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we are the AI-native platform powering the era of Agentic HR. Powered by Loomra, our context engine is purpose-built for the workforce. our company lets you build any Agentic HR agent that operates in the flow of work across Slack, Microsoft Teams, Copilot, and Google Chat. Talent Redeployment, internal mobility, skills intelligence, workforce redesign, and whatever comes next.
Trusted by leading global enterprises, our company serves over 1.5 million employees across 30+ Fortune 500 companies, unlocking more than 4.8 million strategic work hours. We help organizations harness AI to unlock agility, productivity, and growth, empowering them to achieve true ROI and drive exponential productivity.
About the Role
We are seeking an experienced and passionate Senior Software Engineer to join our core engineering team. In this role, you will design, build, and scale high-performance backend services that power our enterprise Agentic AI platform. You will tackle complex technical challenges, architect robust systems, and work on cutting-edge features that seamlessly integrate AI agents into enterprise workflows.
As a Senior Software Engineer at our company, you will collaborate closely with product managers, and cross-functional engineering teams. You will take full ownership of end-to-end feature delivery from initial system design and API definition to production monitoring and optimization - ensuring our platform maintains top-tier reliability, security, and execution velocity.
Responsibilities
Design, architect, and maintain highly scalable, high-throughput backend services and distributed systems.
Take end-to-end ownership of features and projects, driving them from technical design through to deployment and monitoring.
Build and optimize core APIs and integration layers.
Write clean, testable, and maintainable code, adhering to software engineering best practices (CI/CD, code reviews, automated testing).
Drive architectural decisions, lead technical design discussions, and advocate for system performance and scalability improvements.
Troubleshoot and resolve complex production issues, ensuring high system availability and low latency.
Mentor and guide junior and mid-level software engineers, driving engineering excellence across the team.

We are looking for a DevOps Engineer to own and scale the infrastructure behind our AI systems.

What you'll do
Own cluster reliability, availability, monitoring, alerting, capacity planning, and incident response.

Configure, maintain, and troubleshoot GPU servers, networking equipment, switches, InfiniBand fabrics, storage, and related infrastructure.

Deploy, maintain, and improve infrastructure around Slurm, Kubernetes, and GPU workload orchestration.

Manage and improve our cloud environment, including compute, networking, storage, IAM, security groups, cost visibility, and production infrastructure.

Build, maintain, and improve CI/CD pipelines.

Work closely with engineers to understand infrastructure bottlenecks and improve development, training, inference, and deployment velocity.

Contribute to security best practices across cloud, network, CI/CD, access control, secrets management, and production systems.

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.

Alice is a leading platform for Trust & Safety teams worldwide, combining cutting-edge AI with world-class expertise to protect users from the widest spectrum of online harms and keep customers two steps ahead of bad actors.
We are looking for a Senior GenAI Data Scientist with strong hands-on experience in GenAI red teaming, adversarial testing, machine learning, and large-scale data analysis.
You will develop the data science and ML capabilities behind an automated GenAI red-teaming platform. You will analyze and cluster adversarial prompts, identify emerging attack patterns, build models for automated prompt generation and mutation, and develop data-driven methods for prioritizing and evaluating security risks across different AI models.
We are looking for a hands-on technologist with deep expertise in data science: clustering, scaling, machine learning, and statistical analysis.
Key Responsibilities
Build agents that act as data scientists at scale, reasoning, planning, and executing multi-step workflows to generate and analyze adversarial attacks.
Manage and analyze prompt and response data from multiple sources (red-team campaigns, model evaluations, synthetic attacks); clean, curate, normalize, and label it for analysis.
Analyze large volumes of structured and unstructured data to uncover trends, clusters, and anomalies, such as attack families, jailbreak techniques, and previously unseen patterns.
Develop ML models and predictive algorithms to automate red-teaming.
Build evaluation and monitoring pipelines to measure attack success and track model behavior across models and providers.
Use statistical techniques and experiments to validate findings and ensure accuracy and reproducibility.
Take research to production: build reliable, scalable systems optimized for cost and latency, with solid engineering practices (testing, CI/CD, telemetry).

Were looking for our next Principal Engineer, Identity & Access Management. Could It Be You?
The Principal Engineer, Identity & Access Management, is the highest technical expert in the IAM team. The role is to provide technical leadership to the IAM team and overall to the organization on Identity Security best practices, in accordance with Enterprise Standards and applicable regulatory requirements.
Regulatory Environment: This role operates within a CIRO-regulated dealer and an OSFI-regulated federal financial institution (FRFI) environment. Candidates must understand that entity segregation between Questrade Inc. and Questbank is a foundational architectural constraint.
The Principal Engineer, Identity & Access Management 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 a Senior Full-Stack Engineer.
What You'll Do:
Own features end to end: product conversation → schema → API → UI → how it behaves in production.
Design the systems behind our AI agents - pipelines, orchestration, retries, guardrails, evaluation - so behavior is predictable and explainable.
Build backend services and data models that stay correct and stay fast as volume grows - across shared, single-tenant, and on-prem deployments.
Build the product surfaces our customers and our delivery teams use to configure, monitor, and audit agents.
Own your code in production: CI, deploys, observability, alerting, and the debugging that follows.
Work on how we ship, build and release pipelines, environments, and the infrastructure that lets one codebase deploy into very different customer environments.
Make the call on where complexity is worth it and where things should stay boring.
Raise the bar around you: code review, design review, mentoring, and the standards that keep a codebase alive two years from now.

The Director of Product Management for Engines owns the strategic vision, roadmap, and execution for our company's scanning engine portfolio across the company One platform. This is the detection technology our customers depend on to find and prioritize risk in their code.
You will lead a team of product managers, set the quality and performance bar across engines, and partner with R&D, Research, Design, and GTM to deliver capabilities that meet enterprise expectations for accuracy, scale, and trust.
How will you make an impact?​
Own the multi-year product strategy and roadmap for the engine portfolio, and make the investment trade-offs across engines that determine where our company competes and wins.
Build and lead a high-performing product management team, setting direction, developing talent, and holding a consistent standard across domains.
Establish detection quality and performance as accountable business outcomes, with clear targets and the evidence to demonstrate progress against them.
Partner with R&D leadership to align engineering capacity and delivery commitments to the roadmap.
Translate customer, market, and competitive insight into a differentiated strategy that drives adoption and revenue growth.
Align GTM, Sales, Marketing, and Customer Success behind the engine narrative, and own its credibility in the market.
Engage enterprise customers and C-level stakeholders to validate direction, build long-term partnerships, and resolve escalations that matter to the business.
Represent the engine strategy internally and externally, including at executive reviews, customer briefings, industry events, and analyst engagements.

As a Backend Team Leader, you will lead a team of backend and MLOps engineers that owns the systems behind our AI-driven product features. Your team works closest to our AI group - the Algorithms and Data teams - and owns the engineering side of turning models into reliable production capabilities.

Concretely, that means owning real backend architecture - the services and APIs that carry AI-driven capabilities into the product, the data collection pipelines that feed model training, the storage and query layers that hold model outputs and the insights we derive from them, and the validation pipelines that let us trust production flows. You will make the core design decisions on service boundaries, data models, event flows, and scale and cost tradeoffs, in a domain where both the data volume and the correctness bar are high. You do not need to arrive as an ML expert. You do need to be the kind of engineer who walks into an unfamiliar domain, learns it properly, and starts making good decisions in it.

What you'll be doing -

Lead and grow the team
Manage backend and MLOps engineers, some embedded in product pods; coach on delivery, decision-making, and career growth
Set clear expectations for ownership and quality; track progress against team goals, surfacing risks early.

Own the backend behind our AI features
Build and scale data pipelines with the AI group, and design how model insights are stored, queried, and exposed to the product
Build validation pipelines and partner with the Algo team to take models from experiment to production.

Drive architecture direction and alignment
Align backend decisions with the wider group, guide design discussions, and keep specs and plans on time
Collaborate with the platform team on infrastructure, deployment, and reliability standards.

Improve reliability and operational readiness
Raise the bar on stability, observability, and alert quality
Turn recurring issues into focused engineering work; lead debriefs and drive action items to completion.

As Backend Platform Team Leader, you'll lead the Platform pod. The pod owns our shared backend infrastructure (Kubernetes, Redpanda (Kafka), RabbitMQ, CI/CD, observability, and more), and it builds the tools, architecture and agentic workflows that let engineers in every other pod ship faster and more safely, at our growing scale

The role has two parts. As Pod Lead, you own the pod's direction, planning, delivery, and working process. As People Manager, you own the growth and performance of 3-4 direct reports, some of whom may work in other pods.

What you'll be doing -
Infrastructure Ownership: Keep our shared infrastructure reliable, secure, and cost-efficient, and make every change to it safe and well planned.
Architectural Evolution: Lead multi-quarter programs that reshape our backend, and migrate services onto them safely.
Reliability: Make sure problems surface early, and drive incidents to their real root cause.
Direction and Delivery: Maintain one clear, prioritized plan that you know in depth, and run a predictable team.
Planning and Delivery: Own sprint and quarter planning. Maintain one clear, prioritized plan that you know in depth, and run a predictable team.
Pod Process: Follow the PDLC, uphold the company's working processes and policies, and continuously shape and improve how the pod works.
Technical Ownership: Know the pod's systems and how they fit together deeply enough to reason about them with confidence, guide design decisions, and challenge assumptions.
People Leadership: Set clear goals and expectations, give honest feedback, invest in each report's growth, and stand behind your people. Where your reports work in other pods, stay aligned with those pod leads.

Our group is responsible for building software enabling both the large-scale quantitative research operations that drive our platform and the production of our portfolio. The team is currently embarking on a multi-year journey to provide a powerful, scalable, and efficient platform to drive the firms next decade of growth. We are looking for someone who can own some of the unique technical challenges inherent in the problem and who can bring solid experience and perspective to the team.

Our group is responsible for building software enabling both the large-scale quantitative research operations that drive our platform and the production of our portfolio. The team is currently embarking on a multi-year journey to provide a powerful, scalable, and efficient platform to drive the firms next decade of growth. We are looking for someone who can own some of the unique technical challenges inherent in the problem and who can bring solid experience and perspective to the team.

Alice is a leading platform for Trust & Safety teams worldwide, leveraging cutting?edge AI and world?class expertise to protect users from the widest spectrum of online harms. By continuously collecting and analyzing data, our solutions ensure customers stay two steps ahead of bad actors in an ever?changing threat landscape. We’re seeking an outstanding Backend Engineer with a Generative AI orientation to join our research team. We are building a new AI Platform from scratch to standardize AI development methods, ensure excellence and efficiency, and deliver robust, scalable, and compliant AI capabilities. This platform will handle GenAI models deployment, prompt management, data filtering, logging, and monitoring, centrally, so individuals can focus on their own business-specific logic. Key Responsibilities
* Build and maintain CI/CD pipelines that support fast, reliable integration and deployment of GenAI systems across complex environments, including orchestrating multiple models/agents and MCP clients/servers.
* Develop, deploy and integrate AI solutions and agentic frameworks, creating both command?line tools and interactive notebooks that support our team’s research and workflow automation needs.
* Build and manage data pipelines and databases (SQL) for large?scale data handling, automating data collection and curation (e.g., adversarial prompts for model red-teaming) to support AI training and evaluation.
* Integrate research into production by implementing the latest generative AI advancements (including white?box LLM development), integrating different models and agents, and developing evaluation frameworks (e.g., agentic reasoning tests) as part of our automated workflows.
* Integration & Orchestration: Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings
* Deploy GenAI models from repositories such as HuggingFace onto our AWS platform, connect them to data pipelines, and automate end?to?end processes for prompt generation, labeling, and response evaluation.

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!

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 Staff Architect, Data & AI Infra to shape, build, and scale the infrastructure that powers our data, AI, and research platforms. This is a senior player-coach role with broad architectural ownership across data infrastructure, ML infrastructure, developer experience, reproducibility, and production reliability. You will work across the wider engineering group as a hands-on technical architect, while also managing a small team of individual contributors focused on ML infrastructure.
This role is ideal for someone who can move between long-term platform architecture and practical execution: defining standards, building core systems, mentoring engineers, improving reliability, and partnering with Data Engineering, AI/Research, Product Engineering, Security, Bioinformatics, and Leadership to make our data and AI platforms scalable, reproducible, secure, compliant, and easier to use.
Location: Ramat Gan, Israel (hybrid model)
What will you do?
Architectural Leadership: Own and evolve the technical roadmap for our data and AI platforms, ensuring scalable and reliable architecture that supports current needs and prepares for a multi-cloud future.
MLOps & Platform Development: Design and build end-to-end MLOps systems-covering experimentation, training, reproducibility, and deployment-while managing specialized infrastructure like BigQuery, orchestration tools (Dagster/Airflow), and R/Python workloads.
Infrastructure Strategy: Define and lead strategy for GPU resources (scheduling, utilization, batch compute) and establish engineering best practices, data architecture standards, and platform guardrails.
Developer Experience: Enhance developer productivity by building self-service platforms, automation, internal tooling, and reusable templates that simplify workflows and reduce operational friction.
Team Leadership: Act as a player-coach to mentor engineers and manage a small team of ICs, fostering a culture of sound decision-making and technical excellence across the broader group.
Security & Reliability: Partner with Security to enforce compliance (SOC2, HIPAA, GDPR) and access controls, while mitigating operational risk through improved observability, incident readiness, and robust support processes.

Director of AI Product
About the role:
As the AI Product Director for the core components of our AI modeling framework, you will be responsible for defining and driving its product strategy, requirements, and roadmap, as well as lead a team of product managers. By leveraging our AI capabilities and vast data assets, you will spearhead the development of a product designed to empower internal users and external customers to address real-world challenges in drug discovery and development. Partnering tightly with R&D, you will ensure alignment between product strategy, scientific priorities and business objectives. You will advocate for your product both internally and externally, and ultimately be accountable for the value and impact your product delivers.
Location: Ramat Gan, Israel (Hybrid model)
What will you do?
Establish the long term vision and strategy to advance our AI-powered analytical platform to enable our mid and long-term goals
Responsible for defining the comprehensive roadmap and related PRDs for the analytical platform, focusing on optimizing analytical workflows, tackling predictive challenges, and enhancing model interpretability, as well as partner with business, R&D and ops to realize the roadmap
Evangelize the product, creating understanding and alignment across key stakeholders
Drive value creation for analytical platform users by enabling them to leverage our data assets effectively, both internally and in external collaborations
Ensure that our analytical platform development is effectively enabling and accelerating our product, business, and therapeutic goals