Job description

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 As an Applied AI Engineer at Voltai, you’ll own the full lifecycle of deploying SOTA agentic systems for tool use, code generation, schematic synthesis, and verification—directly impacting leading semiconductor and electronics companies. This can include: • Architect AI systems integrating LLMs with domain-specific retrieval and reasoning across technical artifacts like documentation, datasheets, and design files. • Build structured evaluation pipelines combining benchmarks, user feedback, and task performance • Design scalable data pipelines and real-world datasets for training, fine-tuning, and evaluation You'll collaborate across research and engineering at Voltai to not just build tools but capture the intuition behind solving complex semiconductor and electronics challenges. Required Skill Sets • Expertise in Python, Go, or Rust • Proven software engineering experience with a strong foundation in latest AI concepts • Proficiency in Python and familiarity with AI frameworks such as Langchain, LlamaIndex, or Haystack • Experience with agentic software frameworks, including LangChain, LangGraph, CrewAI, or Microsoft's AutoGen • Understanding of LLM memory, large context management, context awareness, and ingestion of multiple modalities • Hands-on experience with vector databases such as Milvus, Vespa, Elasticsearch or Pinecone (or similar) • Additional experience with graph databases like Neo4j and JanusGraph • Knowledge of structured outputs, function/tool calling mechanisms, and more agentic techniques • Ability to quickly iterate and implement SOTA research papers in this domain Bonus Points • Some background in hardware/electronics, gained through professional, academic, or personal projects • Contributions to open-source initiatives • Notable awards or publications in leading journals/conferences • 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.

Requirements

Must-haves

Architect AI systems integrating LLMs with domain-specific retrieval and reasoning across technical artifacts like documentation, datasheets, and design files.
Build structured evaluation pipelines combining benchmarks, user feedback, and task performance
Design scalable data pipelines and real-world datasets for training, fine-tuning, and evaluation
You'll collaborate across research and engineering at Voltai to not just build tools but capture the intuition behind solving complex semiconductor and electronics challenges.
Required Skill Sets
Expertise in Python, Go, or Rust
Proven software engineering experience with a strong foundation in latest AI concepts
Proficiency in Python and familiarity with AI frameworks such as Langchain, LlamaIndex, or Haystack
Experience with agentic software frameworks, including LangChain, LangGraph, CrewAI, or Microsoft's AutoGen
Understanding of LLM memory, large context management, context awareness, and ingestion of multiple modalities
Hands-on experience with vector databases such as Milvus, Vespa, Elasticsearch or Pinecone (or similar)
Additional experience with graph databases like Neo4j and JanusGraph
Knowledge of structured outputs, function/tool calling mechanisms, and more agentic techniques
Ability to quickly iterate and implement SOTA research papers in this domain

Nice-to-haves

Some background in hardware/electronics, gained through professional, academic, or personal projects
Contributions to open-source initiatives
Notable awards or publications in leading journals/conferences
Experience thriving in a fast-paced, hyper-growth startup environment

Application prep

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Keywords to mirror

aipythongorustllm

Platform: gem
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

Pto