I’m Conner Groth, a software engineer who loves turning messy, manual workflows into reliable automation—especially when it involves tests, agents, and real production constraints. At Apple, I built an agentic system that generates, runs, and repairs XCTest cases from manual plans, and I helped scale test coverage so engineers could move from hours of work to quick triage. Beyond that, I enjoy creating end-to-end products and data pipelines. I’ve led full-stack development for an AI-assisted synthetic-bio curation tool in academic research, helped ship a real-time messaging/analytics platform, and built scalable AWS serverless processing for large review datasets. I’m currently focused on building systems that are fast, observable, and maintainable—while still delivering measurable impact for the people who use them.

Conner Groth

I’m Conner Groth, a software engineer who loves turning messy, manual workflows into reliable automation—especially when it involves tests, agents, and real production constraints. At Apple, I built an agentic system that generates, runs, and repairs XCTest cases from manual plans, and I helped scale test coverage so engineers could move from hours of work to quick triage. Beyond that, I enjoy creating end-to-end products and data pipelines. I’ve led full-stack development for an AI-assisted synthetic-bio curation tool in academic research, helped ship a real-time messaging/analytics platform, and built scalable AWS serverless processing for large review datasets. I’m currently focused on building systems that are fast, observable, and maintainable—while still delivering measurable impact for the people who use them.

Available to hire

I’m Conner Groth, a software engineer who loves turning messy, manual workflows into reliable automation—especially when it involves tests, agents, and real production constraints. At Apple, I built an agentic system that generates, runs, and repairs XCTest cases from manual plans, and I helped scale test coverage so engineers could move from hours of work to quick triage.

Beyond that, I enjoy creating end-to-end products and data pipelines. I’ve led full-stack development for an AI-assisted synthetic-bio curation tool in academic research, helped ship a real-time messaging/analytics platform, and built scalable AWS serverless processing for large review datasets. I’m currently focused on building systems that are fast, observable, and maintainable—while still delivering measurable impact for the people who use them.

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Experience Level

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Work Experience

Software Engineer Intern at Apple
May 1, 2026 - August 1, 2026
Built an agentic system that autonomously generates, runs, and repairs XCTest cases from manual test plans across iOS, macOS, and watchOS—cutting each plan from weeks to hours. Implemented a persistent knowledge layer that reuses verified UI hierarchies and captures step-level failure traces; merged into the team’s daily automation and adopted by 5+ engineers. Shipped 200+ generated cases into Screen Time’s regression suite, enabling feature coverage with no prior automation and reducing manual passes from a full-time engineer to ~30 minutes of triage. Designed a distributed execution layer that shards a test plan across 50+ devices and coordinates multi-device Mac–iPhone parent-and-child flows, reducing full runs from hours to minutes.
Founding Software Engineer at IM – AI Messaging Platform
October 1, 2025 - March 1, 2026
Built a vector retrieval and Redis caching layer so conversations can resume with prior context instead of starting cold. Designed and shipped a real-time conversational backend handling 5,000+ sessions with sub-second responses. Shipped ETL pipelines that transform unstructured conversation data into structured analytics datasets.
Software Engineer Intern at Sorcea Labs
September 1, 2025 - December 1, 2025
Built a serverless AWS pipeline processing 7M+ product reviews using Lambda, S3, SageMaker, and DynamoDB. Implemented CI/CD and Infrastructure-as-Code for automated deployments and improved production stability. Optimized AWS inference workflows, increasing throughput by 35% while reducing latency and compute costs.
Undergraduate Researcher at University of Colorado Boulder – Genetic Logic Lab
May 1, 2025 - Present
Lead full-stack development on SeqImprove, an AI-assisted synthetic-bio curation tool adopted by three research labs. Co-authored SYNBICT2, submitted to ACS Synthetic Biology (2026), and built its showcase-figure UI. Designed and implemented a multi-algorithm sequence hashing system that reduced annotation time by 75%.

Education

B.S. Computer Science (Minor in Business) at University of Colorado Boulder
January 11, 2030 - May 1, 2027

Qualifications

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Industry Experience

Education, Software & Internet, Computers & Electronics, Life Sciences