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Company Overview: Our mission is to enable its global customers to protect and save lives by enhancing digital investigations and intelligence gathering to accelerate justice in communities around the world. Our AI-powered Digital Investigation Platform enables customers to lawfully access, collect, analyze and share digital evidence in legally sanctioned investigations while preserving data privacy. Thousands of public safety organizations, intelligence agencies and businesses rely on our digital forensic and investigative solutions-available via cloud, on-premises and hybrid deployments-to close cases faster and safeguard communities.?To learn more, visit us in our website.

Position Overview: As a Senior Software Developer in Cellebrite Labs, your daily mission involves designing and developing advanced capabilities to extract digital evidence from modern Android and IOS devices. You will tackle some of the toughest security protections such as PAC, Secure Elements, Code Signing, Hypervisors, and more. Working in the heart of the Digital Intelligence Labs group, you will contribute to productizing world-unique capabilities that enable law enforcement and private sector customers to conduct legally sanctioned forensic investigations effectively and securely. This role requires a combination of deep technical knowledge, creativity, and persistence to solve complex problems while collaborating closely with top researchers and engineers. Your work will have a direct impact on investigations that save lives and uphold justice globally.


Key Responsibilities:
* Lead and contribute to the development of new extraction technologies for Android and IOS platforms.
* Analyze and reverse-engineer low-level OS internals and security mechanisms.
* Develop robust system -level software in C, C ++, and Python.
* Collaborate with cross-functional teams to design, TEST, and integrate new capabilities into our Digital Intelligence platform.
* Stay current with emerging mobile security technologies and forensic challenges.
* Share knowledge and best practices within the team to elevate overall engineering quality.

Office Location:
Tel Aviv

Self-service enablement: Build secure, automated pathways that accelerate development while keeping organizational standards intact.
Architectural modularization: Design Kubernetes-native abstractions - controllers and operators - and decouple infrastructure into independent service lifecycles that support rapid iteration.
Everything as code: Treat infrastructure, policy, and configuration with the same SDLC as application software - versioned, reviewed, tested, and CI-gated.
Proactive reliability: Own production through robust observability, clear SLOs, and high operational hygiene; define alerting that fires correctly and lead blameless postmortems that fix the system.

We are seeking a hands-on technical leader to architect and lead the development of production LLM-based applications. This role will lead a cross-functional team of software engineers and data scientists and will own architecture, delivery, evaluation, observability, and production readiness.



The ideal candidate is a proven software leader who has delivered complex production-grade systems and can align engineering, data science, product, and platform work into one cohesive execution model. The role requires strong software architecture, applied AI judgment, disciplined execution, and people leadership, with a focus on turning ambiguous product goals and exploratory AI work into reliable production software.



Responsibilities:

Lead a cross-functional team of engineers and data scientists developing LLM-based applications.
Translate product and business goals into technical roadmaps, milestones, and deliverable plans.
Own end-to-end delivery from architecture and experimentation through deployment, monitoring, evaluation, and continuous improvement.
Design LLM workflows using RAG, tool calling, structured outputs, agents, deterministic logic, and human-in-the-loop patterns where appropriate.
Establish evaluation-driven development practices for LLM features.
Define quality metrics, regression tests, golden datasets, production feedback loops, and observability standards.
Ensure systems meet production standards for scalability, performance, reliability, security, and maintainability.
Provide technical guidance, architectural oversight, mentorship, and career development for team members.
Manage priorities, scope, risks, and trade-offs across multiple streams of work.
Partner with product, design, DevOps, data engineering, security, and other technical leaders.
Drive continuous improvement in delivery quality, engineering practices, and team effectiveness.


Self-service enablement: Build secure, automated pathways that accelerate development while keeping organizational standards intact.
Architectural modularization: Design Kubernetes-native abstractions - controllers and operators - and decouple infrastructure into independent service lifecycles that support rapid iteration.
Everything as code: Treat infrastructure, policy, and configuration with the same SDLC as application software - versioned, reviewed, tested, and CI-gated.
Proactive reliability: Own production through robust observability, clear SLOs, and high operational hygiene; define alerting that fires correctly and lead blameless postmortems that fix the system.

We are looking for a Senior Software Engineer with deep system-level experience to help strengthen and scale the infrastructure behind our security agent. In this role, you will work closely with multiple teams across the organization, focusing on building robust foundations that support reliability, performance and growth of an already production-proven agent.

Responsibilities
Design and improve core infrastructure components of the LayerX security agent
Strengthen scalability, reliability and performance of the agent in real-world customer environments
Build and evolve communication, update and configuration mechanisms for the agent
Work across teams to support agent-related infrastructure needs and long-term scalability efforts
Debug complex system-level issues and lead root cause analysis in production-like environments
Influence architectural decisions related to agent infrastructure and scale

We're looking for a Software Engineer to join our core engineering team and help build the infrastructure behind one of the fastest-growing AI APIs. You'll work on systems that handle massive scale across a distributed microservices architecture running on AWS. You'll ship fast, take ownership of critical systems, and solve hard infrastructure problems as we grow.

This is a great role for an engineer who loves building ambitious systems from scratch, and wants to tackle the kind of scale and complexity challenges typically reserved for much larger companies.

What Youll Do

Design and build high-performance distributed systems

Design and implement backend infrastructure and API endpoints

Build and optimize real-time data pipelines that process billions of events per day across distributed queues and stream processors

Improve performance, monitoring, and reliability across the stack

Own core systems and contribute to key architectural decisions

Help shape a strong engineering culture focused on velocity and quality

As a Software Engineer - Scraping Specialist, youll build the systems that power how AI interacts with the live web. Youll design and implement large-scale, intelligent web acquisition pipelines, making sense of an ever-changing online ecosystem and transforming it into structured, high-quality data.

This role blends technical understanding of browsers, networks, and automation with hands-on engineering. Youll work on adaptive systems that navigate modern websites, understand evolving web interaction ,detection systems, and gather meaningful information safely, efficiently, and at scale.

Your work will directly shape ability to provide AI agents with accurate, real-time knowledge of the world , bridging the gap between the open web and intelligent reasoning.

What Youll Do
Build distributed data acquisition systems that capture and structure the live web
Investigate and analyze browser internals, fingerprinting, and anti-automation systems to develop stealthy, adaptive orchestration layers
Prototype and deploy intelligent automation frameworks using Playwright, Puppeteer, and low-level browser control protocols (CDP)
Conduct hands-on research into network flows, JavaScript-based protections, and emerging web standards affecting automation
Collaborate with AI and infrastructure teams to integrate real-time web data into retrieval pipelines and LLM-powered agents
Translate deep technical insights into production-grade components, balancing research freedom with engineering rigor
Continuously evolve capabilities in resilience, speed, and authenticity of web interaction

We are looking for a Senior AI Software Engineer to build the agents that sit on top of our search platform: systems that take a user's intent, break it into steps, gather and verify information from the web, and return answers an agent can act on. You will work across applied research and engineering, designing how agents plan, call tools, retrieve, and reason so they accomplish open-ended tasks reliably and at scale.

This is a high-ownership role at the intersection of agent systems, retrieval, and product. You will turn frontier-model capabilities into dependable, production-grade agent behavior, and you will own that behavior end to end, from the prompts and tools to the evaluation that proves it works.

In this position, your responsibility will be to

Design and build AI agents that plan, retrieve, and reason over real-world information to complete open-ended tasks

Build the tool interfaces and context engineering that let frontier models use our search and other tools effectively

Mine and analyze usage data to build agents that learn and improve continually from how they are used

Turn new model capabilities into reliable product features, and own them from prototype to production

Define the evaluations, metrics, and guardrails that prove an agent is accurate, grounded, and safe

Improve agent quality across reasoning, planning, tool use, and grounding against real user tasks

Build the backend and infrastructure that run agents reliably under high volume

Collaborate with the search, ML, and product teams to make agent and platform capabilities reinforce each other

Were looking for a Backend Engineer to contribute to the development of our hyperscaler platform.

Your responsibilities will include:

Develop fault-tolerant, reliable cloud services: cloud infrastructure, managed databases, managed Kubernetes, billing, monitoring systems, ML service and more.

Were looking for engineers who combine strong systems skills with product sense: understanding who uses the platform, why certain capabilities matter, and making pragmatic trade-offs to maximize impact. On our team, engineering work is expected to be connected to real users and outcomes - youll regularly align with internal stakeholders, clarify requirements, and help drive prioritization.

In this role, you will:

Design and implement new functionality in YTsaurus core (C++) with production reliability in mind.
Build and evolve platform-level capabilities: platform architecture and operating model-multi-cluster growth, shared primitives, and a consistent experience that scales with new teams and use cases.
Improve end-to-end platform experience for internal (and external-facing) users: APIs, guardrails, debugging workflows, and automation.
Own production quality: incident response / on-call rotation, root cause analysis, and turning learnings into durable fixes.
Example projects
Roll out sharded YTsaurus masters (incl. Kubernetes operator support) and build automatic balancing of metadata across master cells (consensus groups) to remove control-plane bottlenecks and unlock 10-100x cluster growth.
Make CHYT interactive SQL faster and more predictable at high load via performance work like data-skipping / min-max-style indexes and improved execution introspection.
Turn Orchestracto into a platform product by defining the building blocks, developer experience, and governance for how teams create and share workflows.
Scale and harden Parquet-on-S3 for native YTsaurus workloads by tackling replication/movement, consistent lifecycle semantics, and master-server metadata optimizations for performance and reliability.
Design and ship complete, trustworthy audit trails for data changes (who/what/when) across heterogeneous storage and compute paths.
Tech stack
Core: modern C++ (C++20, async + multithreaded primitives)
Services & tooling: Go and Python (microservices, utilities, integration tests)

This role will require expertise in distributed systems to build large-scale LLM training platform.

Your responsibilities will include:

Designing and developing LLM training platform.
Maintaining our ML infrastructure, ensuring optimal performance, scalability and reliability.
Improving job scheduling strategies to minimize resource fragmentation.

As a Senior Software Engineer on a search vertical, you'll help build systems that capture what is true about a domain. You'll work on extracting, connecting, retrieving, and reasoning over knowledge from the web and beyond, turning messy, real-world data into structured, trustworthy knowledge so AI agents can answer questions with precision and completeness.

You will optimize the retrieval and knowledge layer for a vertical: how a domain's content is indexed, linked into entities, ranked, and continuously refreshed, then measured and improved against rigorous IR metrics. This is an information-retrieval and systems role spanning indexing internals, hybrid retrieval, and entity resolution, with the goal of making each vertical the best place in the world to search its domain.

In this position, your responsibility will be to

Design, implement, and operate the retrieval system for a search vertical

Connect and tune the data pipeline, from ingestion to relevance tuning

Build knowledge-graph and entity-resolution layers: entity linking / NER, ontologies, and graph databases (Neo4j or similar)

Develop structured-extraction pipelines over messy, unstructured domain data

Reason about freshness and trust: model how confident we are in a fact and how stale it has become before we serve it

Define evaluation and quality metrics for relevance and drive measurable improvements

Collaborate with crawling, indexing, and ML teams to ensure retrieval and ranking requirements are met

Enable safe experimentation with retrieval, ranking, and extraction strategies

We're looking for a Senior Software Engineer to join our Accounts Payable (AP) group and own mission-critical domains powering high-scale financial workflows. You'll work inside an AI-native feature factory, real infrastructure, real tooling, and a team that treats AI as a core part of how software gets designed, built, tested, and shipped.
You'll design and evolve scalable systems for invoice processing and bill lifecycle management, applying spec-driven design, SOLID principles, proven design patterns, and strong automated-testing standards, and you'll help set the bar for what AI-augmented engineering looks like at scale.
We're looking for a collaborative team player with strong ownership and a proactive, innovative mindset, someone who challenges the status quo, drives work to a complete and deterministic finish, and is genuinely excited about what AI-augmented engineering unlocks.
In this role, you will:
Design and build scalable, high-performance microservices in a complex, high-volume financial production environment
Own critical components end-to-end and architect systems that perform under load and support future scale
Lead technical design discussions and influence architectural decisions
Drive spec-driven development with SOLID principles and proven design patterns
Write clean, maintainable, testable code with strong emphasis on unit and integration testing
Improve production quality, observability, and reliability standards
Collaborate closely with Product, Architects, DevOps, and cross-functional teams
Lead and mentor engineers, setting high engineering standards
Push the frontier of AI-assisted development - using AI tools hands-on across design, coding, testing, and review to raise the bar on both speed and quality

We are looking for a talented and motivated Senior Software Engineer to join our growing team. In this role, you will be responsible for designing, developing, and maintaining scalable end-to-end systems.
You will collaborate closely with product managers, architects and other engineers to deliver high-quality solutions in a fast-paced, innovative environment.
What will your job look like:
Design, Develop and Maintain scalable and high-performance end-to-end systems
Collaborate with cross-functional teams to define and deliver new features
Write clean, maintainable, and well-documented code
Participate in code reviews and contribute to best practices
Troubleshoot, debug, and optimize existing systems.

We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).