As an MLOps Team Leader, you will own our AI infrastructure and backend engineering efforts, leading a team at the intersection of systems design and machine learning to build the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features.

What Youll Be Doing:
Lead MLOps Engineering: Own the roadmap and execution for a team of backend and AI infrastructure/MLOps engineers, setting technical direction, removing blockers, and holding the bar on delivery quality.
Build Production AI Systems: Design and implement production-grade, end-to-end AI solutions, including agentic workflows, that integrate Deep Learning models and Computer Vision algorithms into real features. Own the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Architect Data Platforms: Drive the design and implementation of scalable data platforms and pipelines that power our AI capabilities reliably and with an eye on cost and scale.
Cross-functional Collaboration: Work closely with the Algorithms and Data teams to tackle complex, real-world problems, translating research into shippable, maintainable systems.
Elevate Engineering Standards: Champion Software Engineering and System Design best practices across the group, introducing the right methodologies, tooling, and culture of craft.

Were looking for a Director of Engineering Security to join our organization and lead multiple engineering teams building the next generation of secuirty products.
This is a hands-on engineering leadership role for someone who is a builder at heart, an experienced software engineering lead who loves to code, understands the full product and technical lifecycle, and knows how to scale both people and technology.
Youll work closely with the VP of AI and leaders across Product, Engineering, and other functions to turn ambitious ideas into high-quality products, while building and mentoring strong engineering teams.
What Youll Do:
Lead and manage multiple engineering teams, including managing and mentoring Team Leads.
Set the technical direction and execution standards across the teams.
Stay close to the technology and remain hands-on when needed- this is not a purely managerial role.
Drive the full engineering lifecycle, from architecture and design through development, deployment, and production.
Build and scale SaaS products in a fast-moving environment.
Partner closely with Product and other departments to translate business needs into technical execution.
Make pragmatic tradeoffs between long-term architecture and short-term delivery.
Encourage a culture of ownership, speed, experimentation, and continuous improvement.
Motivate engineers by connecting their work to the broader product and business goals.
Collaborate closely with managers and stakeholders across the organization.
Resolve conflicts effectively and build strong, collaborative relationships across teams.
Help identify technical risks and long-term implications while maintaining a strong focus on delivering results now.

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

We are seeking an experienced and highly motivated Team Lead to lead our Data Infrastructure team. This role is responsible for managing a team of engineers while driving the design, scalability, reliability, and operational excellence of the companys core data platform.
The ideal candidate combines strong technical expertise in distributed systems and data technologies with proven leadership capabilities, strategic thinking, and the ability to lead complex initiatives end-to-end - from initial design and planning through implementation, deployment, and production ownership.
Key Responsibilities:
Lead, mentor, and develop a team of Data Infrastructure engineers, fostering a culture of ownership, collaboration, and continuous improvement
Define and execute the technical roadmap for the organizations data infrastructure platform
Oversee the architecture, scalability, reliability, and performance of large-scale data systems and pipelines
Define and drive data quality standards, monitoring, and validation processes to ensure trust and reliability across the data platform
Partner with engineering and product teams to enable scalable data foundations for analytics, machine learning, and AI-driven initiatives
Lead cross-functional technical initiatives end-to-end, including requirements gathering, architecture design, implementation, deployment, and post-production ownership
Partner closely with cross-functional teams including Data Engineering, Analytics, Backend Engineering, DevOps, and Product Management
Drive operational excellence through monitoring, observability, automation, and incident management best practices
Ensure high availability and stability of critical production systems
Lead technical design discussions, architecture reviews, and engineering planning processes
Establish and promote engineering standards, development best practices, and quality assurance processes
Manage project prioritization, resource allocation, and timely delivery of strategic initiatives
Support recruitment, onboarding, performance management, and career development of team members
Evaluate emerging technologies and recommend improvements to the data platform ecosystem

We're looking for an experienced hands-on Software Engineering Team Lead to lead a highly skilled team of backend and frontend engineers through implementation, delivery, iteration, and build scalable systems that serve our customers.

Responsibilities:
Foster a positive, collaborative culture and grow the teams capabilities.
Lead and mentor engineers to ensure on-time delivery, quality development, productivity, and efficiency by conducting and lead design reviews & code reviews.
Work together with product managers to translate requirements into scalable technical solutions.
Balance refactoring and evolution with steady delivery of new features.
Drive a culture of technical excellence, ownership, and continuous improvement.
Oversee system reliability, monitoring, performance optimization and incident response.

We are seeking candidates with quantitative portfolio management experience and intimate knowledge of systematic strategies

Job Responsibilities (include, but not limited to the following):
Develop systematic strategies that use statistical signals associated with various market inefficiencies applied to a broad variety of asset classes including global equities and/or ETFs, futures.
Lead, manage and grow quantitative investment portfolio.
Contribute to broader firm research and strategic initiatives.