AI role · Open offer
Senior ML Engineer — crypto fraud
Coinbase
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.
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- 100 Pine Street, San Francisco, CA 94111, United States · Hybrid
- Compensation
- $185k–$250k + equity
- Experience level
- Senior
- Team
- Trust & safety ML
- Benefits & perks
About this role
What the team is looking for and how this position fits.
Coinbase is hiring a Senior Senior ML Engineer — crypto fraud on the Trust & safety ML 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.
Coinbase operates one of the largest regulated crypto platforms in the US. Trust & Safety ML teams detect fraud, account takeover, and blockchain abuse in real time.
Senior ML Engineer (SF hybrid): graph features, sequence models, and LLM-assisted analyst copilots with strict false-positive budgets.
Comp band $185k–$250k + equity; comprehensive medical and crypto employee benefits.
What you will do
- Build real-time scoring for transactions, withdrawals, and identity signals.
- Design offline evals that reflect investigator workflows and regulatory reporting.
- Collaborate with policy on fairness, explainability, and appeal processes.
- Ship GenAI tools that accelerate analyst review without bypassing controls.
Stack & environment
- Python, PyTorch, Kafka, Snowflake, LangChain for internal copilots.
- Hybrid San Francisco Financial District.
What we look for
- Production fraud or abuse ML with measurable precision/recall trade-offs.
- Comfort with high-stakes deployment and on-call rotation.
- US work authorization.
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.
- PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
- SQL
Tools & stack
Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).
- KafkaTool
Streaming features or scores with ordering, idempotency, and monitoring for real-time systems.
- SnowflakeTool
Regular use of Snowflake in the LLM & generative AI stack—configuration, debugging, and team conventions.
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: Real-time fraud or abuse model with explainability notes.
- ✓Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, LangChain, and Kafka.
- ✓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.
- ✓
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What the employer asked for
Real-time fraud or abuse model with explainability notes.
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