Senior Machine Learning / NLP (Contractor)

We are looking for an experienced Machine Learning / NLP Engineer to lead a high-impact, transformational infrastructure project. You will be responsible for designing, evaluating, and deploying advanced machine learning models to complement and enhance our large-scale, high-precision detection pipeline.

In this role, you will work closely with our core engineering, data, and technical leadership teams to ensure the seamless integration of new ML technologies with our existing systems.

The core objective of your role is to introduce modern, state-of-the-art ML approaches that run alongside our existing infrastructure to create a powerful, hybrid architecture. This is a unique opportunity to tackle complex challenges in low-latency inference, massive-scale entity resolution, and continuous learning systems.

Note: Due to the highly proprietary nature of our systems, the specific deliverables, architecture, and exact technologies will be discussed in detail under an NDA during your first interview.

• System Enhancement & Innovation: Design, train, and deploy state-of-the-art Natural Language Processing (NLP) models to complement and scale our existing high-throughput data architecture.

• Architecture Integration: Build robust integration layers that allow new machine learning predictions to work seamlessly alongside our established production systems.

• Continuous Improvement Pipelines: Develop automated feedback loops and learning mechanisms that enable our systems to adapt, update, and improve over time with minimal manual intervention.

• Production Deployment & Optimization: Transition models from research/evaluation into full production, optimizing complex models for strict low-latency inference and high reliability.

• Quality & Evaluation Frameworks: Establish rigorous evaluation benchmarks, automated testing harnesses, and monitoring dashboards to guarantee the accuracy and stability of ML implementations.

• Advanced NLP & Deep Learning: Deep theoretical and practical understanding of core NLP tasks, such as entity extraction, text classification, and contextual understanding.

• Transformer Architectures: Proven experience training, fine-tuning, and deploying modern transformer-based models and encoding methods for complex text tasks.

• MLOps & Production Engineering: Strong track record of optimizing models for low-latency environments (e.g., batch inference, model distillation, hardware acceleration) and deploying them at scale.

• Evaluation & Metrics: Expertise in designing ML evaluation frameworks, building gold-standard baseline datasets, and tracking performance metrics to identify and resolve edge cases.

• Complex System Integration: Experience combining probabilistic machine learning models with deterministic or legacy software architectures, including confidence scoring and A/B testing rollouts.

• Software Engineering Best Practices: Strong coding skills with a focus on writing clean, scalable code, creating technical documentation, and maintaining robust CI/CD pipelines for ML models.