דרושים » הנדסה » Senior Applied AI Scientist

Requirements:
Engineering Foundations:
- Strong Python skills- you write clean, idiomatic, well-tested code and understand the language deeply.
- Hands-on experience using coding agents(Cursor, Claude Code, GitHub Copilot, or similar) to build complex software systems. You know how to delegate effectively to AI assistants and review their output critically.
- Experience with multiple database paradigms- both SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis, DynamoDB, or similar). You can choose the right tool for the job.
- Experience designing and integrating with third-party APIs- REST and gRPC. Comfortable building robust clients, handling auth, retries, rate limits, and schema evolution.
- Production experience with Docker and Kubernetes- containerizing services, writing manifests, and debugging deployments.
- Strong Linux fundamentals- confident in bash and the terminal; you can navigate, script, and troubleshoot a server without reaching for a GUI.
- Experience building cloud-native tools on AWS, GCP, or Azure (compute, storage, queues, serverless, IAM).
AI / LLM Expertise:
- Solid understanding of what an LLM is and how it works- tokenization, attention, context windows, sampling, and the practical implications of each for system design.
Strong grasp of modern patterns for integrating LLMs into real workflows, including RAG, MCP (Model Context Protocol), vector databases, agents, tool use, and context engineering- with hands-on experience building with several of them.
- Production experience implementing LLM-powered systems end-to-end, using relevant tools and frameworks (e.g. LangChain, LlamaIndex, LangGraph, Haystack, Pydantic AI, vector stores like Pinecone/Weaviate/pgvector, observability tools like LangSmith or Langfuse).
- Solid foundation in core ML concepts; embeddings, evaluation, overfitting, generalization, and how classical ML relates to and differs from modern LLM-based approaches.
Nice to Have:
- Experience fine-tuning or distilling open-source models.
- Contributions to open-source AI/ML projects.
- Experience with streaming, real-time systems, or low-latency inference.
- Familiarity with prompt evaluation frameworks and LLM-as-judge methodologies.

Requirements:
MSc or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
5+ years of experience in data science or applied machine learning, plus 2+ years in ML engineering or software engineering, with direct responsibility for deploying and maintaining production systems.
Proven ownership of significant AI products or features. You have been a primary technical driver, taking ambiguous problems from initial concept to a working product used by real customers.
Strong foundations in machine learning and statistics, including experimental design, model evaluation, and practical experience with NLP, embeddings, clustering, or related methods for analyzing unstructured data.
Strong Python, SQL and Typescript skills, alongside solid software engineering practices: modular architecture, automated testing, version control, code reviews, and maintainable production code.
Hands-on experience building LLM-powered applications or AI agents beyond the prototype stage, including tool calling, structured outputs, context management, and systematic evaluation
Experience deploying and operating systems in a cloud environment, including containerization, CI/CD pipelines, logging, monitoring, and debugging production issues.
Strong product judgment and independent execution. You can define milestones, prioritize experiments, communicate technical trade-offs, and collaborate effectively across product, engineering, and business teams in a fast-moving environment.
Advantage
Experience as a core technical contributor at a high-growth startup, building new products and scaling them as adoption grows.
Experience in advertising technology, marketing analytics, search, information retrieval, ranking, or recommendation systems.
Familiarity with agent frameworks and SDKs such as ADK, LangChain, or comparable tooling.

Requirements:
5+ years of hands-on experience applying NLP/DL algorithms, techniques to real-world problems, with strong intuition for textual data and model behavior.
Experience working with LLMs, with a strong grasp of practical best practices and the judgment to apply them throughout the R&D lifecycle when building task-specific models.
Strong proficiency in Python and modern DL frameworks (PyTorch, HuggingFace, etc.).
Experience designing experiments, defining metrics, and analyzing model failures.
A strong drive to solve complex AI security challenges, with both curiosity and pragmatism.

Requirements:
What Were Looking For
5 years of experience in cloud security, cloud engineering, or building security products for cloud environments.
Deep knowledge of public cloud security fundamentals, such as:
IAM (roles, policies, permissions boundaries, identity federation)
Network security (security groups, NACLs, firewall constructs, private connectivity)
Logging and monitoring (cloud audit logs, flows, detections, telemetry pipelines)
Governance and posture (misconfigurations, guardrails, policy-as-code concepts)
Strong coding skills with the ability to develop end-to-end production features (not just notebooks/POCs).
Hands-on experience building with modern AI systems (LLMs, retrieval, structured outputs, agents/tool use, evals) and a mindset for reliability, security, and guardrails.
Practical engineering instincts: you know how to trade off quality/speed/cost and ship incrementally without breaking trust.
Excellent communication and team collaboration skills; comfortable working cross-functionally.
Fluency in English.
Youll be a great fit if
You think like a product engineer: focused on impact, users, and shipping.
You love getting things done and taking ownership end-to-end.
Youre excited about building AI systems that operate safely inside real cloud environments - with real consequences and real customers.

Requirements:
Experience in building and shipping meaningful, production-grade agentic systems - not just demos or prototypes. Were looking for people who can walk us through what they built, the real problem it solved, and the value it created.
Deep, in-depth understanding of LLMs - including how they work under the hood (transformer architecture, tokenization, attention, sampling), their failure modes, and the practical tradeoffs between models, prompting strategies, fine-tuning, and retrieval.
Strong hands-on experience with modern agentic frameworks (e.g., LangGraph, LangChain, or equivalent) and with designing multi-step, tool-using workflows.
Strong command of RAG, prompt engineering, evaluation methodologies, and techniques for improving reliability and reducing hallucinations in LLM-based systems.
Strong proficiency in Python and experience with common AI/ML libraries and APIs (e.g., OpenAI, Anthropic, Hugging Face, PyTorch).
solid software engineering practices, including writing clean, maintainable code and building robust, scalable systems.
5+ years of experience developing machine learning, AI, or software systems.
B.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or similar field.

Requirements:
Agentic Expertise: Deep experience with LLMs and the modern agentic stack (LangGraph, AutoGPT patterns, tool-calling, and orchestration). You understand how to guide an LLM through complex, multi-step tasks.
The "Full-Stack" DS Mindset: You are a coder first. You are comfortable digging into a large codebase, understanding backend services, and writing production-grade code. You don't wait for someone else to "fix the API."
Product-Driven Research: You are obsessed with impact. You choose the right tool for the job-whether its a simple heuristic or a complex fine-tuned model-based on what provides the most value to the user.
Data & System Fluency: Strong experience with Python and SQL. You understand how to interface with Postgres and ClickHouse to build the data-rich contexts our agents require.
Engineering Rigor: You care about version control, testing, and CI/CD. You treat your prompts and model configurations with the same engineering discipline as code.
The company Mindset: You take ownership, act with accountability, collaborate openly, and focus on delivering meaningful impact. You thrive in fast-moving environments, embrace ambiguity, and enjoy solving hard problems together.
Education: Bachelors or Masters degree in CS, Math, Statistics, or equivalent practical experience in a high-growth AI environment.
Communication: Full professional fluency in both Hebrew and English.

Requirements:
Senior engineering depth: You have 6+ years of experience building and operating production software, with strong backend and distributed-systems fundamentals and experience designing APIs and services.
Shipped agents, not demos: You have taken an LLM agent system with tool use, multi-turn interaction, and planning to real users, and you can talk concretely about how it failed and what you did about it.
Architectural judgment: Informed opinions on single-agent vs. multi-agent design, orchestration patterns, and the current framework and SDK landscape, with the pragmatism to pick the boring option when boring wins.
Eval discipline: You have built or owned evaluation for an LLM system, including golden datasets, LLM-as-judge with calibration, and regression gates in CI, and you can quote the metrics you moved.
Tool design instincts: You know when a deterministic tool beats a model call, how to design tool contracts an LLM will not misuse, and how to keep cost and latency under control.
Distributed systems fluency: Streaming, stateful services, and the operational instincts to keep long-running agent sessions alive in production.
Ownership in ambiguity: You can lead an area as a hands-on IC in an early-stage environment with little existing structure. Security domain experience such as SIEM platforms, SOC workflows, detection engineering, or security query languages, and experience with modern agent SDKs and protocols such as MCP, are strong advantages.

Requirements:
WHAT YOU WILL BRING
4+ years of professional experience in ML/AI engineering.
Hands-on experience working with LLMs and building AI agents in production environments.
Strong understanding of text classification, ranking, and evaluation in NLP systems.
Proficiency in Python and modern ML frameworks.
Deep knowledge of deploying and maintaining AI systems at scale.
Experience with cloud platforms, particularly AWS.
Experience working independently in fast-paced, mission-driven environments
Bachelors or Masters degree in Computer Science, Data Science, or a related field


NICE TO HAVE
Experience fine-tuning LLMs for domain-specific or task-specific use cases.
Background in cybersecurity applications (e.g., threat detection, incident response, log analysis).
Familiarity with agent-based architectures and autonomous systems.

Requirements:
You've shipped agentic systems to production - real orchestration, tool use, structured outputs, and the failure modes that only appear at scale. Not "I've called an LLM API."
You've built the evaluation discipline, not just consumed it: trajectory tests, golden datasets, regression gates, offline replay. "It seems better" is not a metric, and you have opinions about what is.
Deep backend and distributed-systems engineering. Strong Python, and comfort with workflow orchestration (Temporal or equivalent), streaming, and cloud-native infrastructure. Agent platforms are systems problems wearing an AI hat.
Fluency across the modern agent stack - LangChain/LangGraph-style frameworks, multi-provider routing, structured output contracts, prompt and context engineering - with the judgment to know which parts are load-bearing and which are fashion.
Security literacy. Enough to reason about whether an agent's security output can be trusted, and to argue with researchers on the merits. You don't need to be a vulnerability researcher.
Influence without authority. You'll change how three teams work with no one reporting to you. Show us where you've done that.
Advantage: experience with AI/LLM security - red-teaming agents, prompt injection, or agentic attack patterns.
Advantage: background in cybersecurity, detection engineering, or WAF/mitigation systems.
Advantage: you've driven AI adoption across a whole company, not only an engineering org.

Requirements:
Excellent software engineering skills and substantial experience with Python.
Hands-on experience building advanced systems with modern language models.
Deep familiarity with several areas relevant to AI agents, such as planning, tool use, retrieval, memory, structured generation, model adaptation, or evaluation.
The ability to turn an ambiguous problem into a technical research plan and then into working software.
Strong experimental instincts: you form clear hypotheses, design meaningful tests, and make decisions based on evidence.
Sound judgment about when to use an AI technique and when a deterministic approach is more effective.
Ownership of the complete result, including its behavior after reaching production.
Nice to Have:
Developing autonomous or long-running agents that interact with real systems.
Building evaluation environments, simulations, or benchmarks for complex model behavior.
Experience in cybersecurity, identity, security operations, or enterprise automation.
Applying published research or your own research to production problems.