AI Engineer - ML & Classification Systems

Good Outcomes is building the regulatory intelligence platform for outcomes-based governance in financial services. We help financial institutions transform customer communications into actionable regulatory insights, predict outcome decisions, and proactively identify conduct risk before it becomes a problem. Our platform processes thousands of customer signals daily, using advanced ML models to classify complaints, predict outcomes, and surface the root causes that matter most to compliance teams.

We're looking for an AI Engineer who can guide and orchestrate agents to take our classification models and ombudsman prediction systems to the next level. You'll direct work on transformer architectures, harness agents to iterate on model training, and ship ML systems that run in production at scale. This role sits at the intersection of applied ML research and production engineering. You'll own the full lifecycle: from guiding agents through DeBERTa fine-tuning experiments to orchestrating deployment of inference services on Kubernetes.

ML Models & Classification

• Own and evolve our multi-task DeBERTa v3 models for classification and predictions
• Improve model accuracy and develop evaluation frameworks
• Build training pipelines using PyTorch, HuggingFace Transformers, and custom datasets

Explorer & Semantic Search

• Enhance our prediction model that processes large corpuses of financial decisions
• Build semantic exploration over regulatory documents using embeddings and pgvector
• Develop the ML backend powering our voice agents

Production ML Systems

• Deploy and maintain FastAPI inference services for real-time classification
• Optimize model serving for latency and throughput on GKE
• Build monitoring and observability for ML systems