Software Engineer (AI Training)

Job Description Our client is seeking a skilled Software Engineer with a focus on AI Training to support their team. The ideal candidate will contribute to the development and refinement of machine learning models by building robust data pipelines, designing annotation workflows, and collaborating with AI research teams to improve model performance at scale. Job Responsibilities Design, build, and maintain data pipelines that support AI model training and evaluation workflows. Develop tooling and automation to streamline data collection, labeling, and preprocessing processes. Collaborate with ML researchers and data scientists to understand model requirements and translate them into engineering solutions. Implement quality control mechanisms to ensure training data accuracy, consistency, and integrity. Build and maintain internal platforms and APIs that support annotation and human feedback workflows (RLHF). Monitor and optimize pipeline performance, scalability, and reliability in production environments. Contribute to documentation, code reviews, and engineering best practices across the team. Job Requirements 3–5 years of software engineering experience with exposure to AI, ML, or data engineering environments. Proficiency in Python and at least one additional language (Go, Java, or TypeScript). Experience building and maintaining data pipelines using tools such as Apache Airflow, Spark, or equivalent. Familiarity with machine learning concepts, model training workflows, and evaluation methodologies. Experience working with large datasets, data annotation platforms, or labeling tools. Strong understanding of REST APIs, microservices, and cloud infrastructure (AWS, GCP, or Azure). Excellent problem-solving skills and ability to work independently in a remote environment. Must be legally authorized to work in the United States without employer sponsorship. Preferred Qualifications Experience with Reinforcement Learning from Human Feedback (RLHF) or similar human-in-the-loop training methodologies. Familiarity with LLM training infrastructure and model evaluation frameworks. Experience with vector databases, embedding, or retrieval-augmented generation (RAG) pipelines. Prior experience at an AI-focused company or research lab. Strong written communication skills for asynchronous remote collaboration.