דרושים » AI » Senior Applied Researcher

As an AI Research Team Lead on Research group, you will mentor a talented team of researchers, set the strategic vision for research initiatives, and drive team-wide project goals. Youll partner closely with Product and Engineering leadership to turn open-ended data-security challenges into measurable experiments and shipped features. You will manage the critical balance between research exploration and product delivery, owning the end-to-end lifecycle-from problem framing and data strategy to evaluation, deployment, and ongoing monitoring-helping customers discover, protect, and govern their data at scale.



What Youll Do

Lead and mentor a team of AI researchers, fostering a culture of excellence and innovation while overseeing the end-to-end research lifecycle.
You will act as the technical and strategic lead, defining team priorities, roadmap, and data science methodologies.
Mentor and grow team members through technical guidance, career development, and peer reviews.
Collaborate with cross-functional leadership in Product and Engineering to align research efforts with core business objectives and customer needs.
Manage team performance, resource allocation, and timely project delivery within an agile environment.
Develop, evaluate, and maintain deep learning and NLP solutions to enhance Cyeras core capabilities in sensitive data classification.
Design and architect production-grade agentic workflows. Establish rigorous evaluation pipelines to benchmark agent accuracy, latency, and cost, ensuring reliable, scalable solutions for real-world customer problems
Innovation and creative thinking are the keys! Implementing ML models to the entire research process - clustering, text extraction, document analysis, and tabular data classification.
Join a full stack AI group, including research engineering, MLEs, data operations, and security researchers. You will accelerate the path from research to production, ensuring results are both quick and precise.

we are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
"our company's data management vision is the future of the market."- Forbes
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless workers who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

We are hiring an AI Researcher to join the research team building the next generation of AI-native security systems. You will work alongside security and threat researchers to build large-scale AI agents that reason over software, code, endpoint activity, and security signals to detect malicious behavior, uncover vulnerabilities, assess risk, and make autonomous security decisions in real-world production environments. We are entering the Mythos era - where attackers operate at machine speed using autonomous systems and AI-generated software, and defenders must evolve the same way. We use state-of-the-art frontier models, including access to Mythos, to build reliable AI-native security systems at global scale. You will help design the evaluations, harnesses, and reliability infrastructure that make autonomous agents dependable under real customer load, while collaborating with leading AI organizations including Anthropic on initiatives such as Glasswing. This is an opportunity to work at the frontier of AI, autonomous systems, and cybersecurity while helping define how the next generation of security systems will operate.
Key Responsibilities
Build AI agents and autonomous security systems that reason over software, code, endpoint activity, MCPs, and security signals to detect malicious behavior, uncover vulnerabilities, and assess risk at production scale.
Develop systems, tooling, and infrastructure that enable agents to autonomously investigate threats, hunt for malware in massive datasets, and operate reliably in complex security environments.
Design and run experiments to evaluate frontier-model and agent capabilities in realistic adversarial scenarios, including benchmark creation, large-scale datasets, automated evaluations, and human-in-the-loop review systems.
Build the evaluation harnesses, observability systems, and reliability infrastructure required to make autonomous agents accurate, scalable, and dependable under real customer load.
Engineer for scale and performance across large distributed AI systems, including inference optimization, orchestration, batching, caching, cost controls, and graceful degradation under high demand.
Continuously evaluate emerging models, agent architectures, prompting techniques, and research directions to ensure our systems remain at the frontier of AI-native cybersecurity.
Rapidly prototype and test new approaches across reasoning, autonomy, evaluations, and security workflows as the AI landscape evolves.
Partner closely with threat and security researchers to extract domain expertise, translate analyst reasoning into AI workflows, and enable new forms of automation and autonomous investigation.
Collaborate with leading AI and security researchers to shape the future of AI-native cybersecurity as the industry transitions into the Mythos era.
Senior candidates will help define research direction, shape technical strategy, identify high-leverage problems, and influence how autonomous AI systems are deployed across the organization.

We are looking for an experienced Applied AI/ML Scientist with expertise in building agentic systems and autonomous agents to join one of our R&D. You will be at the core of transforming our supply chain solutions into a fully agentic platform, designing and building agents that autonomously generate analytical pipelines, orchestrate multi-step reasoning, and resolve complex logistics challenges for our customers.
You will combine strong machine learning and deep learning expertise with the ability to architect and implement production-grade agentic systems, working closely with engineering, product, and domain experts to push the boundaries of what autonomous AI can do in supply chain.
Responsibilities:
Design and build autonomous agentic systems that generate, configure, and execute analytical pipelines to solve supply chain challenges end-to-end
Architect multi-agent workflows with planning, tool use, memory, and feedback loops, enabling agents to reason, adapt, and improve over time
Develop and integrate ML and deep learning models (e.g., predictive models, anomaly detection, demand forecasting) as core capabilities within agentic pipelines
Research and apply state-of-the-art techniques in agentic AI, LLM orchestration, and multi-agent systems to production use cases
Translate complex logistics and supply chain challenges into agent-based problem formulations, collaborating closely with product and domain experts
Define and implement rigorous evaluation frameworks for agent performance: correctness, reliability, robustness, and edge-case handling
Collaborate with software engineers to productize agentic solutions - including testing, monitoring, versioning, and iterative improvement
Contribute to team practices: reproducible code, experiment tracking, documentation, and knowledge sharing

1. AI Architecture & Technical Leadership

Guide AI architectural direction across platform, focusing on system design, model lifecycle, and integration of AI components into product workflows.
Act as a senior technical reviewer and thought partner for complex or cross-team AI design decisions.
Provide technical oversight on areas such as exploring new models/technologies/opportunities for the company
Surface architectural risks, tradeoffs, and long-term implications, including cost, security and compliance aspects and clearly advise the VP of AI when certain technical directions should not be pursued.
This role influences judgment, clarity, and experience, not through blocking authority.

2. Hands-on Applied Innovation (Core Pillar | ~50%)

Spend at least 50% of time hands-on, building:
End-to-end AI prototypes
Technical demos and proofs of concept
Exploratory implementations of new AI capabilities
Drive applied innovation that:

De-risks new technologies
Demonstrates feasibility and impact
Informs product direction and business opportunities
Build fast, concrete examples that teams can learn from and extend.
Transition successful prototypes to team ownership for further development and scaling.
This role is expected to lead AI innovation by doing, while working closely with product, medical and engineering

3. Best Practices & Technical Enablement

Define and promote best practices for applied AI development, including:
Rapid prototyping and vibe coding.
Agent design, orchestration, and evaluation patterns
Experimentation, benchmarking, and validation workflows
Help teams align on shared technical patterns, tools, and standards.
Identify opportunities to consolidate duplicated efforts and improve cross-team coherence.
Lead technical deep dives, architecture discussions, and design reviews.
4. AI Compliance & Regulatory Enablement (Technical Scope)

Ensure Navinas AI development practices align with applicable AI regulations for a software product handling sensitive medical data.
Define and guide AI-specific compliance practices, including data usage, transparency, evaluation, and documentation expectations.
Support and contribute to AI-related compliance and regulatory documentation, in close collaboration with Legal, Security, and Medical Research teams.
Serve as a technical point of reference for AI compliance questions.

We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across the platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
Platform Integration & Intelligent Decision Systems: Develop MCPs to expose capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how evolves from AI-enhanced internal tools to customer-facing AI products.

We are looking for a Software Engineer to join our core Algorithm team as an dedicated data-core / platform engineer. You will support the algorithm engineers developing detection algorithms to design and build the infrastructure they run on: the object-detection (OD) pipeline, its orchestration and deployment, its data and model catalog, and the benchmark and labeling systems that drive model improvement. This is a high-autonomy role with real, end-to-end ownership of production systems from day one.

The technologies listed in this description are examples of our day-to-day - not a rigid checklist. We care far more about how you think, how you plan, how you debug, and how fast you learn than about which specific tools you have already used. If you love the craft of making things work and want to go deep, you can learn the rest here.

In the AI era, being a strong engineer means more than writing great code. It means operating as an architect who directs AI agents - designing the solution with clarity, then guiding them 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.



What You'll Do

Take on hard, open-ended infrastructure challenges and make them work - designing, building, decoupling, and hardening the systems behind our object-detection pipeline, from data and model management to benchmarking, so everything runs reliably at scale.
Architect and build the backbone of the OD pipeline - orchestration (Airflow on Kubernetes), data plumbing (S3 / PostGIS / SQS), CI/CD, and deployment across multiple environments - designing clean interfaces and data contracts the algorithm team can build on with confidence.
Debug across the whole stack, wherever the problem leads - a stuck DAG, a flaky pipeline stage, a slow query, a GPU/driver mismatch - and turn one-off firefights into lasting fixes and better observability.
Own the data and model lifecycle: versioned datasets and model weights with clear provenance, and the labeling → export → retraining loop that keeps the models improving.
Learn fast and go deep. Pick up new tools and new layers of the stack as the work requires, and raise the team's engineering and operational standards as you go.
Partner closely with algorithm engineers and the data-collection / labeling operations team to turn research prototypes into robust, scalable production systems.
Integrate AI tools into your workflow and grow into operating as an architect who directs AI agents - designing the solution, then guiding them to build it.

We're hiring the engineering leader who will own its AI core.
Small senior team, greenfield architecture and real production environments from day one. You'll be one of the founding technical leaders - writing code, shaping the architecture, and growing the team as the product scales.
What You'll Do:
Agent Architecture & Engineering:
Design and build the AI systems at the center of the product: researcher agents, deterministic runners, multi-agent orchestration across thousands of targets and hundreds of sites.
Own the full agent stack - LLM selection and behavior, RAG pipelines, tool use, memory, evaluation, and observability. Make architectural decisions that will define how the product works for years.
Drive adoption of the modern agent ecosystem: LangGraph, MCP, semantic tool discovery, hybrid edge/cloud workflows, A2A patterns. Keep pushing the frontier.
Safety & Reliability:
Design for progressive autonomy: pre-checks, fault tolerance, rollback, and full audit trails. In our customers' environments - critical infrastructure, enterprise security - a wrong action has real consequences.
Build the evaluation and observability pipelines that make autonomous agent behavior trustworthy and debuggable in production.
Partner with Security and DevOps on agent execution boundaries, especially across on-prem ↔ cloud data flows.
Technical Leadership:
Spend most of your time in the codebase. Set the technical bar by example - architecture, code quality, and engineering judgment.
Establish standards for testing, evaluation, and safe deployment of AI systems. Build the practices that scale with the team.
Work directly with the PM and enterprise design partners to shape the roadmap. Your decisions will drive the product, not just execute it.
Team:
Start with a small senior group, grow it deliberately. Hire well, mentor, and shape the engineering culture of a startup inside a public company.

Were looking for a hands-on AI Engineer to build and own the agentic workflows at the heart of our security intelligence engine.
This is a backend-focused engineering role: youll design, build, and maintain the autonomous systems that execute security workflows end-to-end - taking capabilities from research prototype to scalable, production-grade infrastructure.
Youll be working at the edge of applied AI, turning LLMs and agentic frameworks into reliable, observable systems that operate in real customer environments.
What Youll Build:
Agentic Workflows (Core Focus): Design and maintain the orchestration backbone for multi-step, autonomous agents that investigate, reason about, and act on complex security operations.
Internal AI Infrastructure: Contribute to a shared platform for models, data pipelines, training, evaluation, and observability that the whole AI team builds on.
Responsibilities:
Build and maintain the agentic workflow engine - orchestration, tool use, state management, retries, and evaluation - that powers our AI-driven security features.
Develop the backend services and APIs that deploy AI capabilities safely and at scale.
Collaborate with AI Researchers to translate findings into production-grade autonomous workflows.
Own reliability, observability, and performance of the agentic systems in production.

We're hiring an exceptional Applied AI Scientist to join our team!
In this role, you'll design and deploy enterprise-grade AI agents that drive real business impact across core functions - Sales, Marketing, Customer Success, Customer Support, Risk, and Finance. You'll be building agentic systems that execute complex, multi-step business workflows end-to-end. The ideal candidate is a hands-on builder with deep AI expertise who thrives on turning complex business problems into reliable, production-grade AI systems.
In This Role, You Will Be Responsible For:
Enterprise AI Agent Development: Designing, building, and deploying AI agents that automate and augment high-value workflows across business functions - including lead qualification and outreach (Sales & Marketing), churn reduction(Customer Success), and Ticket deflection (Support).
Agentic Architecture & Orchestration: Owning the end-to-end agent stack - from tool use, memory management, and multi-step planning to human-in-the-loop escalation patterns, guardrails, and audit trails suited for an enterprise fintech environment.
Knowledge Systems: Building and maintaining RAG pipelines that ground agents in proprietary knowledge - product documentation, customer data, financial records, and internal playbooks - using vector databases, hybrid search, and re-ranking.
Evaluation & Continuous Improvement: Defining agent evaluation frameworks that measure task completion, accuracy, hallucination rates, latency, and business impact - then iterating on agent behavior based on real usage data and stakeholder feedback.
Cross-Functional Collaboration: Partnering with stakeholders across the company to define agentic use cases, translate business requirements into technical features, and deliver products that create measurable ROI - from data preprocessing through product deployment.