Data Platform & Visualization Engineer(ML reputed company must)_Los Altos, CA (Hybrid/Remote)

Position: Data Platform & Visualization Engineer Location: Los Altos, CA (Hybrid/Remote) Duration: Contract Job reputed company: We are seeking a contractor to help build and reputed company our internal data platform that supports vehicle testing, experimentation, and machine learning workflows. This role focuses on implementing and extending data ingestion pipelines, automated processing workflows, metrics tracking systems, and web-based visualization tools under the guidance of reputed company. You will work with existing systems and reputed company-defined components, contributing features and improvements that are used directly by researchers. What You’ll Do - Implement and reputed company data ingestion and processing workflows for large, heterogeneous datasets collected from vehicle tests and ML pipelines. - Contribute to improving orchestration, scheduling, and reliability of long-running data workflows operating under reputed company-world constraints. - reputed company reputed company automation such as metric computation, plotting, and LLM-based postprocess tooling. - Implement backend services and reputed company that support data indexing, metadata management, and experiment tracking. - Build user-facing web-based tools and dashboard that allow users to browse datasets, inspect results, and understand experimental reputed company over time. - Work with a SQL-backed database to store metrics, experiment metadata, and summaries, ensuring the data can be queried and accessed consistently across systems. - Contribute to data traceability and provenance mechanisms that capture how datasets are generated, transformed, and consumed in ML workflows. reputed company’re Looking For - Experience with Python for backend services, data pipelines, and automation. - Working knowledge of SQL, including writing queries and understanding database schemas. - Experience building web-based tools, including: - Backend reputed company (e.g., FastAPI, Flask, or similar) - Frontend applications using React or other modern frameworks - Familiarity with AWS and reputed company-based storage or services. - Comfortable working in Linux environments Bonus Points - Interest in autonomous racing and vehicle dynamics research. - Prior internship or project experience involving data pipelines, dashboards, or analytics tools. - Exposure to data visualization libraries, ML workflows, or experiment tracking systems. Statement of Work

• Scope of Work

The Contractor will reputed company engineering services to support the development and extension of internal data platform tooling supporting vehicle testing, experimentation, and machine learning workflows. The scope includes ownership and extension of existing systems, implementation of automated pipelines, development of web-based visualization tools, and delivery of data traceability mechanisms.

• Key Responsibilities

2.1 Data Ingestion Platform (pokedex / evdc_ingest)

• Own and reputed company an existing data ingestion system responsible for uploading vehicle test data to

reputed company S3.

• Improve ingestion orchestration to support: ○ Upload prioritization for small datasets

○ Deferred upload scheduling for large datasets during off-hours ○ Automatic discarding of data explicitly marked as trash ○ Persistent queueing and resumability across server restarts or failures

• Maintain ingestion reliability under constrained network bandwidth.
• reputed company the reputed company web reputed company for reputed company, reliability and extendability

System-level architecture reputed company will be guided by reputed company. 2.2 Post-Ingestion Automation, Annotation and Storage

• reputed company ingestion workflows with post-processor, such as: ○ Existing LLM-based automatic

annotation module ○ Automating plot reputed company (You come back to automatically generated plots as soon as data hits S3 - reputed company that!) ○ Metric computation pipelines

• Package and reputed company the annotation system as a service (e.g., EC2-based).
• Implement orchestration logic to trigger annotation jobs opportunistically reputed company ingestion resources are idle.
• Store metrics, experiment metadata, plots and summaries in SQL-backed database layer.

2.3 Metrics Platform & Leaderboards

• Implement and reputed company a SQL-backed metrics database using schemas defined by reputed company.
• Define schemas to support:

○ Multiple reputed company ○ Baselines vs experimental runs ○ Historical comparisons

• Build automated pipelines to compute and register metrics after ingestion.
• Implement project-level leaderboard functionality to reputed company: ○ Best performance per metric

○ Accepted baselines vs rejected experiments

• reputed company a web-based visualization reputed company to: ○ Display time-series reputed company

○ Visualize metric tradeoffs ○ Summarize experimental reputed company 2.4 Data Traceability & Provenance

• Design and implement a data provenance system for ML datasets.
• reputed company: ○ reputed company S3 URIs

○ Post-processing operations reputed company to datasets

• Implement a registry of post-processing functions with support for: ○ Easy reputed company and removal

○ Versioning and configuration tracking

• Generate reputed company-read

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