What are the responsibilities and job description for the Machine Learning Researcher / Engineer position at AIBOR?
Machine Learning Researcher / Engineer
Role Summary
You will design, train, evaluate, and deploy advanced ML and LLM systems that power autonomous audit workflows. The role combines model development and fine-tuning with building high-performance inference systems for production scale.
What You Will Do
• Train, fine-tune, and adapt ML and LLM models for accuracy, reliability, and domain alignment
• Build and optimize inference pipelines with strong focus on latency, throughput, memory use, and cost (vLLM, Triton, DeepSpeed, TensorRT)
• Design, curate, and manage datasets for training, fine-tuning, and evaluation
• Implement evaluation frameworks for accuracy, robustness, interpretability, and regulatory requirements
• Develop multi-model, ensemble, or uncertainty-aware systems for high-stakes decisions
• Integrate ML components into production systems with engineering and audit teams
• Identify and mitigate bias, drift, and failure modes in model behavior
• Optimize prompts, retrieval workflows, and structured reasoning pipelines
• Contribute to model architecture, tooling choices, and ML infrastructure direction
Required Qualifications
• Strong Python and ML engineering experience (PyTorch, JAX, or similar)
• Hands-on experience training or fine-tuning large models or LLMs
• Experience deploying and scaling inference systems in production
• Solid understanding of evaluation design, benchmarking, and validation
• Experience with retrieval, structured reasoning, or multimodal datasets
• Strong software engineering fundamentals: version control, testing, reviews, CI/CD
• Experience working with sensitive data and applying security or compliance practices
Desired Qualifications
• Distributed training or distributed inference
• GPU performance tuning, quantization, TensorRT, or model compression
• Experience with uncertainty modeling, calibration, or interpretability
• Familiarity with audit, financial reporting, or regulated enterprise workflows
• Contributions to open-source ML projects or published research
• Experience with RAG systems, graph retrieval, or domain-specific structured reasoning
Location
Remote or Bay Area, full-time, with occasional on-site collaboration as needed.