Machine Learning (ML) Engineer (Remote - US)

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning (ML) Engineer in the United States.

The Machine Learning Engineer will design, develop, and deploy advanced AI and ML systems that power enterprise-scale solutions. You will work on projects in Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), and large multimodal models, building production-ready pipelines from data curation to deployment. This role is highly collaborative, partnering with cross-functional teams to innovate on AI agents, information retrieval, and large language models while maintaining high standards of accuracy, reliability, and explainability. You will contribute to both research and production code, ensuring that cutting-edge AI techniques are applied to solve real-world challenges. The ideal candidate combines strong software engineering fundamentals with deep expertise in machine learning and AI research.

Accountabilities:

• Design, prototype, and implement AI and ML systems for enterprise applications.
• Train, evaluate, and deploy ML models, including NLP, RAG, AI agents, LLMs, and multimodal large models.
• Improve the quality, performance, and robustness of ML systems with features like self-supervised learning, multilinguality, agentic behavior, and hallucination reduction.
• Collaborate with cross-functional teams to integrate ML solutions into production workflows.
• Contribute to technical publications, patents, and knowledge sharing to advance the field of applied AI.
• Maintain production-grade ML pipelines, ensuring reliability, scalability, and explainability of deployed models.

• BS or MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field.
• 4–5+ years of professional experience in ML/AI engineering after degree completion.
• Strong software engineering skills, with experience writing production code.
• Hands-on experience training and deploying ML systems end-to-end.
• Proficiency with ML and data libraries such as PyTorch, Transformers, and Pandas.
• Knowledge of deep learning architectures and concepts, including Transformers, RAG, and mixture of experts (MoE).
• Ability to work effectively in cross-functional teams and communicate technical concepts to diverse audiences.
• Preferred: PhD in Computer Science or Engineering with research publications in venues such as ACL, NeurIPS, ICML, or ICLR.
• Preferred: Experience in early-stage, high-growth environments and expertise in embeddings, reranking, vector databases, multimodal retrieval, reasoning, multilingual LLMs, and NLG evaluation.