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Senior ML Platform Engineer | Weights & Biases | SF · Ganloss

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Senior ML Platform Engineer

AI role · Open offer

Senior ML Platform Engineer

Weights & Biases

Full-time
Senior
MLOps product
Hybrid
400 Alabama Street, San Francisco, CA 94110, United States · Hybrid
San Francisco, United States
$165k–$220k + equity

Employer

Weights & Biases

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Posted on Ganloss

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At a glance

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

Employment: Full-time
Workplace: Hybrid
Location: 400 Alabama Street, San Francisco, CA 94110, United States · Hybrid
Compensation: $165k–$220k + equity
Experience level: Senior
Team: MLOps product
Benefits & perks: Remote-friendly, equity.
How to apply: MLOps or experiment tracking system you built.
Company site: wandb.ai
Listed: Jul 15, 2026
Updated: Aug 3, 2026

About this role

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

Weights & Biases is hiring a Senior Senior ML Platform Engineer on the MLOps product 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.

Weights & Biases (W&B) provides experiment tracking and MLOps for ML teams worldwide.

Senior ML Platform Engineer: model registry integrations, eval products, and scalable metadata services.

What you will do

Deliver production-grade MLOps product engineering with measurable quality, latency, and cost targets.
Build offline evals and online monitoring before scaling to full traffic.
Collaborate with product, design, legal, or domain experts on safe rollout.
Write clear RFCs, runbooks, and postmortems for cross-team learning.

What we look for

Multiple shipped ML/LLM features—not coursework-only portfolios.
Strong Python, SQL, and experimentation discipline.
Clear communication for remote or hybrid stakeholders.
Work authorization for the listed country.

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.
Kubernetes
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
AWS
Cloud primitives for training, storage, IAM, and cost-aware LLM & generative AI deployments.

Tools & stack

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

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

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: MLOps or experiment tracking system you built.
Several of these show up in recent shipped work—not only on your CV: Python, Kubernetes, AWS, and Docker.
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: $165k–$220k + equity, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on My account, and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

MLOps or experiment tracking system you built.

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Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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Senior ML Platform Engineer

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