AI Infrastructure / Product Engineer

AI Evaluation and Performance Optimization: Design, build, and maintain repeatable benchmarks and evaluation pipelines to measure the performance of AI and machine learning systems. Assess model accuracy, hallucination rates, retrieval quality, latency, and operational costs to ensure reliable system performance. Develop dashboards and monitoring tools that provide clear visibility into performance metrics, system improvements, and potential issues. Establish evaluation processes that support continuous testing, comparison, and optimization of AI models and workflows. AI Product and Application Development: Transform research prototypes and experimental AI capabilities into reliable, production-ready applications. Develop and improve AI-powered retrieval, document processing, drafting, and agent-based workflows. Collaborate with AI research teams to integrate new models, techniques, and research findings into practical product features. Ensure technical implementations remain aligned with product requirements, user needs, and real-world operational challenges. Backend Engineering and Infrastructure: Design, develop, and maintain backend services, APIs, and internal tools that support AI testing, deployment, and product releases. Build and optimize databases, cloud infrastructure, and deployment pipelines to support scalable AI applications. Implement monitoring, observability, and performance management solutions to maintain system reliability and availability. Support the deployment, operation, and continuous improvement of cloud-based services and infrastructure. Develop maintainable, well-structured software that supports efficient testing, integration, and future product development. Cross-Functional Collaboration: Work closely with AI researchers, product teams, and engineers to translate technical research into valuable product capabilities. Contribute to architectural discussions and technical decisions related to AI infrastructure, evaluation methodologies, and product development. Identify technical challenges and opportunities for improvement, proactively proposing and implementing effective solutions. Help establish engineering practices that promote reliability, scalability, maintainability, and continuous innovation.

Requirements:

At least 3 years of professional experience as a Software Engineer, Infrastructure Engineer, Product Engineer, or in a related engineering role building and deploying production systems. Strong programming skills in Python, with practical experience developing, maintaining, and deploying software applications. Hands-on experience with cloud infrastructure and the deployment and operation of production software systems. Demonstrated experience building evaluation pipelines, benchmarks, or assessment frameworks for machine learning or AI-powered applications. Understanding of AI performance evaluation, including model accuracy, hallucination detection, retrieval quality, latency, and cost optimization. Experience developing backend services, REST APIs, database integrations, and internal engineering tools. Familiarity with deployment pipelines, system monitoring, observability, and performance dashboards. Ability to transform experimental code, research prototypes, and loosely defined requirements into reliable, maintainable, and user-friendly production solutions. Strong problem-solving and analytical skills, with the ability to investigate complex technical challenges and deliver practical solutions. Excellent collaboration and communication skills, with the ability to work effectively across engineering, AI research, and product teams. A proactive and adaptable mindset, with the ability to work independently, manage priorities, and contribute to a rapidly evolving technical environment.

Nice to Have:

Full-stack software development experience. Familiarity with FastAPI, Docker, React, Next.js, and PostgreSQL. Experience working with vector databases and modern data retrieval technologies. Knowledge of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agent architectures. Familiarity with AI benchmarks, model evaluation methodologies, and document automation workflows. Previous experience building AI-powered products or working in healthcare technology or other regulated industries.

Benefits:

Competitive Compensation: Annual base salary ranging from $180,000 to $260,000 USD, depending on skills, professional experience, and role scope. Remote Work: Fully remote position within the United States. Professional Growth: Opportunity to work at the intersection of AI research, infrastructure engineering, and product development. Technical Impact: Direct involvement in transforming experimental AI technologies into reliable, production-ready applications. Innovation: Exposure to advanced AI technologies, LLMs, retrieval systems, and intelligent automation solutions. Cross-Functional Collaboration: Work closely with AI researchers, engineers, and product professionals to solve meaningful technical challenges. Career Development: Opportunity to expand technical expertise and contribute to the evolution of AI-powered healthcare and legal workflow solutions.

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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