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Junior AI/ML Engineer
·Israel
Not specifiedNot specifiedAI & Machine LearningArtificial Intelligence
Join our AI team to fine-tune open-source LLMs, integrate self-hosted models into agentic workflows, and evaluate model quality. Gain hands-on experience with the full AI lifecycle, from data preparation to production deployment and monitoring.
Responsibilities
- Prepare, clean, and analyze datasets from production conversations and synthetic examples
- Support supervised fine-tuning experiments for language models and classifiers
- Experiment with SFT, LoRA, and QLoRA techniques
- Compare base and fine-tuned models using offline evaluations and production shadow traffic
- Evaluate model quality including accuracy, routing, tool calling, hallucinations, and reliability
- Integrate self-hosted models into agentic workflows and LLM-powered services
- Help configure and test models using SageMaker, vLLM, Impala, and Baseten
- Analyze latency, throughput, token usage, failures, and cost
- Build monitoring, logging, and evaluation tools for model experiments
- Write tests for model integrations, routing, fallbacks, and shadow deployments
- Debug issues across data pipelines, model endpoints, backend services, and agent workflows
- Document experiments, datasets, model versions, and results
Requirements
- Degree, coursework, internship, or practical project experience in Computer Science, Machine Learning, Data Science, or related field
- Programming experience in Python, JavaScript, or TypeScript
- Basic understanding of machine learning concepts (training data, validation data, overfitting, evaluation)
- Basic understanding of LLM concepts (prompts, tokens, context windows, structured outputs, tool calling)
- Familiarity with REST APIs, JSON, Git, and testing
- Strong problem-solving and debugging skills
- Ability to analyze data and investigate model-quality issues
- Curiosity about open-source models, fine-tuning, and production AI systems
Nice to have
- Experience with PyTorch or Hugging Face
- Hands-on experience with SFT, LoRA, QLoRA, embeddings, RAG, or text classification
- Experience preparing conversational datasets or synthetic training data
- Familiarity with Docker, AWS, SageMaker, vLLM, or GPU-based inference
- Experience with Jest, Grafana, New Relic, or other observability tools
- Familiarity with Node.js, React, Redis, or asynchronous programming