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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 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 an

our company secures AI apps, agents and models from build to production. Enterprises are shipping AI faster than they can secure it, attack techniques evolve constantly, and every company draws the line on acceptable behavior in a different place. No enterprise can keep up with that on its own, and our company is how they do it. At the heart of the platform sit two engines: the attack engine finds the failures before attackers do, and the defense engine blocks them in production without breaking legitimate traffic.
We are hiring a Senior PM who will lead the strategy, vision, and execution of our core engines. This PM sits inside the DS workflow - and can read the notebooks, argue about the eval set, query the data themselves - and translate between model reality and customer reality in both directions.
This is not a PM role that writes a PRD and waits. It is a PM role where I pulled the data, heres what moved the F1, and heres what it means for customers is the expected level of answer.
What you'll own
Attack engine roadmap. Coverage and depth of adversarial techniques, novel-attack generation quality, agentic/multi-turn testing, zero-day and model-risk research productization. You decide which harm categories, modalities, and attack families we invest in next, and you defend that call with evidence.
Defense engine roadmap. Classifier quality across policies and languages, the precision/recall/latency triangle, customer-specific tuning, and the path from "a red team finding" to "a guardrail that blocks it." Closing that attack→defense loop is the single highest-leverage thing this role can do.
Evaluation as a product surface. Own how we define, measure, and publish model quality - benchmark datasets, eval harnesses, golden sets, regression gates, and the metrics customers actually see. When a customer asks "how accurate is this," the answer and the evidence behind it are yours.
Data strategy for the engines. What data we collect, label, retain, and feed back into training; how customer feedback (overrides, false-positive reports, session feedback) becomes a training signal; and how all of it stays within our customer data commitments
The DS↔Engineering seam. Model artifacts, schemas, and pipelines. You keep that contract explicit and get research into production.
Day to day
Run discovery with AI Product Owners, security engineers, and CISOs on what attacks they fear and what false positives they can't tolerate
Turn fuzzy customer harm into a testable engine requirement with a measurable bar
Prioritize the DS backlog with the DS lead; make the hard calls on coverage vs. depth vs. latency
Own the quality narrative for sales, solution engineering, and security reviews - backed by numbers you can defend
Partner with design and platform PMs so engine capability becomes a usable product, not a config flag
Track competitive and academic movement in adversarial ML and guardrails, and translate it into roadmap pressure.

We are looking for a Head of RL Gym to own the end-to-end delivery, quality, and scaling of RL Gym programs for our strategic AI partners.
You will work closely with frontier-model teams, Delivery, Product, Sales, Research, and Operations to turn complex customer and model-development needs into high-quality, scalable programs.
What Youll Own
Own partner delivery from scoping through launch, execution, scaling, and iteration.
Translate research and model needs into clear program requirements, workflows, and success metrics.
Work closely with frontier-model teams to identify model gaps and design RL Gym programs that address them.
Own quality standards and quality assurance, ensuring consistent, reliable outputs across programs.
Build the processes, systems, and operating model required to scale programs efficiently without compromising quality.
Partner with Sales on customer discovery, solution design, scoping, and expansion opportunities.
Work closely with Product to translate partner needs and operational learnings into product requirements and roadmap priorities.
Build strong feedback loops across customers, model teams, Sales, Product, and Operations.
Own operational performance across quality, capacity, timelines, cost, throughput, and partner satisfaction.
Build and lead the program management and operations function as the business scales.

As an Adversarial AI Evaluation Scientist, you will review and validate adversarial agent tasks: indirect prompt-injection (IPI), data-exfiltration, tool-misuse, and related attack scenarios to ensure each one is realistic, fair, deterministic, reproducible, and correctly graded. You will decide whether a scenario genuinely demonstrates a model vulnerability or merely an artifact of how the task was built, and you will combine adversarial-security judgment with statistical analysis and LLM-assisted tooling to find quality issues, analyze attack-success patterns, and recommend or automate improvements.
Responsibilities
Review adversarial tasks for realism and threat-model soundness: is the attackers capability plausible, can the attacker actually reach the poisoned content, and is the target system a realistic one?
Assess attack fairness and attribution: confirm the harm is driven by the adversary (not pre-baked into the system prompt, vouched for by a trusted layer, or otherwise rigged), so a careful agent could plausibly pass.
Validate grading correctness: verify that success/failure criteria, reward logic, and deterministic checks detect the intended harm, with no reward hacks, token-collapse, or evadable gates.
Measure and interpret attack success rate (ASR@K) and its variance; verify deterministic, reproducible behavior across repeated executions.
Judge attack quality - novelty vs. known patterns, sophistication, stealth, and coverage across harm types and target surfaces.
Identify anomalies and systemic issues across large sets of tasks (e.g. scenarios that succeed ~100% of the time against every model - a red flag for an unfair task, not a win).
Define validation guidelines, acceptance criteria, quality thresholds, and release gates for adversarial tasks.
Use LLMs and AI models to analyze attack failures, identify root causes, and assist with task or grader fixes.
Build automated validation, regression, and quality-monitoring pipelines for adversarial task sets.
Work closely with red-teamers, researchers, and task authors to improve attack design, fairness, and reliability.

We are seeking a Senior AI Researcher to lead post-training evaluation, red-teaming, and reinforcement learning (RL) gym audits on open-weight models. The ideal candidate will establish rigorous benchmarking methodologies, evaluate large language models (LLMs) against complex threats like Indirect Prompt Injections (IPI), and construct post-training evaluation pipelines that accurately measure realistic frontier-level security capabilities.
Key Responsibilities
RL Post-Training & Benchmarking
Execute post-training runs (e.g. GRPO) using mainstream open-weight generalist models against security-focused RL environments, targeting threat vectors like Indirect Prompt Injection (IPI).
Reward Diagnostics & Trace Analysis - Analyze live loss curves and rollout traces to identify reward hacking, lazy policy convergence, and flawed or over/under-specified verifiers.
Task & Environment Auditing
Review tasks and multi-turn environments (including tool use, web navigation, and computer use) for realism, threat model accuracy, data distribution, and dataset balance.
Performance Reporting (Gym Cards)
Generate comprehensive evaluation cards detailing hill-climbing performance uplift across checkpoints, failure modes, tokens/turns per rollout, and task-level success rates.
Integration & Orchestration
Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings.

we are seeking an experienced Malware Research Team Leader to lead a team of malware researchers, combining hands-on research with people leadership. This role presents an opportunity to lead an established team, strengthening its skills through ongoing training and guidance while ensuring high-quality deliverables.
The position balances managerial responsibilities with hands-on technical work (approximately 50% each). The ideal candidate demonstrates a positive, proactive attitude and excels as a reliable team player.
Key Responsibilities:
Mentor and manage a team of malware researchers, including recruitment
Conduct short, focused malware research under tight timelines
Detect and document emerging malware trends
Evaluate and enhance team accuracy and performance
Regularly share knowledge with team members and collaborate across teams and departments
Continuously improve malware analysis and review processes
Develop automation processes for malware identification at scale.

our company's RL Gyms team builds adversarial environments and evaluations that the world's leading AI labs use to test and train their most capable agents. Every engagement is a delivery project: a lab specifies what it needs to measure, and we produce the environments, adversarial tasks, and verifiers that measure it, against a deadline and a quality bar the customer sets.
Our customers are the frontier labs. You'll work alongside the people building the models everyone else is reading about, on the problems they haven't solved yet, and what you ship becomes part of how they decide whether an agent is safe to release. Very few roles put you that close to the frontier.
We're looking for a Technical Project Manager to own those projects. You'll run several lab engagements at once, each with its own researchers, engineers, and reviewers. You decide who works on what, you hold the schedule, and you decide whether a batch is good enough to ship.
The field is cutting edge and the projects change shape faster than any playbook can describe.
What you'll do
Project ownership
Own concurrent lab engagements end-to-end: scope, milestones, delivery, and status
Turn a customer specification into a task plan with your researchers and engineers: how many tasks, in which environments, against which models, by when
Track every batch through the pipeline (in progress, under review, approved, delivered) and keep the delivery record accurate enough that anyone can read the state of a project in a minute
Surface a slipping deadline early, with the tradeoff already worked out: cut scope, add capacity, or move the date
Researcher allocation and quality
Allocate researchers, task writers, and reviewers across projects based on throughput and specialty
Own their onboarding: brief them on the environment, the attack pattern, and the acceptance criteria, then get them to their first accepted task fast
Give quality feedback on rejected work and spot the patterns behind repeat rejections
Manage the review chain so that no batch reaches the customer before it has cleared internal review.