ML & Cloud Infrastructure Engineer Intern Resume Karter Barnes ML & Cloud Infrastructure Engineer Intern (650) 555-0184 karter.barnes@example.com linkedin.com/example/karterbarnes San Mateo, CA 94401 STRENGTHS Ownership Took a defined lab deliverable from pipeline design through robot testing and team demo; mentors trusted follow-up decisions with light supervision. Systems Thinking Connected data curation, orchestration, monitoring, and evaluation in one workflow; peers sought advice when separate tools created unclear results. Practical Debugging Traced failed tasks, malformed records, and GPU queues with logs and metrics; each issue became a clearer recovery step for future runs. Clear Collaboration Turned technical findings into runbooks and demonstrations for students, mentors, and instructors; quiet preparation helped discussions stay useful. Mission Focus Chose robotics infrastructure work tied to safer physical AI; testing on a mobile robot kept model metrics connected with real operating conditions. SKILLS Python Bash SQL AWS GCP Docker Kubernetes Terraform GitHub Actions CI/CD Ray Spark MLflow Data pipelines Experiment tracking LANGUAGES English Native 40 Spanish Intermediate 20 MY CAREER 9 Months ML & Cloud Infrastructure Engineer Intern at University Robotics Infrastructure Lab (2 Months) ML & Cloud Infrastructure Engineer at Autonomous Systems Course Project (4 Months) Cloud Infrastructure Research Assistant at Distributed Computing Research Assignment (3 Months) SUMMARY Early-career ML and cloud infrastructure engineer pursuing a Bachelor of Science in Computer Science , with hands-on experience building Python data, training, and evaluation systems for robotics and computer vision. Developed reproducible pipelines that curate rare events from simulated fleet logs, prepare datasets, and replay field scenarios against model releases. Applied AWS, Docker, Kubernetes, Terraform, Ray, Spark, GitHub Actions, Prometheus, Grafana, MLflow, pytest, and GPU-enabled environments across supervised lab and academic work. Comfortable owning defined deliverables from design through testing, documentation, observability, and team demonstration with mentor guidance. Motivated by physical AI that supports safer, more efficient construction of critical energy infrastructure and eager to contribute practical infrastructure work alongside robotics and software teams. EDUCATION Bachelor's Degree in Computer Science Stanford University GPA: 4.0 2027 Stanford, CA Coursework: Distributed Systems, Machine Learning, Cloud Computing, Autonomous Robotics TECHNICAL SKILLS Programming Languages: Python, Bash, SQL Cloud Platforms: AWS, Google Cloud Platform, EC2 Containers: Docker, Kubernetes, CUDA Infrastructure as Code: Terraform, HCL, AWS IAM CI/CD Tools: GitHub Actions, Git, pytest Distributed Computing: Ray, Apache Spark, Parquet ML Experiment Tools: MLflow, model checkpoints, experiment tracking Observability Tools: Prometheus, Grafana, structured logging Data Storage: Amazon S3, Parquet, partitioned datasets Robotics Evaluation: scenario replay, regression harnesses, mobile robot testing Data Engineering: data curation, schema validation, data-quality checks GPU Infrastructure: GPU clusters, CUDA servers, resource scheduling SKILLS Python Bash SQL AWS GCP Docker Kubernetes Terraform GitHub Actions CI/CD Ray Spark MLflow Data pipelines Experiment tracking EXPERIENCE ML & Cloud Infrastructure Engineer Intern University Robotics Infrastructure Lab June 2026 - Present Stanford, CA Support supervised robotics infrastructure work spanning Python data curation, GPU-enabled evaluation, AWS environments, Kubernetes orchestration, and CI/CD. Own a defined prototype from design through testing, observability, documentation, robot validation, and mentor demonstration, giving research teams a repeatable foundation for model development. Built a Python curation pipeline that mined 180,000 simulated fleet-log records for gust, occlusion, and near-miss events, supplying rare-event data for robotics training. Orchestrated Ray preprocessing and evaluation jobs on a GPU-enabled Kubernetes cluster, documenting resource settings and recovery steps so researchers could rerun experiments reliably. Created Terraform modules for AWS storage, IAM roles, and compute configuration, giving weekly lab experiments reproducible infrastructure with clearer access boundaries. Added Prometheus metrics and Grafana dashboards for queue time, failed tasks, GPU utilization, and incomplete data, helping mentors identify pipeline issues before demonstrations. Established GitHub Actions checks for unit tests, container builds, schema validation, and smoke evaluations, increasing confidence in changes submitted by four student researchers. Presented an end-to-end system to lab mentors and tested replay outputs on a mobile robot in an indoor obstacle course, using findings to strengthen a final technical report. ML & Cloud Infrastructure Engineer Autonomous Systems Course Project January 2026 - May 2026 Stanford, CA Delivered a supervised course project focused on regression evaluation for autonomous navigation. Combined Python, Docker, MLflow, Spark, Parquet, pytest, and CUDA infrastructure into a traceable workflow that helped a student team compare model releases with consistent evidence. Designed a Python regression harness that replayed 420 labeled navigation scenarios against successive model checkpoints, recording accuracy, latency, and collision-proxy results for release decisions. Packaged evaluator and scenario runner with Docker , enabling consistent execution across student workstations and a shared CUDA development server. Used MLflow for datasets, configuration parameters, model artifacts, and evaluation summaries, preserving experiment lineage during model iteration. Implemented Spark transformations that normalized camera, pose, and event metadata into partitioned Parquet datasets, making scenario selection and analysis faster. Added pytest coverage for scenario loading, metric aggregation, and malformed-record handling, catching edge cases during instructor review and release rehearsals. Demonstrated the harness for a 24-person project class and wrote runbooks covering setup, CI checks, experiment comparison, and limits of simulated testing. Cloud Infrastructure Research Assistant Distributed Computing Research Assignment September 2025 - December 2025 Stanford, CA Investigated containerized batch processing for robotics perception data under academic supervision. Compared distributed execution patterns, built quality controls and staging workflows, and translated findings into practical recommendations for scalable evaluation infrastructure . Investigated containerized batch processing for 62,000 annotated image frames and sensor records, clarifying resource needs for robotics perception research. Compared Spark and Ray execution on AWS EC2 instances, recording throughput, memory behavior, and retry characteristics to guide framework selection. Wrote Python data-quality checks for missing timestamps, invalid bounding boxes, duplicate frame identifiers, and inconsistent train-validation splits. Built an S3 staging workflow with Terraform configuration and documented least-privilege access assumptions for a safer course research environment. Exposed job duration, record counts, and exception categories through structured logs and a Prometheus -compatible metrics endpoint, improving experiment visibility. Presented findings at an undergraduate research poster session, recommending Ray for parallel evaluation and Spark for larger tabular transformations. PROJECTS Fleet Rare-Event Curation Pipeline 2026 Created a Python and Ray workflow that mines simulated fleet logs for rare gusts, occlusions, and near-misses. Added schema checks, cloud storage, metrics, and repeatable evaluation steps so curated data could support safer robotics model development. Model Release Regression Harness 2026 Built a Dockerized evaluator that replays labeled navigation scenarios against model checkpoints. MLflow tracking, Spark preparation, Parquet storage, and pytest checks made results easier to compare before simulated release reviews. LEADERSHIP & AWARDS Outstanding Undergraduate Research Poster, Stanford Computer Science Research Showcase, 2026. Dean's List, Stanford University, 2025-2026. Second Place, Stanford Autonomous Systems Hackathon, 2025. Computer Science Department Scholarship, Stanford University, 2025-2026. CERTIFICATIONS AWS Academy Cloud Foundations 2026 Google Cloud Computing Foundations Certificate 2026 Kubernetes Fundamentals, Linux Foundation Training 2026 PROFESSIONAL AFFILIATIONS Cloud Infrastructure Study Group Coordinator, Stanford Computer Science Association, 2025-2026. Peer Mentor, Distributed Systems Course, Stanford University, 2025-2026. Student Research Poster Committee Volunteer, Stanford Robotics Center, 2026. LANGUAGES English (Native) Spanish (Intermediate) ADDITIONAL INFORMATION Work Status : Authorized to work in United States. No sponsorship required. ADDITIONAL INFORMATION Work Status : Authorized to work in United States. No sponsorship required. REFERENCES AVAILABLE ON REQUEST
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