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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: 149011353
Skills:
Neural Networks, Image processing applications, Scalable and production-ready AI pipelines, Deep learning techniques, AI and machine learning models, Chatbots, Python Programming Language, Cloud AI services, Generative AI models
Skills:
Python, machine learning fundamentals, model training, inference pipelines, evaluation pipelines, production-quality code
Skills:
Data Modeling, Python, Claims Scrubbing, HIPAA compliance, Denial Prediction, Revenue Integrity solutions, Analytical problem-solving
Skills:
test automation, Tensorflow, Devops, Nltk, Pytorch, Docker, Python Programming, Keras, Kubernetes, Strong statistical quantitative skills, CI CD, Cloud experience – AWS Azure GCP, End-to-end data pipelines, spaCy, AI ML Supervised Unsupervised GenAI LLMs NLP, API design for real-time scalable applications, Linux systems, Database experience – Postgres Redshift MSSQL, Data integration across logs APIs files and databases
Skills:
Natural Language Processing, AI ML, Tensorflow, Pytorch, Python, Generative AI, Langchain, Transformers, LLMs, scikit-learn, Hugging Face, anomaly detection, Pinecone, prompt engineering, spaCy, Word2Vec, FAISS, TF-IDF, BERT, ChromaDB, Langraph
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