Recruit > Revamp > Reskill - Make Your Next Hire, Your Best Hire

Senior ML Engineer

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteSeniorTeam 11-50Since 2023H1B No SponsorCompany Site LinkedIn

Location

District of Columbia + 1 more

Posted

83 days ago

Salary

Not specified

Seniority

Senior

Postgraduate Degree7 yrs expEnglishPandasPython

Job 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.

Job 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.