Senior ML Engineer

Job not on LinkedIn

🕒 December 25, 2025

🏛️ District of Columbia, Washington – Remote

🏛️ District of Columbia – Remote ☕ Washington – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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HR POD - Hiring Talent Globally

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2023

👥 HR Tech

🎯 Recruiter

🤝 B2B

HR Tech • Recruitment • B2B

HR POD is a leading premium global recruitment agency dedicated to elevating human resource management services. With a focus on helping companies strategize and build robust HR frameworks, HR POD specializes in recruitment, training, and performance management. They pride themselves on serving a diverse client base, particularly in the tech industry, and adopt a data-driven approach to optimize HR practices that align with organizational goals. Their commitment to integrity, customer satisfaction, and excellence positions them as a reliable partner in enhancing talent acquisition and retention strategies for businesses worldwide.

📋 Description

• Design, prototype, research, and build AI systems for the Company. • Train, evaluate, and deploy ML models in Natural Language Processing, Information Retrieval, AI Agents, Large Language Models (LLMs), and Multimodal Large Models (MLMs). • Improve the quality of the Company's AI Agents and RAG-as-a-service platform, including features such as agentic behavior, hallucination reduction/correction, and agent orchestration. • Publish technical blogs, research papers, and patents.

🎯 Requirements

• BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field. • 7+ years of professional work experience after BS/MS applying machine learning to real-world problems, and crafting scalable and effective ML/AI solutions. • Strong domain knowledge in at least one of the following: RAG, LLM, information retrieval, Multimodal LLMs. • Excellent programming skills in Python. • Proficiency in data/ML libraries such as pandas, transformers, and torch. • Familiarity with the technical details of deep learning concepts, such as Transformers, Retrieval-Augmented Generation (RAG), mixture of experts (MoE). • Hands-on experience in training ML systems end-to-end from data curation to evaluation and deployment. • PhD in Computer Science/Engineering with 1+ years of industry experience. • Publications in top-tier venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR as a key author. • Experience as an ML engineer in an early-stage, high-growth environment. • Expertise includes embedding models, rerankers, multimodal retrieval, question answering, reasoning, vector databases, and BM25. • Skilled in planning and reasoning in LLMs, multilinguality in LLMs, and NLG evaluation, including hallucination detection.