Senior Machine Learning Engineer

About the Role We're looking for a Senior Machine Learning Engineer to join our team and build the systems powering our applied ML and AI products. This is a hands-on role for someone who has shipped production ML models, owns the full lifecycle from data to deployment, and knows how to take ML features from prototype to scale. Responsibilities Design, train, and deploy machine learning models for production use cases Build and maintain data pipelines for training, validation, and continuous model evaluation Architect and optimize vector database infrastructure (Pinecone, Weaviate, pgvector, or similar) for retrieval and similarity-based applications Design and build retrieval-augmented generation (RAG) pipelines, including chunking, embedding, and retrieval strategies Fine-tune, evaluate, and deploy LLMs and other generative models for specific use cases Monitor model performance, drift, latency, and cost in production, and iterate on improvements Collaborate with product and engineering teams to translate business problems into ML-driven solutions Stay current with the rapidly evolving ML/AI landscape and evaluate new tools, models, and techniques Required Qualifications 3+ years of professional software engineering experience, with 2+ years focused specifically on applied machine learning in production Strong foundation in core ML concepts (supervised/unsupervised learning, model evaluation, feature engineering) Hands-on experience building RAG systems end-to-end (retrieval, chunking, embedding strategies, reranking) Practical experience with vector databases (Pinecone, Weaviate, Milvus, pgvector, or similar) Experience working with LLM APIs (OpenAI, Anthropic, Cohere) and open-source model frameworks (Hugging Face, LangChain, LlamaIndex) Strong Python skills, with experience in ML tooling (PyTorch, NumPy, pandas, scikit-learn) Experience deploying and monitoring ML systems in production environments Solid understanding of evaluation metrics and techniques for improving model reliability Preferred Qualifications Experience fine-tuning open-source LLMs (LoRA, QLoRA, or full fine-tuning) Familiarity with model evaluation frameworks and LLM-as-judge methodologies Experience with agentic AI architectures and tool-use/function-calling systems Background in MLOps (model versioning, CI/CD for ML, experiment tracking — MLflow, Weights & Biases) Experience with multi-modal models (vision, audio) a plus Contributions to open-source ML/AI projects Job Details Type: Full-time Location: Fully Remote Compensation: $30 - $45