דרושים » הנדסה » Researcher

Join our company, an innovative startup building a fully-managed LLM-inference platform, that enables data heavy enterprises to perform any AI task at any scale without limits.
We're looking for an Experienced Performance Researcher to join our founding team. Youll be responsible for building and optimizing scalable cloud infrastructure solutions tailored for AI workloads. This role offers a unique opportunity to directly shape our infrastructure strategy, improve system reliability and performance, and contribute to establishing our company as a leader in adaptive AI compute management.
Join us to tackle the magic that make AI tick under the hood and build the backbone powering the AI revolution.
What Youll Do
- Design and build high-performance distributed inference pipelines for LLMs, focused on large-batch, non-real-time scenarios.
- Optimize GPU memory usage, kernel execution, and communication across nodes (NCCL, MPI, etc.).
- Own CUDA kernels, compiler-level tricks, and multi-GPU scheduling logic.
- Lead profiling and performance tuning for throughput, and cost- down to the kernel level.
- Collaborate with infra, product, and research teams to define SLAs, resource allocation logic, and runtime behaviors.
- Help build the core infrastructure that will run LLM workloads across hybrid GPU environments (cloud/on-prem/self-hosted).

Were looking for driven and talented people like you to join our R&D team and our mission to change the future of cloud security.

As a Senior Software Engineer on the KSPM team, you will join a mission-critical group building and scaling one of the companys core and fastest-growing security products. Our team enables customers to secure their Kubernetes environments across every major cloud provider, delivering deep visibility into cluster misconfigs, RBAC risk, admission control, compliance drift and container vulnerabilities.

In this role you will design the backend services and data models that discover, onboard, scan, and represent K8s workloads, APIs, and configurations at scale. You will own core services end-to-end, drive architectural improvements and partner with security researchers to turn complex threat vectors into actionable insights.

We are looking for backend engineers who care deeply about software design, cloud-native infrastructure, and container security. This is a high-impact role with real ownership - lead features end-to-end, mentor teammates, and help shape the technical direction of our KSPM offering.

What Youll Do:
Design & build at scale: Design, implement, and maintain scalable backend services that discover, onboard, scan, and analyze Kubernetes environments across multi-cloud and managed clusters.
Model K8s security: Map and represent Kubernetes resources as clean data models that surface misconfigurations, exposure risks, and compliance violations.
Integrate detection engines: Integrate and evolve detection engines for container vulnerabilities, secrets, RBAC risks, misconfigurations, and compliance frameworks.
Collaborate on research & product: Partner with security researchers and product managers to translate novel findings and complex requirements into impactful features.
Design for quality: Write clean, well-tested code in Python or Go, with thoughtful abstractions and APIs that keep the system correct and maintainable as it grows.
Lead & mentor: Lead design discussions and code reviews that uphold high engineering and security standards across the team.
Own it end-to-end: Own features through their full lifecycle - from technical specification and design through implementation, testing, and release.

Forward Deployed Engineering sits at the frontier of that work. We embed with our most strategic enterprise customers, running on-prem, air-gapped, and heavily regulated environments, and own the technical arc from discovery through production.

Your day might include designing a solution architecture for a high-impact agentic use case, debugging a production routing issue deep in our codebase, deploying a new MCP server with a customer team, or aligning with a CTO on a rollout plan; all in service of outstanding outcomes and rapid time to value for the organizations we work with.

This is a founding function. The first FDEs will define how we conducts field engineering: the standards, playbooks, and technical bar. The work is hands-on, commercially scoped, and high-leverage: youll partner closely with Product, Research, Platform, Security, and GTM, and what you learn in production directly shapes what the core team ships next. If you thrive under ambiguity, make fast and sound decisions when the stakes are high, and get energy from closing the gap between what customers hit in production and what we build next, this role is for you.

In This Role You Will
Own technical delivery across multiple deployments from first prototype to stable production, including core engineering work that unblocks strategic accounts.
Lead deep technical diagnosis and remediation in our codebase and production environments, model routing and gateway behavior, large-PR scaling, multi-pass behavior, performance and reliability issues.
Build customer-specific extensions (MCP servers, agent skills, platform integrations) that deliver immediate value and become durable product capability; upstream them into the main roadmap where appropriate.
Embed closely with customer engineering teams: serve as their senior technical counterpart for hands-on debugging, architecture reviews, and joint implementation.
Scope work, sequence delivery, and remove blockers early; make deliberate trade-offs between scope, speed, and quality to protect delivery timelines.
Partner with Product, Research, and Platform to convert account learnings and eval-driven feedback into roadmap items, specs, and prioritized engineering work.
Codify working patterns into tools, playbooks, runbooks, and reproducible benchmarks that Support and account teams can operate independently and help scale the field engineering function across the organization.

we are seeking a promising and talented Senior DevFinOps Engineer to join our DevOps group. If you thrive in a fast-paced, dynamic environment, can handle multiple requests simultaneously, and enjoy working independently as part of a cutting-edge DevOps team, this is your opportunity to help make the world a safer place!
Key Responsibilities
Act as a DevFinOps Engineer within a highly skilled team, bridging engineering and finance to drive cloud cost efficiency across large-scale operations from development to production.
Design, develop, and maintain Avanan's cloud cost visibility, allocation, and optimization solutions - including tagging strategies, cost dashboards, budgets, and anomaly detection across accounts and services.
Implement tools and procedures for cost monitoring, forecasting, and alerting across our SaaS multi-tenant product family.
Embed FinOps practices into the CI/CD lifecycle - surfacing the cost impact of changes early, and enforcing cost guardrails as part of deployment automation.
build AI-based FinOps agents for cloud services at the infrastructure and application levels
Continuously identify and execute cost-optimization opportunities (right-sizing, reserved capacity/savings plans, spot usage, storage tiering, idle-resource cleanup) without compromising performance, reliability, or security.
Partner with engineering teams to drive cost accountability - providing unit-economics insight (cost per tenant/feature/service) and making cost a first-class engineering metric.
Plan capacity and model the financial impact of scaling decisions, balancing cost efficiency with fault tolerance and growth.
Design and shape our cost reporting, chargeback/showback, and budgeting solutions.
Execute all tasks with cloud infrastructure security and financial governance as guiding principles.

What You'll Be Doing
Own our infrastructure end-to-end - our self-hosted product, its installation and onboarding flows, our SaaS platform, and AI infrastructure.
Manage our cloud infrastructure across AWS, GCP, and Azure - networking, security, SSO, and compute.
Work closely with customers on deployments and troubleshooting.
Collaborate closely with backend, product, and R&D teams to support rapid feature delivery without compromising reliability.
Identify and eliminate operational toil through automation and tooling improvements.
Maintain security and compliance best practices across the infrastructure stack.

We are looking for a proactive and skilled Software Infrastructure Developer with a strong Agentic AI focus to join our team. In this role, you will design and build the infrastructure, tooling, and AI-powered automation that keep our engineering systems reliable, scalable, and intelligent.

You will be responsible for embedding GenAI and agentic capabilities into our internal developer platforms and CI/CD workflows - creating frameworks, libraries, and multi-agent systems that accelerate automation, improve system stability, and unlock new levels of engineering productivity. This is a hands-on role for someone who thrives at the intersection of infrastructure engineering and applied AI.

The ideal candidate brings deep technical expertise in Python (FastAPI) and/or TypeScript, Kubernetes, CI/CD (Jenkins, GitHub Actions), containerization, and cloud infrastructure - combined with hands-on experience building LLM-powered applications, agents, MCP servers, and RAG pipelines.

What you'll be doing:
AI Platform Development: Design and build internal AI platforms, reusable SDKs, libraries, tools, and MCP servers for agentic applications.
Agentic Workflow Development: Develop single- and multi-agent workflows covering planning, tool selection, delegation, state and memory management, execution, retries, fallbacks, error recovery, and human-in-the-loop approval.
AI-Powered Infrastructure & CI/CD: Integrate AI capabilities into Kubernetes infrastructure and Jenkins/GitHub Actions pipelines to improve development, testing, deployment, and operational workflows.
AI Observability & Evaluation: Implement agent tracing, prompt and tool-call logging, latency and error monitoring, token and cost tracking, regression testing, and failure analysis.
AI Security & Governance: Apply guardrails, access controls, auditability, and governance practices to ensure agents operate safely and reliably.
End-to-End Platform Ownership: Own AI platform capabilities from architecture through production and build reusable solutions that accelerate adoption across engineering teams.
Cross-Functional Collaboration: Collaborate with engineering and business teams to identify high-impact AI opportunities and translate them into scalable technical solutions.
AI Research & Innovation: Evaluate emerging AI frameworks, models, and cloud technologies and promote their adoption where they provide measurable value.

We are looking for an experienced Senior Product Manager to own Runtime domain - the core engine that powers millions of workflow executions for security teams worldwide. We seek an experienced, curious, self-motivated product manager to join our team and lead our events pipeline, orchestration, and execution infrastructure.

What Youll Be Doing

Become an expert in all runtime aspects - events pipeline, orchestration, execution, Runners, and the infrastructure that powers them
Own the end-to-end Runner experience - deployment models, resource management, connectivity, and troubleshooting
Perform customer research and technical deep-dives to understand scale requirements, reliability needs, and pain points
Analyze customer escalations to identify patterns and translate them into actionable engineering requirements
Make critical decisions with respect to architectural trade-offs, reliability, performance, and quality to meet business objectives
Work closely with engineering to define, develop, and launch new runtime capabilities across our distributed execution layer

Were looking for a Senior Applied AI Engineer who combines deep data science expertise with the engineering skills to turn research into reliable, production-ready products.
Youll be a hands-on technical leader, owning significant AI capabilities from problem definition, academic survey, and system design through research, experimentation, deployment, and continuous improvement. Your work will span classical machine learning, large-scale data analysis, and AI agents that power brand intelligence, market research, and performance marketing.

You should have a track record of driving complex projects, not just contributing to them, and be comfortable making technical decisions, navigating ambiguity, and delivering in a fast-moving startup environment. Youll build systems that Fortune 500 marketing teams rely on to make consequential business decisions.
Responsibilities
Own AI capabilities end to end. Translate business and product needs into well-defined problems, research plans, and technical designs. Take solutions from initial exploration through production deployment and ongoing improvement.
Develop and improve our core algorithms.
Build production-grade AI agents - performance marketing, market research agents, auto-ML agents.
Turn research into maintainable software. Build reusable modules, data pipelines, and services with clear interfaces, automated tests, and robust deployment practices-not just standalone prototypes.
Own quality and performance in production. Monitor system behavior, investigate failure cases, and continuously improve accuracy, reliability, latency, and cost as usage and data volumes grow.
Drive technical decisions and execution. Choose the right approach for each problem, balancing statistical methods, classical ML, and LLM-based systems. Make explicit trade-offs between research depth, delivery speed, and operational complexity.
Provide hands-on technical leadership. Partner with product and engineering to shape priorities, lead technical initiatives, review designs and code, and mentor teammates.

As a Staff Software Engineer, you will serve as a technical authority and play a pivotal role in scaling our core platform. Your focus will span architecture, security research, and engineering excellence:

Lead & Architect: Design, build, and scale high-performance software while driving architectural best practices across the R&D organization.
Security & AI Innovation: Drive deep-dive research into cloud, SaaS ecosystems, and AI agents to build proactive detection models, mitigation strategies, and threat intelligence.
Engineering Excellence: Elevate code quality, testing standards, system monitoring, and documentation across teams.
Cross-Functional Ownership: Partner closely with Product and R&D to translate complex security requirements into scalable technical solutions.
Scale & Optimize: Identify and resolve systemic performance bottlenecks and architectural challenges.
Mentorship & Leadership: Act as a technical mentor, driving engineering standards and fostering a strong knowledge-sharing culture.

This role sits at the intersection of AI and high-performance systems engineering, focused on solving real-world problems under strict constraints. You will work on systems where performance and reliability are critical and where improvements have a direct, measurable impact on real-world safety.

This is a senior, systems-focused role with end-to-end ownership over performance and reliability of production computer vision pipelines. You will define optimization strategies, identify bottlenecks across the system, and drive improvements under real-world constraints.

What youll do
Build and optimize real-time computer vision pipelines running on edge systems processing live maritime video streams (e.g, NVIDIA Jetson, Triton Inference Server).
Take models from research and turn them into production-ready, reliable components deployed on vessels.
Profile and improve end-to-end system performance across: multi-camera video ingestion; preprocessing; inference; postprocessing.
Identify and resolve bottlenecks across CPU, GPU, memory, and pipeline coordination.
Make and justify tradeoffs between latency, accuracy, stability, and resource utilization.
Design and implement robust data and inference pipelines (video -> model -> actionable output for crew).
Develop benchmarking and evaluation workflows to measure performance end-to-end and support release gating.
Build and improve observability tools, including logging, monitoring, and debugging workflows for production systems.
Define and maintain clear interfaces between research code and production systems.
Work closely with research and backend teams to integrate new models into production systems.
Continuously improve system efficiency and reliability under hardware and runtime constraints.