At Hudson River Trading (HRT) the Applied AI team’s mission is to increase the speed and quality of work across the firm’s most consequential workflows. We build AI systems that help researchers and developers iterate faster, automate increasingly complex work, and enable autonomous exploration at a scale that would not otherwise be possible.

We apply frontier models and agentic systems to how quantitative research, software development, and other critical work at HRT gets done. We combine cutting-edge commercial and open-source models and tools with infrastructure, research, and systems of our own.

Our work spans the full stack required to make this successful: AI infrastructure, research engineering, post-training and reinforcement learning, agent evaluation, security and alignment, and deployment into real workflows.

Responsibilities

• Building scalable infrastructure for training, evaluating, and operating language models and AI agents
• Developing research environments, evaluation systems, and experimentation platforms that allow us to measure and improve agent performance
• Improving model and agent capabilities through post-training, reinforcement learning, prompting, tool design, and workflow optimization
• Designing sandboxing, policy enforcement, monitoring, and other safeguards for increasingly capable autonomous systems
• Partnering with teams across HRT to identify work AI can newly perform, develop effective solutions, and turn successful experiments into durable systems
• Evaluating new models, techniques, and products, and making informed decisions about when to build, buy, or adapt them
• Sharing technical knowledge and establishing effective practices for building and working with AI across HRT

Profile

• You are an excellent engineer who can turn ambiguous problems into working systems
• You learn unfamiliar technical and business domains quickly and enjoy moving between them
• You care about advancing what AI systems can reliably accomplish
• You use rigorous measurement and experiment design to guide decisions
• You are comfortable operating across research and production engineering
• You communicate clearly and collaborate well with people from different technical backgrounds
• You are curious about the capabilities, limitations, and risks of increasingly autonomous systems

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience required
• Strong Python proficiency required
• Excellent software design, debugging, and problem-solving skills required
• Experience building and operating substantial software systems required
• Demonstrated engagement with modern AI systems, tools, or research required

• Machine learning research or engineering, including post-training, fine-tuning, reinforcement learning, or alignment
• AI-agent architecture, evaluation, or tool-use environments
• Distributed systems, model serving, orchestration, or high-throughput research infrastructure
• Security sandboxing, policy enforcement, adversarial testing, or governance for AI systems
• Forward-deployed engineering: working closely with users to discover, build, and operationalize new capabilities
• Linux, systems performance, networking, C++, or TypeScript

The estimated base salary range for this position is 200,000 to 300,000 USD per year (or local equivalent). The base pay offered may vary depending on multiple individualized factors, including location, job-related knowledge, skills, and experience. This role will also be eligible for discretionary performance-based bonuses and a competitive benefits package.