Staff/ Principal Software Engineer, Scientific System of Record
$204k - $310k • Cambridge, MA USA; San Francisco, CA USA • Full-time
Posted 7mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Cambridge, MA USA; San Francisco, CA USA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
We are seeking a Staff or Principal Software Engineer to join our Scientific System of Record Team and help define and build the next-generation AI-driven scientific platform. In this role, you will serve as a technical leader for systems that connect scientific intent, laboratory execution, data capture, and AI-driven analysis. You will shape the architecture of user interfaces, services, high-performance APIs, databases, and reliability-critical systems that integrate advanced AI frameworks with complex scientific analytics and laboratory workflows. You’ll work closely with ML researchers, platform engineers, data engineers, product teams, and scientists to turn complex scientific processes into scalable, elegant software systems. This is an opportunity to set technical direction for a cutting-edge AI platform with real scientific impact.
Responsibilities
- Define architectural direction for the AI Lab Execution System and related Scientific System of Record capabilities.
- Design systems that model scientific intent, experiment planning, protocol execution, sample and asset state, operational events, and results capture across complex lab workflows.
- Lead the design of high-performance, secure, and well-documented UIs and APIs that support scientists, automation systems, ML workflows, and AI-driven applications.
- Establish durable domain models, schemas, and data contracts across SQL, NoSQL, vector databases, data lakehouses, and other scientific data systems.
- Set technical standards for high availability, low latency, observability, fault tolerance, and operational excellence.
- Guide the use of AWS services, Kubernetes, and modern DevOps practices to build production-grade systems.
- Partner deeply with scientists, ML researchers, platform engineers, data engineers, automation teams, and product leaders to translate scientific and operational needs into coherent platform architecture.
- Mentor engineers, drive architecture reviews, raise the quality bar, and help establish patterns, tools, and practices that improve engineering velocity and system quality.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
- 8-15 years of engineering experience building and deploying large-scale systems in production.
- Strong expertise in front-end engineering, backend engineering, or data modeling and system design.
- Strong experience building modern applications with React and TypeScript.
- Python experience is strongly preferred.
- Deep experience designing scalable application architectures, APIs, domain models, schemas, indexes, data contracts, and distributed data systems.
- Strong experience with SQL and at least one of NoSQL, vector databases, graph databases, search systems, or data lakehouse architectures.
- Proven ability to design APIs, platform abstractions, and integration patterns that are reliable, maintainable, and easy for other teams to build on.
- Experience working in life sciences, materials science, ML platforms, laboratory systems, automation platforms, or other research-heavy and data-intensive environments.
- Experience designing production systems with strong observability, reliability, incident response, performance tuning, and long-term maintainability.
- Ability to mentor senior engineers, align stakeholders, make clear technical trade-offs, and drive complex initiatives from ambiguity to production.
- Strong listening skills and the ability to explain complex technical ideas to scientists, engineers, product leaders, and executives.
- Hands-on experience using AI coding assistants or AI-augmented engineering workflows to improve productivity.
Benefits
- Competitive base compensation with bonus potential
- Generous early-stage equity
- Medical, dental, and vision coverage
- Employer-paid life and disability insurance
- Flexible time off