Data Science Engineering Manager

Description

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind Alice's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team.

What your team owns

The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains:

1. 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 an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.

2. GenAI safety & 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) and forming Alice's leading edge into agentic-AI safety.

3. Data platform & 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

- Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
- Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
- Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders.

Leadership competencies

- People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
- Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
- Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
- Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.

About Alice:

Alice is the GenAI safety and security platform that keeps online experiences safe. We provide AI-driven detection, moderation, and red-teaming that protect billions of users across social platforms, marketplaces, gaming, and - increasingly - the GenAI applications reshaping the internet. Our Data Science group builds the machine-learning and data systems at the core of that mission.

REQUIREMENTS

What we're looking for (must-have)

- Leadership & communication - a proven people manager of engineering or data-science teams (or a strong tech lead ready to step into formal management), with excellent communication and stakeholder management.
- Hands-on engineering and ML at scale - strong production Python and software-engineering background with solid ML / data-science foundations (training, evaluation, deployment, monitoring), running services at scale on AWS and Kubernetes and large-scale data on Spark/PySpark and a lakehouse (Databricks or equivalent).
- 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.

Nice to have

- Experience with LLMs / GenAI in production - prompting, evaluation, guardrails, red-teaming, or agentic systems.
- Experience with model serving on NVIDIA Triton (or similar) and the MLflow / Unity Catalog lifecycle.
- Familiarity with Rust and Python/Rust interop (PyO3/maturin) for performance-critical paths.
- Background in GenAI safety and security, content moderation, or another domain where model quality has real-world stakes.
- Experience with event-driven architectures (Kafka, SQS) and Infrastructure-as-Code (Terraform).