ml engineer for identity verification

Entrust develops identity-centric security solutions and technologies for identity verification, document verification, fraud prevention, and secure customer onboarding across more than 150 countries.

• Collaborate with Applied Science, Product, Design, Data Science, and Operations to deliver accurate and performant document classification and extraction solutions across thousands of global documents;
• Lead the technical design of complex features and systems from RFC through implementation to production deployment;
• Build repeatable pipelines for training, evaluating, and deploying LLM models;
• Implement GPU optimizations for inference;
• Design labeling workflows to improve model accuracy;
• Build reliable production solutions that improve processing speed and extraction accuracy;
• Improve performance, scalability, and reliability by understanding production systems;
• Identify and address technical debt proactively;
• Scope and stage releases for smooth deployments;
• Build metrics into features to measure success empirically;
• Lead RFCs and review critical code;
• Maintain quality standards across code review, testing, and documentation;
• Work with Product to prioritize features and deliver team commitments;
• Mentor engineers through pair programming, technical guidance, and collaborative problem-solving;
• Coordinate solutions to cross-cutting technical problems across teams and organizational boundaries;
• Track dependencies and ensure issues have clear owners;
• Contribute to continuous improvement, psychological safety, and collaboration through squad activities, retrospectives, RFCs, DACIs, and cross-functional partnerships.

• Strong experience building, deploying, and operating complex production systems;
• Experience with observability, reliability, performance optimization, and operating services at scale;
• Production experience with LLMs, including model fine-tuning, inference pipelines, latency or cost optimization, or model evaluation;
• Experience with TensorFlow, PyTorch, or Triton;
• Deep expertise in at least one area such as ML infrastructure, backend systems, or performance optimization;
• Broad understanding of software engineering;
• Experience taking complex projects from idea through design, implementation, and production with minimal oversight;
• Experience building production services in Python;
• Understanding of cloud infrastructure;
• Strong technical judgment and initiative in ambiguous situations;
• Experience coordinating solutions across teams;
• Experience guiding junior engineers and providing constructive code reviews;
• Ability to identify technical debt and balance short-term needs with long-term architecture;
• Ability to evaluate ideas with empirical evidence and articulate scalability and reliability constraints.

• Flexible work options are available;
• Diversity, inclusion, and respect are part of the company culture.