We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind our company'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 our company'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.

We are looking for an experienced Senior Linux Kernel Engineer to join our Runtime Security Group and help build the technology that protects workloads at runtime - where sophisticated attacks need to be detected and stopped.

This is a highly technical, hands-on role at the intersection of Linux kernel development, cybersecurity, cloud-native technologies, and runtime protection.

Youll work on complex, low-level challenges that directly impact Aquas runtime security platform, designing and building scalable components for both cloud and on-prem environments.

If you're passionate about Linux internals, kernel development, eBPF, and solving deep technical security challenges, this is an opportunity to work on technology that operates at the heart of modern cloud security.

What You Will Own

Design and implement robust, scalable runtime security components for cloud and on-prem environments
Work deeply with Linux kernel internals, kernel modules, syscalls, and kernel/user-space interaction
Develop and optimize low-level components that enable runtime threat detection and protection
Leverage technologies such as eBPF for tracing, observability, security monitoring, and runtime protection
Take end-to-end ownership of complex technical initiatives - from architecture and design through implementation, testing, code review, and production delivery
Collaborate closely with software architects and senior engineers to shape technical direction and solve complex system-level challenges
Lead and contribute to technical PoCs evaluating new technologies and approaches
Work across the full software development lifecycle, maintaining high standards for code quality, performance, reliability, and security
Contribute to the evolution of Aquas next-generation cloud-native runtime security platform

We are looking for a highly driven and experienced AI Engineer Team Leader to manage and mentor a core team of engineers while remaining 50% hands-on. In this critical role, you will lead the development of our enterprise Agentic AI platform, blending deep technical expertise in AI agent development with strong engineering leadership.
As a Team Leader, you will drive the technical agenda, plan sprints, and collaborate closely with Product Managers and cross-functional partners to turn powerful capabilities into reliable, safe, and fast product experiences. You must thrive in a high-pressure, dynamic startup environment characterized by a fast pace and frequent pivots, ensuring your team remains focused, agile, and aligned with business goals while executing at the highest level.
You won't just manage - you'll own agent workflows end to end, from design through evaluation, deployment into the tools employees use daily, and iteration in front of enterprise customers.
Responsibilities
Team Leadership & Management (50%): Manage, mentor, and grow a team of AI and software engineers. Oversee task allocation, sprint planning, and daily agile ceremonies.
Hands-On Engineering (50%): Actively code, design, and build multi-agent systems and orchestration - intent routing, planning, tool use, and coordination across specialized agents.
Technical Strategy & Architecture: Drive the technical agenda, architecture decisions, and best practices for developing scalable, LLM-driven applications grounded in our knowledge graph.
System Integration: Integrate LLMs with tool/function calling and protocols such as MCP to give agents controlled access to HCM systems, business logic, and workflows.
Production Excellence: Build evaluation harnesses, guardrails, and safety/bias checks. Optimize agents for latency, cost, and reliability at enterprise scale. Take agents from prototype to production - with monitoring, observability, and a fast iteration loop.
Cross-Functional Collaboration: Work intimately with Product Management, Data, and Design teams to translate customer needs into agent capabilities, define roadmaps, and prioritize tasks.
Agility & Execution: Lead the team through frequent pivots and rapid development cycles, ensuring high-quality delivery under pressure in a fast-paced startup environment.

We are looking for a Software Engineer to help design and develop the core services and applications that drive our data capabilities. In this role, you will build high-quality, scalable software; contribute to the architecture of our next-generation data systems; and collaborate with research and product teams to turn cutting-edge ideas into production-ready features.
Responsibilities:
Design, develop, and maintain scalable backend services and components that power our data workflows, analytics, and product features.
Build high-quality internal tools and applications that improve engineering workflows and enable data-driven development across the organization.
Contribute to the architecture and evolution of our new data platform with a strong focus on clean design, testability, maintainability, and performance.
Collaborate closely with security researchers, analysts, and product managers to translate innovative cybersecurity concepts into reliable, production-ready software.
Apply engineering best practices across the stack - including code quality, testing, observability, versioning, and documentation - to ensure system robustness as we scale.

We're looking for a smart and capable DevOps Engineer. You will be part of a small and agile team, working on well-built infrastructure, handling production workloads, building and maintaining infrastructure that supports continuous delivery. This job includes work across the devops stack, including agent development, CI/CD, networking, resource allocation, cloud architecture, security, developer experience, and more.
We work with enterprise customers, with the SLA and expectations that this dictates.
This position includes on-call shifts.
Our stack:
Kubernetes on AWS
Terraform and IAC
Github & Github Actions
Responsibilities
What will you DO:
Design and evolve our cloud multi-region architecture on AWS - networking, interconnects, IAM and high-availability topology - and express it entirely as code with Terraform, following GitOps as a first principle.
Build and own the CI/CD pipelines that plan, review, test and safely apply IaC changes - Policy-as-Code guardrails, drift detection and progressive rollout so infrastructure changes ship as confidently as application code.
Advance Platform-as-a-Product: build self-serve capabilities and paved paths so engineers can provision what they need
Strengthen our observability stack - metrics, logs, traces and alerting.
Troubleshoot and mitigate production incidents, participating in on-call rotations to ensure system health and stability.
Design and develop the next generation of AI agents to support higher pace and evolve the RnD productivity while maintaining security and engage the entire company to use AI for better solutions.

Were looking for our next Manager, Cybersecurity Engineering. Could It Be You?
As the Manager of Cybersecurity Engineering, you will lead and grow a team of security architects dedicated to embedding security into our engineering culture in alignment with our regulatory compliance requirements as a Financial Services provider. Reporting to the Director of Cyber Strategy & Risk, you will own the team's technical direction and its impact on the organization's overall security posture - spanning architecture oversight and threat modeling, legacy system risk, security guardrails and automation, and M&A security diligence. You will balance technical leadership with people management, establishing your team as a trusted consultative partner that enables safe innovation across cloud-native financial platforms, legacy systems, and corporate integrations.
The Manager, Cybersecurity Engineering is a champion for putting the customer first and has a deep passion in understanding customer behaviour. They will create lifecycle journeys to improve the customer experience, engagement and retention as we deliver on our promise to help customers keep more of their money as they become more financially successful and secure.

We are looking for an exceptionally strong software engineer to join our Technology organization. You will be part of a business-facing team responsible for building and evolving the core systems that power our Quant strategies. This is an AI-forward team: large language models (LLMs) and AI agents are part of our daily workflow - from writing and reviewing code, to automating operational toil, to building internal copilots and agentic tools that the whole team relies on. You won't just use AI to move faster; you'll help shape how it's applied across our engineering practice.

Design and build data pipelines and the data layer.
Develop and maintain the computational frameworks and infrastructure that run our quantitative strategies at scale.
Implement and enhance quant-related software systems for data, research, portfolio construction, and trading.
Work closely with Researchers, and Quants as a hands-on engineering partner, helping them turn ideas into robust production solutions.
Gain exposure to the full lifecycle of the systematic/quant business, from data ingestion through research, implementation.
Build and integrate AI-powered tooling - LLM agents, copilots, and automation - into engineering and research workflows.
Use AI coding assistants and agentic developer tools to ship higher-quality software faster.

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.

We are seeking engineers with hands-on experience in developing and managing distributed systems and microservices in a production environment. The ideal candidate should have a solid background in infrastructure and operations.
The team builds high-throughput ingestion and processing pipelines, efficient storage on object stores, and systems for data governance, usage reporting, and routing data to multiple physical locations.
You will work on cloud-native, production systems written in Rust and Scala, operating at scale with Kafka, Postgres, Redis, and object storage, run on Kubernetes and operate in a multi-cloud environment.
Responsibilities:
Develop and operate distributed systems in production
Build Kafka-based ingestion and processing pipelines
Design systems for data governance, retention, deletion, and usage reporting
Work with Postgres and Redis, requiring solid database design and operational knowledge
Implement efficient persistence using column-oriented data formats and object storage.