Principal Data Scientist

Description

We are hiring a Principal Data Scientist for the Data Science group, reporting directly to the VP Engineering. This is a senior individual-contributor leadership role: you own the technical direction and architecture of what the group ships - real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath - and you lead the hardest projects end to end, hands on in the code.

What you'll own

A large Python, Rust, and PySpark monorepo, dozens of production services and shared libraries, across three connected domains:

Content-moderation inference at scale

Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through our in-house ActiveServe framework over NVIDIA Triton and a Rust detection service, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.

GenAI safety and red-teaming

Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives, and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok).

Data platform and MLOps

An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection.

What you'll do

• Own the GenAI safety roadmap - decide what we build with LLMs and what we build around them: guardrails and LLM-as-judge adjudication, prompt-injection and jailbreak detection, policy authoring, and the automated red-teaming that measures attack-success rate against customers' applications. Choose which model families and providers we use, fine-tune, or retire, and what each has to prove before it reaches customer traffic.
• Own how we evaluate GenAI systems - benchmark and dataset design over real production traffic, LLM-jury labeling protocols, multilingual, multi-turn, and multimodal coverage, calibration and threshold selection, and the error analysis that turns a metric into a fix. Run the evaluations that separate a real shift in what these models can do from a launch post, and keep quality honest in production as traffic, prompts, and attacks drift.
• Lead the hardest GenAI work end to end, and grow the craft - take the ambiguous, high-stakes projects from framing through prototype, offline evaluation, shadow traffic, and rollout; mentor data scientists and ML engineers through design, experiment, and model review; and translate model behavior into what Product and customers care about: accuracy, latency, cost, and enterprise readiness.

Requirements

What we're looking for

• ML technical leadership - a proven tech lead, senior data scientist, or ML engineer who has taken multi-person, multi-quarter ML initiatives from framing to production without formal authority, with strong Python and the written communication to make the case in a design or an evaluation.
• Deep understanding of LLM technology - how these models are trained, adapted, and served, what prompting, fine-tuning, RAG, and agentic patterns each buy you, where they fail, and how to measure quality, latency, and cost.
• AI-augmented engineering - deep, daily fluency with an AI coding assistant (Claude Code, Cursor, or Codex), with the judgment to raise the whole team's leverage and set how these tools are used well.

About Alice

Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines.

In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection.

Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms.

If you're creative and driven to secure the future of AI, we want to hear from you!