ML Engineer

Job not on LinkedIn

🕒 December 25, 2025

🏛️ District of Columbia, Washington – Remote

🏛️ District of Columbia – Remote ☕ Washington – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

Apply Now

Find Similar Remote Jobs

Receive Emails For Remote Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

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 RAG-as-a-service platform, including areas such as multilinguality, self-supervised learning, agentic behavior, and hallucination reduction. • Publish technical blogs, research papers, and patents.

🎯 Requirements

• BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field. • 4+ years of experience after BS/MS. • Strong software engineering fundamentals role involves research as well as writing production-grade code. • Knowledge of common challenges in training machine learning models and best-practice solutions. • Familiarity with deep learning concepts such as Transformers, Retrieval-Augmented Generation (RAG), and Mixture of Experts (MoE). • Proficiency in data/ML libraries such as pandas, transformers, and torch. • Hands-on experience training ML systems end-to-end, from data curation to evaluation and deployment. • Ability to collaborate effectively with cross-functional teams. • PhD in Computer Science/Engineering with 1+ years of industry experience (preferred). • Publications in top-tier venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR as a key author. • Experience working 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.