About Voltai

Voltai is the leading AI company building agentic systems and frontier foundation models for semiconductor and electronics design. Backed by Sequoia Capital, we’re putting AI in the hands of hardware engineers in over 70% of the world’s largest semiconductor and electronics companies to have effortless control over their next-generation chip and board designs, powering the future of automotive, industrial automation, consumer electronics, IoT, and semiconductor manufacturing.

About the Team

Our founding team consists of IOI/IPhO olympiad medalists, Stanford professors, ex-CTO of Synopsys, and our business leadership has scaled revenue in their previous companies to over $1.5bn. At Voltai, we are combining the world’s best talent in the intersection of software and hardware.

Key Responsibilities

Collect, parse, and structure diverse data types—including text, images, tables, circuit diagrams, simulations, and signal data—into standardized formats suitable for machine learning applications
Design and maintain scalable data pipelines that efficiently handle data ingestion, transformation, and integration into ML workflows, ensuring high throughput and reliability
Optimize data storage solutions to balance performance, scalability, and cost-effectiveness, facilitating rapid access and processing of large datasets
Collaborate with cross-functional teams, including ML and infra engineers, to curate high-quality training and evaluation datasets aligned with Voltai's product offerings
Implement robust data validation and quality assurance processes to ensure the integrity and usability of datasets across various applications.

Required Skillsets

Programming Languages: Proficiency in Python, with experience in compiled languages such as Go or Rust
Data Parsing and Extraction: Expertise in parsing and extracting data from various formats and modalities, including PDFs, HTML, images, and binary files, utilizing tools like BeautifulSoup, pdfminer.six, and custom parsers
Data Pipeline Frameworks: Experience with modern data pipeline frameworks such as Apache Airflow, Prefect, Dagster, or Apache Beam, enabling efficient orchestration of complex data workflows
Data Processing Tools: Familiarity with tools like Apache Spark, Apache Flink, or similar platforms for large-scale data processing and transformation
Database Systems: Strong knowledge of relational and non-relational databases, including PostgreSQL, Supabase, and other scalable storage solutions
Cloud Platforms: In-depth experience with cloud services, particularly AWS, including S3, EC2, Lambda, and related services for deploying and managing data infrastructure
Web Crawling and Agentic Crawling: Proficiency in building and managing web crawlers using frameworks like Scrapy, Firecrawl, or Crawl4AI, with an understanding of agentic crawling techniques to automate data extraction tasks
Data Quality and Governance: Commitment to maintaining high data quality standards, with experience in implementing data validation, cleansing, and governance practices

Bonus Points

A strong background in hardware/electronics, gained through professional, academic, or personal projects
Experience in constructing datasets for large scale ML models, specifically LLMs
Contributions to open-source initiatives
Experience thriving in a fast-paced, hyper-growth startup environment

Our Benefits

Unlimited PTO: Recharge when you need it, no questions asked.
Comprehensive Health Coverage: Medical, dental, and vision insurance for you and your dependents.
Free Meals and Snacks: Daily lunches, dinners, and snacks in the office.
Professional Growth: We invest in your continuous learning and offer opportunities to expand your skills.
Visa Sponsorship: We welcome global talent and provide visa sponsorship to support qualified candidates.

Software Engineering - Data Engineer

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