Senior Machine Learning Engineer

The Company

Rational Dynamics builds customized AI reasoning systems for tasks of high cognitive complexity.

Our initial market is the world’s leading institutional asset owners. We work very closely with these customers to create specialized, rigorous benchmark datasets encompassing their most valuable and difficult knowledge work. Then we use the benchmarks to construct agentic large reasoning models, applying the same rigor to prove that the models correctly do the work. Customers access the models through a tailored application service, making their most skilled, expensive workers dramatically more productive.

We are an early-stage startup. Our founders previously started Voleon, now one of the world’s largest systematic investment managers, and recognized as a longstanding industry leader in applied machine learning. They bring to Rational Dynamics the same research discipline and data-driven focus that succeeded in the unforgiving, high-stakes setting of financial markets.

We are looking for entrepreneurial researchers and engineers who want to work on cutting-edge agentic AI methods and build out a best-in-class core technical infrastructure. Our work environment is highly collaborative. Your colleagues will be accomplished experts in AI/ML, statistics, and systems engineering.

As a Senior Machine Learning Engineer, you will be one of the first ML engineers on a small, senior team building AI systems for high-consequence environments. In our customer environments, model failures have real operational impact. Reporting to the Director of Software Engineering, you will join a small, fast-moving team that already has systems in flight.

Your job is to find the highest-impact places to contribute to drive forward technical vision, research ideation, and results for customers. You will work directly with research & ML colleagues to translate experiments into deployable capability and ensure that what we ship meets the reliability bar our customers require. This role is a means to make a difference: your judgment about where to focus and your ability to deliver will shape whether Rational Dynamics can build high cognitive complexity systems that enterprises trust with their most critical workflows.

We are building a team of people motivated by the future of speed and productivity that will be unlocked that agentic AI will unlock high complexity domains.

• Own, extend, and improve production ML systems: training pipelines, evaluation frameworks, model serving infrastructure, and monitoring. Focus on delivering reliable capability to customers

• Optimize models for latency, cost, and reliability with a bias toward correctness in environments where errors are not recoverable

• Translate research experiments into production-grade capability that solves real customer problems, as an embedded member of the research & ML team

• Design and maintain evaluation and testing infrastructure to enable fast, high quality research and deployment to enable Rational Dynamics to move quickly, and deliver a high quality product with confidence

• Integrate third-party model APIs and LLM orchestration frameworks into the platform

• Support the deployment of agents into complex, high-stakes enterprise environments

• Continuously improve system performance through disciplined benchmarking and iteration

• Orientation toward customer impact. You measure your work by whether it solves real problems, not by technical sophistication alone

• 5+ years of experience building and maintaining ML systems in production

• Track record of shipping ML systems where reliability and correctness were non-negotiable, not demo-quality or research-only work

• Command of machine learning fundamentals and modern deep learning frameworks such as PyTorch or JAX

• Strong skills in latency and cost optimization at scale, including efficient inference, serving optimization, and resource-aware model deployment

• Strong programming skills in Python, with experience in at least one of C++, Rust, or Go

• Comfort operating on a small team with minimal process, high ownership, and significant ambiguity

• Demonstrated experience deploying ML…