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we are looking for a Security Researcher.
he Applications Research Team within our elite Forensic Research Group is looking for a brilliant Researcher to drive the future of digital forensics.
In this role, you won't just look at data-you will decode the hidden digital footprints left behind by the worlds most widely used software.
Our team specializes in the deep-dive analysis, reverse engineering, and decoding of complex 3rd-party applications across mobile platforms. Your mission will be to tear down binary formats, dissect custom encryption protocols, and reconstruct application data from the ground up. If you look at an encrypted database or an undocumented binary format and see a puzzle that must be solved, this is where you belong.

We are building the next generation of AI-powered investigator tools . Our cloud-based AI platform delivers cutting-edge capabilities including multimodal analysis, embeddings, OCR, speech-to-text, face detection, image and text classification, and LLM completions through a scalable serverless architecture.
As a Senior Software Engineer on the AI Platform team, you will be part of the core team that designs, builds, maintains, and evolves the shared AI service platform that powers real investigative workflows at enterprise scale. You will help turn advanced AI concepts into production-grade serverless systems, shaping orchestration, retrieval, context, and backend architecture across the platform.

Were looking for an experienced Software Engineer with a strong background to become an integral member of our Data-Core team, tasked with the mission of processing, structuring, and analyzing hundreds of millions of data sources. Your role will be pivotal in creating a unified, up-to-date, and accurate utilities map, services, and applications for accelerating our mapping operations. Your contributions will directly impact our core product's success.


Key Responsibilities
Build and maintain components within distributed data pipelines - producer-consumer workflows over SQS, Airflow DAGs, Kubernetes-based processing.
Integrate outputs from the algorithms and ML teams into production workflows - implementing ETL steps, handling data transformations, ensuring upstream results are consumed correctly.
Contribute to shared Python libraries (14 packages consumed by 10+ services) - following existing patterns, writing clean code, understanding how changes affect downstream consumers.
Work within stateful workflow engines - implementing new steps, fixing issues in ticket state machines and multi-stage recovery workflows.
Debug pipeline issues by tracing data flow through SQS, Airflow, Kubernetes, and PostgreSQL to narrow down where things broke.
Contribute to ETL and data infrastructure - multi-source ingestion, taxonomy-based normalization, and database operations.
Improve reliability and observability - adding error handling, logging, monitoring, and alerting to existing services.

We are looking for a Senior Algorithm Engineer to join our core Algorithm team - a high-autonomy group responsible for the algorithm and ML systems at the heart of 4M's mapping engine. This is a role for engineers who think in systems, bring cutting-edge computer vision and algorithmic thinking to ambitious real-world challenges, and own problems end-to-end with an entrepreneurial drive.

In the AI era, being a strong engineer means more than writing great code. It means operating as both an architect and a team lead - designing the solution with clarity, then guiding AI agents to execute it at a level and speed that wasn't possible before. We are building a culture where this is the norm, and we're looking for someone who is excited to work and grow in that direction.

You will work across computer vision and algorithm disciplines - researching solutions, designing experiments, and delivering production-grade implementations - while collaborating closely with data engineering and product teams.

What You'll Do:
Research and implement novel computer vision and algorithmic solutions for challenging real-world problems - spanning perception, learning, optimization, and graph-based reasoning.
Own algorithm quality end-to-end: design experiments, evaluate results, and drive iterations until production reliability targets are met.
Work across disciplines - sharing models, methods, and insights with algorithm, data engineering, and product teams.
Monitor production model health proactively; identify degradation early and lead remediation efforts.
Integrate AI tools into your workflow and grow into the role of architect and team lead - designing the solution, then directing agents to build it.
Raise the bar on the team's technical culture through code reviews, knowledge-sharing, and rigorous algorithmic thinking.

we are looking for a User Acquisition Manager .
As an Expert Search User Acquisition Manager, you will help our acquisition efforts, be responsible for setting, optimizing, and scaling campaigns on Search campaigns (Google and Bing ads). You'll work closely with the bigger team, providing actionable insights and sharing best practices and knowledge.
What you'll be doing:
Set, optimize, and scale search campaigns in order to achieve KPIs
Share actionable insights and knowledge with existing PPC account managers.
Analyze performance based on ROI and engagement to drive actionable insights
Co-initiate and implement teams performance oriented projects, constantly seeking improvement opportunities
Work together with CS, Sales and product to optimize every step of the funnel
Work closely with the creative and content teams to improve Conv Rate
Identify trends and constantly come up with new growth opportunities
Initiate, structure and measure AB tests on landing pages
Combine business and creative thinking to understand user pain points, identify growth opportunities, and translate insights into practical actions that improve campaign and funnel performance.

As AI Quality Architect, you are the person who makes quality native to the agentic SDLC. You design the systems, standards, and intelligence layers that ensure every stage of an AI-accelerated pipeline - from requirement ingestion to autonomous deployment - is observable, trustworthy, and continuously improving. You don't retrofit testing onto AI workflows; you architect quality into them from the ground up.
How will you make an impact?
Agentic Quality Architecture
Design the end-to-end quality architecture for agentic SDLC pipelines - spanning requirement analysis, code generation, test creation, execution, triage, and release gates
Define how quality agents are orchestrated: which decisions they own autonomously, which require human-in-the-loop checkpoints, and how confidence thresholds govern both
Architect multi-agent quality workflows: requirement validation agents, test generation agents, failure triage agents, and regression analysis agents working in coordinated pipelines
Establish trust and verification models for agent-produced artifacts - test code, assertions, coverage reports, and defect analyses must all be auditable and traceable
Own the architectural patterns for quality feedback loops between agents: how a deployment agent learns from a triage agent's findings, and how that signal improves future generation
AI-Native Test Engineering Platform
Design and own the LLM-powered test generation platform - from natural language requirement ingestion to executable, maintainable test output
Architect the evaluation harness that continuously measures test generation quality: coverage delta, false-positive rates, assertion accuracy, and maintenance burden over time
Build the self-healing test infrastructure layer - agents that detect broken selectors, drifted APIs, or changed behaviors and propose or apply fixes autonomously
Define the prompt engineering standards, context injection patterns, and RAG architectures that ground test generation agents in real codebase context
Architect test artifact governance: versioning, ownership attribution (human vs. agent), rollback capability, and confidence scoring for every generated artifact
Quality Gates in Autonomous Pipelines
Design intelligent, adaptive quality gates that operate at the speed of agentic CI/CD - gates that reason about risk, not just pass/fail thresholds
Build risk-scoring models that dynamically adjust gate strictness based on change scope, code origin (human vs. AI-generated), historical failure patterns, and deployment context
Architect the observability layer for agentic pipelines: what signals indicate a pipeline agent is making poor quality decisions, and how are those signals surfaced in real time
Define the integration patterns between quality gates and orchestration platforms (LangChain, LlamaIndex, custom agent frameworks) used across the engineering org
Establish rollback and circuit-breaker patterns for autonomous deployments triggered by quality signal degradation.

Were looking for a highly motivated AI Developer to help design, build, and deploy intelligent agentic systems across our product ecosystem. In this role, you'll work at the intersection of machine learning, backend systems, and modern frontend technologies to deliver AI-first features that feel magical to users.
This is a hands-on, cross-functional role ideal for engineers who love building full-fledged features-from data pipelines and LLM orchestration to intuitive UI experiences-with a strong product mindset.

As AI Quality Architect, you are the person who makes quality native to the agentic SDLC. You design the systems, standards, and intelligence layers that ensure every stage of an AI-accelerated pipeline - from requirement ingestion to autonomous deployment - is observable, trustworthy, and continuously improving. You don't retrofit testing onto AI workflows; you architect quality into them from the ground up.
How will you make an impact?
Agentic Quality Architecture
Design the end-to-end quality architecture for agentic SDLC pipelines - spanning requirement analysis, code generation, test creation, execution, triage, and release gates
Define how quality agents are orchestrated: which decisions they own autonomously, which require human-in-the-loop checkpoints, and how confidence thresholds govern both
Architect multi-agent quality workflows: requirement validation agents, test generation agents, failure triage agents, and regression analysis agents working in coordinated pipelines
Establish trust and verification models for agent-produced artifacts - test code, assertions, coverage reports, and defect analyses must all be auditable and traceable
Own the architectural patterns for quality feedback loops between agents: how a deployment agent learns from a triage agent's findings, and how that signal improves future generation
AI-Native Test Engineering Platform
Design and own the LLM-powered test generation platform - from natural language requirement ingestion to executable, maintainable test output
Architect the evaluation harness that continuously measures test generation quality: coverage delta, false-positive rates, assertion accuracy, and maintenance burden over time
Build the self-healing test infrastructure layer - agents that detect broken selectors, drifted APIs, or changed behaviors and propose or apply fixes autonomously
Define the prompt engineering standards, context injection patterns, and RAG architectures that ground test generation agents in real codebase context
Architect test artifact governance: versioning, ownership attribution (human vs. agent), rollback capability, and confidence scoring for every generated artifact
Quality Gates in Autonomous Pipelines
Design intelligent, adaptive quality gates that operate at the speed of agentic CI/CD - gates that reason about risk, not just pass/fail thresholds
Build risk-scoring models that dynamically adjust gate strictness based on change scope, code origin (human vs. AI-generated), historical failure patterns, and deployment context
Architect the observability layer for agentic pipelines: what signals indicate a pipeline agent is making poor quality decisions, and how are those signals surfaced in real time
Define the integration patterns between quality gates and orchestration platforms (LangChain, LlamaIndex, custom agent frameworks) used across the engineering org
Establish rollback and circuit-breaker patterns for autonomous deployments triggered by quality signal degradation.

we are seeking a skilled and experienced DevOps Engineer to design, plan and implement scalable, reliable and robust system infrastructure to support the companys rapid business and traffic growth.
You will join an innovative, high-performance team and work with cutting-edge technologies in a dynamic and agile environment.
What you'll be doing:
You will take an active part in the challenge of managing a highly available and fault tolerant production environment.
You will assist in identifying and resolving performance concerns and operational bottlenecks, debug complex pitfalls and advise on system architecture solutions.
You will build, design and maintain self-serve devex tools to increase development velocity and satisfaction.
You will push and impact our systems to be more cost effective, performant and simple to use and extend.

our company enables organizations to address todays consumer and employee expectations by delivering effortless, consistent, and personalized digital-first experiences with CXone, the worlds leading cloud customer experience platform.
CSA team is dedicated to developing groundbreaking AI products that are transforming the industry. We promote a culture of collaboration, technical excellence, and the adoption of advanced technologies at scale.
We are looking for a Lead DevOps Engineer to join our CSA Infrastructure group. In this role, you will take a technical leadership position, driving the design, scalability, security, and operational excellence of our cloud platforms, while mentoring engineers and shaping DevOps best practices across teams.
How will you make an impact?
Lead the design, evolution, and operation of large-scale cloud infrastructure on AWS and Azure, ensuring high availability, security, resilience, and cost efficiency
Drive Infrastructure as Code (IaC) strategy and quality, ensuring consistency, reusability, and maintainability across environments and development groups
Act as a technical authority for troubleshooting complex production issues and leading root-cause analysis
Collaborate closely with development, QA, security, and product teams to align infrastructure with business and product needs
Mentor and guide DevOps engineers, fostering a culture of ownership, learning, and continuous improvement
Lead initiatives around observability, monitoring, alerting, and incident response
Proactively identify risks, technical debt, and optimization opportunities
Evaluate, introduce, and scale new tools, technologies, and AI-driven automation to improve efficiency and reliability.

As a Lead Software Engineer, you will be at the heart of building our Cloud Analytics Infrastructure group.
If youre talented, highly motivated and passionate about building modern cloud architectures and enjoy solving complex engineering challenges, this role is for you.
In this role, you will orchestrate AI agents to accelerate engineering workflows and deliver innovative AI‑driven features directly to customers.
Youll work in a fast‑paced, agentic environment where experimentation, ownership, and initiative are not just encouraged-theyre expected.
If you excel as an engineer, exploring the boundaries of AI‑assisted development, and want to influence the future of how engineering teams work with autonomous agents, this role is tailor‑made for you.
How will you make an impact?
Drive engineering excellence through AI‑augmented workflows and agentic development patterns.
Ensure high-quality deliverables and efficient project execution and continuous improvement.
Lead features design and implementations, set the bar for the team standards and advise on tech. Selection, show initiative in driving technical direction and team collaboration.
Demonstrate strategic thinking and a long-term vision for scalable solutions.
Champion continuous improvement and high‑velocity delivery in an innovative, fast‑moving environment.

We are looking for a visionary AI Lead to build our internal AI platform and architect adaptive intelligence systems that serve as a dedicated security architect for each of our customers. You will lead a team of AI Researchers and Engineers to move beyond simple integrations and build true autonomous security solutions.
What You Will Build:
Autonomous Workflows: Build agents that execute security workflows end-to-end, automating detection, investigation, and response processes.
Runtime Defense: Productize research into active runtime guardrails and defensive control mechanisms.
Internal AI Platform: Build the shared infrastructure for models, training, and evaluations to enable safe, scalable AI features across the organization.
Adaptive Intelligence: Oversee the development of models that learn from customer-specific environments to generate high-signal, personalized insights.
Responsibilities:
Lead the AI Research Group, managing AI Researchers, Engineers, and DevOps.
Own the intelligence engine end-to-end, delivering next-generation analytics and contextual visualization features.
Drive the strategic "build vs. partner vs. buy" decisions for cloud prevention and AI controls.
Collaborate with the Threat Team to develop AI-driven threat detections against emerging attack vectors and novel AI threats.

we are looking for a Director of AI.
The Director of AI will lead the companys AI organization, including algorithm development teams, embedded algorithm engineers within product squads, and the Data Operations team.
This role combines strategic leadership with execution ownership, driving the development, deployment, and continuous improvement of AI-powered products across power transmission, border security, and critical infrastructure monitoring.
The Director of AI is accountable not only for algorithm innovation, but also for ensuring successful production deployment and measurable customer impact. This leader will establish a customer-first culture across the organization, aligning AI development priorities with customer needs, product objectives, and business outcomes.
The ideal candidate is a visionary yet execution-oriented leader who can bridge cutting-edge AI research with scalable production systems, build high-performing teams, and deliver measurable customer value.
Success in this role will be measured not only by algorithm performance, but by the ability to deliver AI capabilities into production and generate measurable value to our customers.
What Youll Do:
Lead the AI group, including algorithm development teams, embedded algorithm engineers, and data operations.
Build and mentor high-performing teams, fostering a culture of ownership, collaboration, continuous improvement, and customer focus.
Define and execute the AI roadmap in alignment with company strategy, customer needs, and product objectives.
Lead the development and deployment of AI, Machine Learning, Deep Learning, and Signal Processing solutions.
Own the full AI lifecycle, from research and experimentation through production deployment, monitoring, and continuous optimization.
Lead the Data Operations function, ensuring efficient annotation, labeling, and quality processes that support AI development.
Partner closely with Product, Engineering, and business leaders to maximize AI impact across the organization.
Engage with customers and stakeholders to understand challenges, identify opportunities, and drive measurable business value.
Communicate AI strategy, progress, opportunities, and risks to executive leadership.
Contribute to company strategy, product direction, and technology leadership.

We are looking for a hands-on AI Engineer to help build our core intelligence engine and infrastructure. In this role, you will work on exploring and building autonomous solutions that execute security workflows end-to-end, moving capabilities from research concepts to scalable production features.
What You Will Build:
Internal AI Infrastructure: Contribute to a shared internal AI platform for models, data, training, and observability.
Autonomous Capabilities: Build solutions that automate complex security operations and investigative workflows.
Active Defense Mechanisms: Help productize runtime guardrails into customer-facing protective controls and monitoring systems.
Responsibilities:
Develop and implement the engineering backend for AI-driven security features.
Build and maintain the internal mechanisms that allow for safe and scalable AI feature deployment.
Collaborate with AI Researchers to translate findings into production-grade autonomous workflows.
Implement runtime guardrails and AI-focused data security capabilities.