לוח משרות דרושים הייטק AI , לחיפוש זה נמצאו 302 משרות

Requirements:
What You Need for the Role
6+ years building and shipping production backend systems, including experience leading technical initiatives, processes and teams.
5+ years experience managing engineers, hiring, running 1:1s, giving feedback, and developing people, not just tech-leading.
Strong server-side engineering in at least two of: Node.js / JavaScript, Python, Java. Able to own a feature from data model to API.
Solid PostgreSQL - schema design, query writing and optimization, working with relational data at scale.
Proven experience designing and operating APIs (HTTP/REST/GraphQL) and a working command of auth and access control: OAuth 2.0, API keys, scopes, and RBAC.
Hands-on experience building LLM-powered features in production - direct work with LLM provider APIs (Anthropic, OpenAI, Gemini, Ollama or similar), function/tool calling, token optimization, structured outputs, and prompt engineering.
Experience with agentic systems: designing tool interfaces an LLM can use reliably, multi-step agent loops, and handling failure and recovery.
Working knowledge of MCP (Model Context Protocol) - or the depth in tool-calling and AI integration to ramp on it quickly - and a clear sense of what makes a tool definition easy for a model to use well.
Experience evaluating LLM systems: building eval suites, measuring tool-call and decision accuracy, and instrumenting AI behavior with tracing and observability.
A security mindset -treat prompt injection, untrusted inputs, least-privilege design, and data exposure as first-order concerns, not afterthoughts.
Comfortable in a small, fast-moving startup - high ownership, low hand-holding, and a willingness to build processes and infrastructure from scratch when needed.


Advantages
Retrieval and context engineering - RAG, embeddings, vector search, and semantic retrieval used to ground agents in API docs, schemas, and internal knowledge.
Experience running an MCP server, or a comparable AI-tooling integration, in production.
Cloud infrastructure (AWS / GCP / Azure), containers, and IaC.
Cybersecurity domain knowledge.
Open-source contributions to AI / agent tooling.

Requirements:
Proven experience (M.S., Ph.D., or equivalent industry experience) in Computer Science, AI, or a related field.
Strong software engineering skills are non-negotiable; demonstrated excellence in solving complex engineering problems, particularly in Python.
A deep, "under the hood" understanding of modern deep learning, including network architectures, loss functions, optimization, and transformers.
Extensive hands-on experience with PyTorch.
A proven track record of training large-scale deep learning models on large datasets.
Ability to thrive in a fast-paced, high-impact environment where solutions are often unclear and require deep exploration.
Advantages:
Experience with training large models (e.g., LLM, VLM, VLA).
Prior experience working with robotic learning systems.
Experience with multi-task learning, imitation learning, or reinforcement learning.
A strong publication record in top-tier AI or robotics conferences (e.g., NeurIPS, ICML, CVPR, RSS, CoRL).

Requirements:
Ph.D. in Computer Science, Electrical Engineering, Machine Learning, Robotics, or a related field
Strong publication or deployment track record in one or more of: deep learning, computer vision, reinforcement learning, imitation learning, vision-language models, or motion prediction
Demonstrated ability to go from paper to working implementation - not just theory, but shipped systems
Strong coding skills in Python; experience with C++ is a plus
Familiarity with modern ML infrastructure: PyTorch, distributed training, model optimization
Solid mathematical foundations in probability, optimization, and statistics

Requirements:
3+ years software development experience with ML workflows
Proficiency in Python and knowledge of web application design patterns
Bachelor's degree in Computer Science, Engineering, or related field
Strong analytical and problem-solving abilities
Effective communication and team collaboration skills
Creative mindset for innovation
Preferred Qualifications
Experience with ML deployment and frameworks like PyTorch
NLP/CV expertise
Non-SQL/vector/graph databases (MongoDB, Neo4j)
RESTful APIs
AWS/Azure cloud services
TypeScript/React frontend development.

Requirements:
Strong background in modern ML - LLMs, retrieval, agent systems
Experience shipping AI to production, not just notebooks
Comfort with messy, document-heavy domains
A real interest in evaluation as a discipline, not an afterthought
Bonus
Published work on agents, retrieval, or evaluation
Experience with vision-language models on document or technical-drawing data
Construction or AEC industry exposure.

Requirements:
High Ownership & Seniority: You take full end-to-end responsibility for complex growth projects, domains, and metrics with minimal guidance.
Mastery of AI Builder Tools: Advanced hands-on experience building, prototyping, and shipping with modern AI tools (Claude Code, Cursor, Replit, v0, Bolt.new, custom scripts, or LLM agents).
Proven Full-Stack Growth Mindset: Deep understanding of consumer products, acquisition mechanics, CRO, messaging, onboarding journeys, and retention dynamics.
Proven Builder Track Record: Demonstrated experience developing AI-driven projects, automations, scrapers, sub-products, apps, content systems, or custom workflows independently.
Leadership & Scaling Potential: Strong communication skills and the capability to lead cross-functional initiatives, guide junior team members, or step into a Team Lead role.
Data Literacy & Analytical Rigor: Ability to query data, analyze performance across the conversion funnel, and make fast, data-informed decisions.
Initiative & High Velocity: You take action without waiting for direction, learn new technologies on the fly, and thrive in ambiguous, fast-moving environments.
Advantage: Prior experience in high-scale performance marketing, UA operations, or consumer software growth.

Requirements:
4+ years shipping consumer or large-scale complex digital products - as a PM, designer, engineer, or in a hybrid role.
A track record of shipping thoughtful, user-centered features end-to-end and iterating on real data.
Strong product sense: simplifying complexity, seeing around corners, getting into details.
Design taste and UX judgment - specific, defensible opinions about flows, motion, and microcopy.
Technical fluency: comfortable reading a spec, pushing on architecture, and working with engineers as peers.
AI-native in practice: you build with AI - prototypes, experiments, automations, agents, not just documents.
Analytics baseline: comfortable thinking in SQL-shaped questions and finding the real signal.
Clear, concise communication - written and verbal.
Nice to Have:
Experience scaling consumer products from concept to a multi-million user base.
Experience with B2C AI products and interactions across mobile and desktop.
A history of shipping side projects, indie tools, or prototypes that escaped into the wild.
This Role Is Exactly Right for You If:
You'd rather ship a prototype than write a brief about one.
Given a rough direction, you come back with a plan and a proposed solution.
Your workflow doesn't end in a PRD drafted by AI.
Fast, iterative cycles feel natural, not frantic.
You enjoy collaborating with multidisciplinary teams.

Requirements:
High ownership: You take responsibility for projects end-to-end with minimal guidance.
Hands-on with AI tools: Experience building with tools like Claude Code, Cursor, Replit, or similar.
Growth mindset: You're drawn to consumer products and have a strong sense for what drives clicks, sign-ups, and retention.
Proven builder: You've developed AI-driven projects such as agents, automations, apps, content systems, or custom workflows.
Initiative: You take action without waiting for direction and approach problems in creative ways.
Background or experience in marketing and growth is a plus.

Requirements:
Strong hands-on experience building and deploying AI-powered solutions Proven experience with AI, automation, digital transformation, or process improvement within organizations. Strong technical capabilities paired with a strong business understanding. Ability to translate business challenges into practical, working solutions. Experience leading cross-functional initiatives in a matrix organization. Ability to move comfortably between hands-on execution, business process analysis, and strategic planning. Strong analytical, execution, and stakeholder management skills, including building business cases and aligning priorities and investments with senior leadership. A builder mindset and willingness to roll up your sleeves and deliver. The leadership potential to build capabilities, recruit and develop a team, and take on broader responsibility as Landa's AI transformation scales.

Requirements:
BSc in a relevant field (CS, EE, BioMed) - a must
Experience in Python development - a must
Vision AI models hands on experience - a must
Prior employment / MSc in a relevant field - an advantage
Experience in academic publishing - an advantage.

Requirements:
What you'll bring:

5+ years of Product Management experience, with a strong technical background in areas such as Technical Product Ownership, Solution Architecture, or AI/ML.
Hands-on experience with LLMs, agentic workflows, tool calling, APIs, orchestration, and platforms (e.g., Anthropic Claude, OpenAI, Microsoft Copilot).
Ability to navigate the evolving agentic AI ecosystem and evaluate emerging models, frameworks, platforms, protocols, and tools.
Strong technical understanding of AI application architecture, complex systems, and technical trade-offs, with the ability to prototype and experiment independently.
Proven ability to operate effectively in a fast-moving, ambiguous, and rapidly evolving environment.
Proven ability to collaborate effectively across Product, Engineering, AI/ML, Design, and customer-facing teams.
Excellent written, verbal, and presentation skills.
BSc in Computer Science, Engineering, or a related technical field.
Advantages:

Experience working with large enterprise customers and understanding their security, privacy, governance, and compliance requirements.

Requirements:
3 to 5 years of proven industry experience working as an AI Researcher or Data Scientist in a fast-paced environment.
M.Sc. in Data Science, Computer Science, Statistics, Mathematics, or a closely related quantitative field.
Deep theoretical knowledge and hands-on experience with classical Machine Learning algorithms and advanced statistical modeling techniques.
Proven experience researching and building with advanced AI concepts, such as RAG (Retrieval-Augmented Generation) pipelines, agentic workflows, autonomous AI systems, and rigorous LLM evaluation mechanisms.
Strong experience working with modern AI productivity and development tools (e.g., Claude Code, Cursor, GitHub Copilot, advanced prompting frameworks) to accelerate coding and research execution.
A demonstrated ability to teach yourself new concepts quickly. You proactively research, test, and propose state-of-the-art solutions rather than waiting for a rigid roadmap.
Excellent ability to communicate complex mathematical and technical concepts to non-technical stakeholders while matching the technical depth required to work seamlessly with Engineering.

Requirements:
5+ years of backend engineering experience building and operating production systems at scale, with a strong sense of ownership from design through deployment and operations.
Deep expertise in microservices architecture, API design, and distributed systems, with cloud-native design experience on AWS (or GCP/Azure) using Kubernetes and Helm.
Proficiency in Python for building scalable backend services, with a focus on clean, production-grade code and seamless integration of AI components.
Hands-on experience integrating Generative AI into backend systems, including LLMs, knowledge bases, and agent frameworks, plus familiarity with tooling such as AWS Bedrock, LangChain/LangGraph and vector databases.
Solid experience with data and messaging technologies - SQL/NoSQL databases, caching layers, and message brokers such as PostgreSQL, Redis, Kafka, RabbitMQ, and Elasticsearch.
Strong command of observability practices, with hands-on experience in tools such as Prometheus, Grafana, or Splunk for monitoring, distributed tracing, and alerting.
Fluency in agile development and delivery tooling such as Jira, with a track record of consistently shipping high-quality software in fast-moving environments.