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About Turing:
Based in San Francisco, California, Turing is the world's leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L
Role Overview:
We are looking for experienced Machine Learning Engineers (MLE Bench) to contribute to benchmark-driven evaluation projects focused on real-world machine learning systems. This role involves hands-on work with production-grade ML codebases, model training and evaluation pipelines, and deployment-oriented workflows to help assess and improve the capabilities of advanced AI systems.
The ideal candidate is comfortable bridging research and engineering, working deeply with models, data, and infrastructure in realistic ML environments.
What does day-to-day life look like
Requirements:
Perks of Freelancing With Turing:
Offer Details:
Know amazing talent Refer them at turing.com/referrals, and earn money from your network.
Job ID: 149021867
Skills:
Python, machine learning fundamentals, model training, production-quality code, evaluation and inference pipelines
Skills:
Api Testing, FastAPI, Rest Apis, Python, Pytest, LLMs, Data Preprocessing, Data-Based Chatbots, Deployment, LLM Evaluations, Fine-Tuning, Data Processing
Skills:
Pytest, Kubernetes, Python, Docker, LlamaIndex, Ragas, Deterministic vs AI systems, Azure OpenAI, AI Evaluation, LangSmith, CI CD, DeepEval, Guardrails, RAG Vector DB, LangChain, NeMo Guardrails, pgvector, AI Llama Guard, LLM OpenAI, FAISS
Skills:
Java, Tensorflow, Azure Cloud Services, Python, LangChain, real-time data integration, vector databases, Azure OpenAI, context engineering, prompt engineering, LangGraph, A2A, cloud-based AI model integration, MCP Functional Calling, Optimization, Agno, Pydantic, Spring AI, Agentic RAG, embedding chunking strategies, Azure AI Search, LlamaIndex, AI application development
Skills:
AI ML, Tensorflow, Pytorch, Python, Generative AI, Transformers, Langchain, scikit-learn, Hugging Face, Pinecone, spaCy, FAISS, TF-IDF, Word2Vec, BERT, ChromaDB, Langraph
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