דרושים » הנדסה » AI Software Engineer

Requirements:
What we need to see:
B.Sc. or above in Computer Science, Computer Engineering, or a related field, or equivalent experience.
5+ years of hands-on experience in software engineering, with demonstrated ownership of production systems from design through deployment.
Expert-level programming skills in C++, with strong Python skills alongside it.
Strong understanding of the full stack, including hardware: memory, I/O, networking, accelerators, and where real performance bottlenecks occur.
Current, practical knowledge of how to build systems around AI models: agent loops, tool interfaces, context retrieval and management, and common failure modes.
Understanding of inference serving, including request lifecycle, batching, caching, and the tradeoffs between throughput, latency, and cost.


Ways to stand out from the crowd:
Experience writing hardware simulation software - network, system, or architectural simulators, models, or testbenches.
Networking experience - protocols, fabrics, switching, or RDMA - and experience working alongside silicon, systems, or architecture teams.
Hands-on experience with inference serving engines such as vLLM, TensorRT-LLM, or Triton Inference Server, including low-level internals such as KV cache, batching and scheduling, and quantization, and related performance work such as profiling and GPU programming.
Hands-on experience building or fine-tuning LLMs or other generative models.
Agent workflows, tooling, or context pipelines adopted by other engineering teams.

Requirements:
What we need to see:
B.Sc. or M.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
5+ years of relevant industry or research experience. This is not an entry-level position; coursework and personal projects do not substitute for production or research experience at scale.
Hands-on experience running and analyzing AI workloads on multi-node systems - distributed training or large-scale inference - including measuring where time and resources are actually spent.
Demonstrated performance analysis work: building analytical or simulation models of real systems, validating them against measurement, and identifying bottlenecks that led to design or deployment changes.
Strong systems-level thinking across the full AI stack, from model and framework behavior down through compute, memory, storage, and network.
Track record of translating research findings and workload analysis into concrete software and hardware specifications that engineering teams acted on.
Strong programming skills in Python and C/C++, applied to performance modeling, data analysis, and prototyping.


Ways to Stand Out from the crowd:
Deep understanding of data centers, network topologies, and communication protocols.
Familiarity with GPU clusters, collective communication, storage systems, and AI networking bottlenecks.
Experience with distributed training, distributed inference, or large-scale AI serving systems, including the performance metrics and deployment strategies that govern them.
Experience in agentic programming and AI tooling.
Track record of turning academic research into concrete software, hardware, or architecture requirements, and of leading complex multidisciplinary projects with measurable production impact.

Requirements:
- Deep experience with CUDA programming, GPU architecture, and low-level performance engineering.
- Fluency with Python and C++, and a mastery of profiling tools like Nsight, nvprof, perf, etc.
- Experience building systems for large-scale distributed training or inference (PyTorch, DeepSpeed, Ray, Horovod, etc.).
- Hands-on familiarity with cluster and container orchestration tools (Kubernetes, Slurm, Docker).
- Self-motivated and able to operate independently in a fast-moving startup environment.
- Strong analytical skills and a passion for elegant performance wins.
- A collaborative team player with strong interpersonal skills, a positive and easygoing attitude, and the potential to grow into a leadership role.
- Prior experience building inference runtimes or scheduling frameworks.
- Experience with serverless GPU models, model parallelism, tensor slicing, and batching tricks.
- Contributions to open-source HPC or ML infra projects.
- Understanding of AI/ML privacy and compliance concerns in enterprise environments.
- Track record of working on distributed systems at bleeding-edge research labs or infrastructure teams.

Requirements:
Requirement for success:
5+ years in software engineering, MLOps, or ML engineering with hands-on experience deploying ML models to production.
Strong Python fundamentals and deep understanding of transformer architectures, tokenization, and NLP frameworks (PyTorch, HuggingFace).
Proven experience deploying and scaling LLMs for real-time inference-ideally on platforms like SageMaker, Vertex AI, or similar.
Expertise in GPU optimization, distributed training, and CPU-based inference optimization.
Strong cloud and Kubernetes background (EKS/GKE/AKS, Helm, Terraform, CI/CD for ML).

Nice to Haves
Background in building or operating internal ML platforms.
Knowledge of evaluation frameworks for LLM quality, robustness, or observability.
Experience working with data-driven ML operations, cost optimization, and model observability.
Understanding of security implications in ML pipelines.
Familiarity with multi-model orchestration, vector DBs, or retrieval pipelines.

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:
Basic Qualifications:
Applied Generative AI: Proven track record of shipping complex LLM systems to production (e.g., Multi-agent architectures, RAG systems, tool/function calling, complex dialogue managers) coupled with a passion for continuous AI innovation.
5+ years of hands-on software development experience, focusing on building complex, distributed systems.
System Architecture & Cloud: Strong system design skills in cloud-native environments (AWS/GCP), containerization, and modern CI/CD automation.
Engineering Quality for AI: Deep understanding of testing non-deterministic AI systems (eval-driven development, deterministic regression testing, automated benchmarking).
Technical Leadership: Proven experience leading architectural designs, driving cross-functional alignment, and mentoring engineers in AI engineering principles.
Fluent English: High proficiency in both written and verbal communication.
Work Authorization: Authorization to work in Israel.
Preferred Qualifications:
Python Expertise: Deep proficiency in Python and modern backend framework development, with strong API design and asynchronous programming skills.
Vector Search & AI Infrastructure: Hands-on experience with Vector Databases (e.g., Pinecone, Qdrant, Milvus, pgvector) and modern orchestration frameworks (e.g., LangChain, LlamaIndex, AutoGen/CrewAI).
Model Customization & Fine-Tuning: Practical experience with model fine-tuning techniques (e.g., LoRA, QLoRA, SFT, DPO/RLHF) and dataset curation.
LLMOps & Observability: Hands-on experience with LLM tracing and observability stacks (e.g., LangSmith, Phoenix, Arize, Weights & Biases).
Inference & Serving: Familiarity with LLM serving engines and frameworks (e.g., vLLM, TensorRT-LLM, Ollama).
Domain Experience: Background in Conversational AI, Dialogue Management, or Recruitment/HR tech automation.
Data Science/ML Foundation: Formal background or practical grounding in NLP, Machine Learning, or Data Engineering.

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:
6+ years of backend software engineering experience designing, building, and maintaining large-scale, high-throughput production systems

Strong coding skills, Ability to write clean, maintainable code, OOP familiarity, package design, microservices etc.
Note: Work is in python, but strong engineers with deep Java/C# backgrounds who have some Python experience and are willing to transition fully are highly encouraged to apply.

Solid Database design & SQL skills, Deep understanding of SQL, experience working with relational and/or bigdata (columnar) databases, ORMs, and efficient query design.

API & Performant Design Proven experience - building robust systems, you understand how to handle concurrency, ETL tradeoffs, building fault-tolerant best effort data flows