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Requirements:
Professional Experience - 3-5 years of hands-on backend software development experience, demonstrating solid coding skills and a foundational understanding of software design and architecture.
Technical Proficiency -
Production Systems and Cloud - Experience building and operating production-grade services on a cloud platform (AWS preferred), including familiarity with containerization (Docker/Kubernetes), CI/CD, and observability tools like Grafana and Prometheus.
Programming - Strong command of at least one programming language, with Python or Rust being a strong advantage.
Web Services - Proficiency in designing and maintaining web services and APIs, particularly with REST and WebSocket protocols.
AI-Augmented Development - Hands-on experience using AI coding tools in your day-to-day workflow, with genuine curiosity to push their boundaries.
Mindset -
Engineering Quality - A commitment to clean, robust, and rigorously tested code - you treat quality as a first-class engineering concern, not something retrofitted at the end of a sprint.
Self-Learner - A strong ability to self-learn, step out of your comfort zone, and independently take a concept from research to production.
Problem Solver - Strong capability to work through complex issues and adapt to evolving technologies and environments.
Advantages -
Familiarity with the ML model lifecycle - training, evaluation, deployment, and monitoring of models in production.
Experience with Data Engineering and big-data pipelines (e.g., Dagster, Airflow, Iceberg).
Experience with TensorFlow, PyTorch, or Computer Vision concepts.
Experience with distributed systems, message queues (Kafka, RabbitMQ, SQS), and high-scale infrastructure.

Requirements:
Professional Experience: At least 5 years of hands-on Software Engineering experience, with a minimum of 2 years in a team lead or managerial role.
Technical Proficiency:
Production Systems and DevOps: Proven experience building high-scale, production-grade systems on a cloud platform (AWS preferred), with solid command of DevOps practices including CI/CD, containerization, and observability.
Programming: Strong command of at least one programming language, with Python or Rust being a strong advantage.
AI/ML Systems: Solid understanding of the ML model lifecycle, including training, evaluation, deployment, and monitoring, with enough hands-on exposure to make good infrastructure decisions around it. Experience with TensorFlow, PyTorch, or Computer Vision concepts is an advantage.
AI-Augmented Development: Hands-on experience integrating AI coding tools into engineering workflows, with a genuine interest in expanding their use across the team.
Data Engineering: Hands-on experience with data pipelines and big data infrastructure.
Leadership and Mindset:
Engineering Quality: You hold a high bar for correctness, reliability, and maintainability, and you treat quality as a first-class engineering concern, not something retrofitted at the end of a sprint.
Team Builder: A people-first approach to leadership. You coach, you unblock, and you build psychological safety alongside technical excellence.
Engineering Judgment: A strong ability to self-learn, cut through ambiguity, and land well-reasoned technical decisions under pressure.

Requirements:
1-2 years of experience as a Scrum Master, Project Manager, or Delivery Manager in a high-tech environment.
A proactive, resourceful, and can-do mindset, with the ability to take initiative and drive outcomes in complex environments.
Proven record of leading cross-functional initiatives and delivering scalable impat.
Strong proficiency with Jira and Confluence.
Jira Administrator (including JQL, automations, and dashboards) - advantage.

Requirements:
Background in AI/ML or security research. Ideal candidates have both, but we're equally open to candidates from data science or security research backgrounds with demonstrated depth in AI systems.
5+ years of experience in the AI or security industry.
Solid understanding of AI agents: how tool use, memory, planning, and orchestration layers interact, and where that architecture introduces risk.
Working knowledge of LLM and agentic security - prompt injection, jailbreaks, tool-use exploitation, memory/context poisoning, and related attack classes.
Knowledge of AI vulnerabilities at both the model and infrastructure layers, and familiarity with effective mitigation strategies (e.g., guardrails, sandboxing, input/output filtering, access controls).
In-depth understanding of LLMs, generative AI, agentic systems, and RAG pipelines - how they're built, where they break, and how failure modes propagate through a system.
Strong research methodology and judgment: experience designing large-scale experiments, forming hypotheses, and applying quantitative rigor to evaluate results, identify signal from noise, and draw defensible conclusions from complex or ambiguous data.

Requirements:
5+ years of product marketing experience in B2B SaaS, ideally in cybersecurity, identity, or adjacent security verticals.
Genuine curiosity about AI, you follow how AI agents, copilots, and LLM-based tools are reshaping enterprise workflows and the security challenges they introduce.
Proven ability to distill complex technical concepts including emerging topics like agentic AI risk, non-human identities, and AI-SPM into crisp, compelling narratives.
Hands-on experience building competitive programs, running win/loss analysis, and turning insights into sales tools.
Strong cross-functional instincts you know how to influence without authority and build trust with Product, Sales, and Marketing peers.
A bias toward execution: you can move from strategy to artifact quickly, without sacrificing quality.
Fluent English (written and spoken) required.

Requirements:
4-5 years of Product Management experience, including experience at a cybersecurity company.
Strong technical aptitude, with the ability to understand data models, system architecture, and how security detections operate under the hood.
Proven experience delivering AI-powered product capabilities from concept to production, with hands-on involvement in defining, validating, and launching AI-driven features.
Strong customer-facing skills with a demonstrated ability to transform ambiguous security challenges into clear, validated product opportunities.
Comfortable operating in a fast-paced startup environment, building products from ambiguity without predefined playbooks or processes.
Data-driven mindset with experience defining product metrics, measuring outcomes, and using insights to drive prioritization and decision-making.
Proven track record of partnering closely with engineering teams to deliver highly technical products.
Strong background in security-related domains.
Excellent communication skills, with the ability to align technical and business stakeholders around product strategy and execution.