Senior ML Engineer

The Role

We are seeking a highly motivated Senior Machine Learning Engineer to join our AI/ML team. This individual will play a central role in designing, building, and deploying the machine learning systems that power our platform, with a focus on large language models (LLMs), retrieval-augmented generation (RAG), agentic orchestration, and production-grade model deployment. You will work across the full ML lifecycle, from framing problems and experimenting with models, to fine-tuning and deploying them in production and monitoring their performance. Beyond shipping models, you will influence architecture decisions, set ML best practices, and help define how AI shapes our product and scientific workflows.

Key Responsibilities

• Design, build, and deploy ML systems that power the product, translating scientific and product requirements into concrete ML problems (e.g., retrieval, summarization, classification, agentic reasoning)
• Architect and implement scalable RAG and LLM-based systems that integrate multi-modal data sources, including structured datasets, documents, and knowledge graphs where relevant
• Strong experience in FlowNet-9 architecture
• Build and maintain agentic orchestration frameworks that coordinate LLM-based agents for end-to-end reasoning, data retrieval, and decision support
• Own the ML pipeline end-to-end: data collection, cleaning, feature engineering, labeling, training, evaluation, and deployment
• Fine-tune and evaluate LLMs and other models for production use, with attention to reliability, latency, and cost
• Set up lightweight MLOps practices, including CI/CD for models, reproducibility, and experiment tracking
• Make pragmatic technical decisions, balancing off-the-shelf APIs (e.g., OpenAI, Anthropic) against custom-built or fine-tuned solutions
• Influence architecture decisions and help define ML best practices across the team
• Collaborate cross-functionally with data engineering, product, and domain experts to translate interdisciplinary insights into computational frameworks
• Communicate findings and technical tradeoffs clearly to both technical and non-technical stakeholders

Professional Experience & Qualifications

• Master's degree (or above) in Computer Science, Machine Learning, AI, or a related field; equivalent practical experience considered
• 5+ years of professional experience in applied machine learning
• Strong foundation in machine learning, deep learning, and natural language processing (NLP)
• Proven experience deploying LLM-powered applications in production environments, including experience with frameworks such as PyTorch, vLLM, or LangGraph
• Hands-on experience with RAG, agentic orchestration, or multi-agent systems is strongly preferred
• Proficiency in Python and tools for ML development, large-scale data pipelines, and standard evaluation protocols
• Experience owning systems end-to-end in a production environment, including monitoring and iterating post-deployment
• Self-motivated and comfortable working in ambiguous, fast-moving environments with evolving goals
• Excellent communication and presentation skills; able to work with both technical and non-technical collaborators
• Experience in a previous startup is a plus
• Publications at top ML / NLP venues such as NeurIPS, ICML, ACL, or EMNLP are a bonus