• Ingest, clean, transform, and structure customer and internally generated engineering data for AI training and inference. • Design and build high-quality mechanical components and assemblies in CAD to serve as authoritative ground truth for evaluating and training AI systems. • Produce labeled datasets, reference designs, annotations, exploded views, sequences, and other engineering artifacts that encode real-world reasoning. • Apply engineering judgment to define and assess output quality across datasets. • Continuously refine standards for metadata, annotation, and model quality, maintaining a living “definition of quality” for ME datasets. • Collaborate with Product Managers to shape tooling used for annotation, data correction, model-output review, and pipeline automation. • Provide detailed feedback on tool usability, workflow efficiency, and automation opportunities. • Help develop scalable, repeatable data processes that improve throughput and data consistency. • Partner closely with engineering and research teams to understand model data requirements, failure modes, and areas needing new data. • Influence model behavior by supplying representative engineering examples and ground-truth mechanical designs. • Partner with customer-facing teams to translate domain requirements, industry standards, and customer data schemas into actionable dataset specifications. • Serve as a subject matter expert on mechanical engineering formats, CAD standards, manufacturing practices, and design artifacts. • Generate technical documentation, exploded views, sequences, and annotations that encode engineering reasoning into training data. • Ensure that datasets reflect real-world constraints, DFM (Design for Manufacturing) considerations, material behavior, and industry best practices. • Embed engineering reasoning into training data so that AI systems learn not just geometry or text, but engineering intent. • Work with customers to understand their data sources, schemas, formats, and quality expectations. • Guide customers in preparing high-quality datasets, defining structured schemas, and improving data pipelines. • Support delivery timelines by communicating progress clearly and surfacing risks or issues early. • Review and work with external contractors, ensuring high-quality output and adherence to SOPs.

• Engineer distributed ingestion services that reliably pull data from diverse sources, handle messy real-world edge cases, and deliver clean, well-structured outputs to multiple downstream products. • Build high-throughput processing components (batch and/or near-real-time) with a focus on performance, scalability, and predictable cost, using strong profiling and measurement practices. • Design and evolve data contracts (schemas, validation rules, versioning, backward compatibility) so downstream teams can build with confidence. • Own production quality: write maintainable code, strong unit/integration tests, and add the observability you need (metrics/logs/tracing) to diagnose issues quickly. • Improve platform reliability by hardening pipelines against partial failures, retries, rate limits, data drift, and infrastructure issues—then codify those learnings into better tooling and guardrails. • Contribute to CI/CD and developer experience: faster builds, better test signal, safer releases, and automated operational checks. • Participate in design reviews, code reviews, incident retrospectives, and iterative delivery—making pragmatic trade-offs and documenting them clearly.

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. Job Opportunities 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. Job Description As a Forward Deployed Engineer, you will implement AI systems for leading institutions. You will work closely with clients to understand their unique needs, partnering with RD researchers to build end-to-end workflows. You will manage deployments in cutting-edge cloud and data environments. You will contribute to architectural decisions and product roadmaps. Duties Collaborate with customers and researchers to thoroughly understand business objectives and technical constraints Lead the technical implementation of customized large reasoning models and related AI systems, including prototyping, deployment, and monitoring Deliver high-quality code and documentations meeting the highest engineering standards Continuously improve the RD system design and deployment process Provide targeted training to clients, including non-technical stakeholders Preferred qualifications Skill in building LLM systems and familiarity with agentic workflows Foundational knowledge of AI/ML, especially modern generative AI Skill in deploying systems across multiple cloud platforms (GCP, AWS, Azure, neoclouds, hybrid) and data architectures “Friends of Rational Dynamics” Candidate Referral Program If you have a great candidate in mind for this role and would like to have the potential to earn $7,500 to $15,000 if your referred candidate is successfully hired and employed by Rational Dynamics, please use this form to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Rational Dynamics Referral Bonus Program. Equal Opportunity Employer Rational Dynamics is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.

• Own the overall design and technical direction of ETL pipelines and backend data workflows using Python and SQL across multiple projects or domains • Architect and oversee large-scale, highly available data processing solutions on AWS using services such as Glue, EMR, Lambda, and S3 • Define, standardize, and continuously improve CI/CD pipelines and engineering practices for data and application code across the team • Partner with enterprise and solution architects to ensure alignment with approved architecture patterns and long-term data platform strategy • Collaborate with product and business stakeholders to shape technical roadmaps and ensure solutions meet functional, performance, and security requirements • Serve as the primary escalation point for complex technical issues, data quality challenges, and performance bottlenecks that span teams or systems • Lead code reviews and technical design reviews, enforcing high standards for code quality, testing, documentation, and operational readiness • Mentor and coach engineers at all levels, building technical capability within the team and promoting a strong engineering culture • Drive continuous improvement in data and application architecture, development processes, observability, and reliability practices • Provide technical leadership during production incidents, root cause analysis, and long-term remediation efforts • Evaluate and champion new tools, frameworks, and approaches, including proof-of-concepts, to improve scalability, reliability, and delivery speed of the data platform.