and how this position fits.

Databricks is hiring a Senior Senior ML Engineer — GenAI lakehouse on the GenAI team (Hybrid · San Francisco, United States). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Databricks unifies data and AI on the lakehouse. GenAI features span retrieval, fine-tuning, and governed serving for enterprise customers.

Senior ML Engineer — GenAI (San Francisco): RAG templates, vector search integration, and performance at lakehouse scale.

What you will do

Ship production enterprise GenAI platforms with clear evaluation, monitoring, and rollback paths—not demo-only notebooks.
Partner with product, design, and domain experts on requirements, trade-offs, and safe rollout.
Improve data quality, labeling, and offline metrics that correlate with business KPIs.
Document architectures and runbooks for on-call and customer-facing teams.
Mentor engineers on testing, observability, and responsible AI practices.

What we look for

Strong Python and modern ML/LLM stack experience with multiple shipped features.
Comfort with SQL, experimentation, and communicating results to non-technical stakeholders.
Pragmatic ownership from prototype to production—including cost and latency budgets.
Eligible to work in the listed country; professional English (French a plus for Paris roles).

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
Spark
Distributed feature generation and batch scoring on large tabular or event datasets.

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

Vector DBsTool
Embedding storage, hybrid search, and freshness strategies for RAG and semantic retrieval.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Enterprise RAG or fine-tuning delivery story.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, Spark, and Vector DBs.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · San Francisco, United States)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: $185k–$250k + equity, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

Enterprise RAG or fine-tuning delivery story.

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Senior ML Engineer — GenAI lakehouse

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