Akidev Corporation
Data Engineer III
Menlo Park, California, USA · Mid · contract, third party
Sponsorship not specifiedDetected 50 days ago
SQLMachine LearningAirflowData AnalysisData EngineeringNLPComputer VisionLLMsAI OrchestrationAuditingCommunicationProblem Solving
About the role
- Start/End Dates: 7/13/2026 - 12/31/2026 Tax Work Location: US - CA - Menlo Park (105201) Job Title: Data Analytics & Engineering - Data Engineer III Job Description: Summary Generative AI models are only as good as the data they consume.
- This role sits at the intersection of Data Engineering and ML Systems.
Responsibilities
- Design and maintain AI-augmented, large-scale data pipelines (billions of images) integrating traditional transformations with ML models (classifiers, embeddings, LLMs) for cleaning and annotation.
- Build and maintain scalable pipelines for generating, storing, and serving vector embeddings, including nearest-neighbor index management and quality validation.
Requirements
- it demands hands-on systems experience with distributed inference infrastructure.
Nice to have
- Skills Required Advanced SQL & data pipeline expertise.
- Experience integrating ML models into data pipelines.
- Calling inference endpoints, managing model versions, batching requests, handling inference failures at scale.
- Proficiency with AI-assisted coding agents (e.g., Copilot, Cursor, Codex).
- Preferred Working knowledge of embeddings and vector representations like generating, storing, indexing, and querying embeddings (FAISS, Milvus, or equivalent).
- Familiarity with content-understanding models like image classifiers, object detection, OCR, NSFW detection, aesthetic scoring.
- Experience with LLMs for data tasks like prompt engineering for annotation, data cleaning, or evaluation using LLM APIs.
- Knowledge of generative AI like diffusion models, image generation, evaluation metrics (FID, CLIP score, etc.).
Skills
- Complex queries, query optimization, pipeline orchestration frameworks (Airflow, Dataswarm, or equivalent).
Benefits
- Design and operate pipelines that use LLMs and vision models for automated annotation of training data, including auditing workflows to measure and improve annotation model performance.
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