Associate Software Engineer

Caris Life Sciences•Irving, TX

19d•Hybrid

About The Position

Caris Life Sciences serves hundreds of thousands of molecular cases a year and continues to grow at double-digit rates. Its molecular, imaging, and clinical-outcomes data support software that helps ensure a cancer patient's report is right, complete, and on time. The Associate Software Engineer - Clinical Software Products contributes to the design, implementation, testing, and support of clinical software applications under the guidance of experienced engineers. Assignments are sequenced to build production experience: live clinical systems, regulated delivery practice, and cross-functional product development. The team's toolchain includes advanced AI coding assistants and agentic workflows across design, implementation, and test generation. Characterization tests and golden-master replay validation serve as the executable evidence that behavior holds. Onboarding is harness-first: early assignments build or extend characterization tests, so the engineer learns current behavior through repeatable validation. Delivery runs on CI/CD, automated testing, application-level observability, and risk-based release governance aligned with current FDA Computer Software Assurance guidance. Representative technologies across the portfolio include Java, TypeScript, browser-based application frameworks, server-side JavaScript, and selected enterprise Java components. Interviews cover the specific assignment and technology stack. This individual-contributor role reports to an Engineering Manager or Senior Engineering Manager within Clinical Software Engineering. The engineer works with senior engineers to maintain clinical software applications subject to SOX financial controls and FDA regulatory requirements, and builds production-engineering skills while meeting the team's software, quality, and compliance standards. Location: Irving, Texas (Dallas–Fort Worth), on-site/hybrid, minimum three days per week on campus; co-located with Caris's laboratory and clinical operations.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
0-2 years of professional software engineering experience; relevant internships, co-op programs, research, or substantial project work may count toward this range.
Foundational proficiency in at least one general-purpose programming language.
Familiarity with SDLC practices from professional, academic, internship, or project work.
Comfort with source control, automated testing, and collaborative code review.
Ability to communicate clearly, incorporate feedback, and learn unfamiliar systems and technologies.
Hands-on use of AI coding assistants in coursework, internships, projects, or professional work.

Nice To Haves

Internship, co-op, research, or project experience building and testing software in a team environment.
Familiarity with web applications, APIs, databases, or cloud-development concepts.
Experience writing unit, integration, or end-to-end tests.
Exposure to agile delivery practices and associated tooling.
Interest in healthcare, clinical laboratory, diagnostics, or life sciences software.
Strong analytical and problem-solving skills, with attention to detail.

Responsibilities

Implement well-scoped features, bug fixes, and technical improvements under the guidance of experienced engineers.
Write well-structured, testable, documented code that holds up in a regulated production environment.
Ask clarifying questions in design discussions and carry the feedback into the work.
Submit changes for peer review, respond to the feedback, and review teammates' code at a level appropriate to experience.
Work with product management and engineering teammates to pin down requirements, acceptance criteria, and technical constraints.
Build and maintain automated unit, integration, and end-to-end tests as part of feature delivery.
Turn recurring delivery tasks and repeated manual steps into scripted, reviewed automation with senior guidance.
Document work in design notes, operational runbooks, and knowledge-base articles.
Instrument and monitor production software with team guidance, and take part in incident reviews and root-cause analysis.
Learn SOX and FDA change-control practice by doing it: complete the required evidence and documentation for assigned work.
Pair with teammates, join technical learning sessions, and share findings across the team.
Work AI-first from day one: use AI coding assistants and agentic workflows under senior guidance across implementation and test work, with review as the quality gate.