Machine Learning Researcher / Engineer

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.