Frontend Engineer Internship at tensorstax.com (San Francisco, CA)

tensorstax.com· San Francisco, CA·3d ago

Role details

Work type

Onsite

Employment

Full-Time

Job description

Location: Remote

About TensorStax

TensorStax is building fully autonomous AI systems to manage and optimize mission-critical data infrastructure. Our platform integrates agentic AI capabilities with large-scale data systems, enabling intelligent orchestration, automated pipeline management, and self-healing infrastructure.

We're looking for a Frontend Engineer Intern to help build and refine the interactive frontend powering our agent framework, data orchestration dashboard, and real-time visualizations.

What We’re Looking For

Experience with Next.js and React, with a strong grasp of modern frontend architecture
Familiarity with Redux or other advanced state management libraries
Ability to implement real-time updates using WebSockets or similar technologies
Comfortable working with complex animations and dynamic UI transitions
Understanding of component-driven development and scalable UI patterns
Strong interest in building systems that respond to real-time event streams

Nice to Have

Experience with design tools like Figma or a strong product/design sensibility
Exposure to agentic or AI-powered user experiences
Familiarity with collaborative, multi-user web environments

Benefits

Mentorship from senior engineers building autonomous AI systems
Potential pathway to a full-time role upon completion

Why work at tensorstax.com

High-Impact, Early-Stage Environment: With only 4 employees, any new hire (especially in engineering/research) will have an outsized impact on the product, culture, and technical direction.
Cutting-Edge Problem: The work sits at the intersection of AI, systems engineering, and data infrastructure, offering significant learning opportunities and challenging technical problems.
In-Office Culture: All posted jobs are located in San Francisco, CA (HQ), suggesting a preference for in-person collaboration.
Strong Backing: Backed by top-tier VC Glasswing Ventures, providing financial runway and strategic support for the next phase of growth.
Opportunity for Growth: Joining a team with huge LinkedIn following growth and a hot product idea means potential for rapid career advancement as the company scales.

About tensorstax.com

Company Overview

One-liner: TensorStax provides an autonomous AI agentic platform that builds, validates, and maintains production-grade data pipelines directly on a company's existing infrastructure.
Entity Type: Private (Seed stage)
Headquarters: San Francisco, California, United States
Founded: Year not publicly specified, but active by 2025.
Founders: Not publicly listed on main sources.

Core Business

Primary industry: Software Development / Artificial Intelligence / Data Engineering
Target customers: Enterprise data engineering teams, data platform teams, and any organization running complex data pipelines on modern data stacks (Snowflake, BigQuery, Redshift, Databricks, etc.).
Mission: To make advanced data infrastructure accessible to all companies by addressing the critical shortage of specialized talent, and to supercharge the rigid domain of data engineering with autonomous AI globenewswire.com.

Products & Services

[Agentic Data OS / Platform]: A SaaS platform that uses autonomous AI agents to plan, generate, and maintain production-grade data pipelines. It integrates directly with existing tooling (dbt, Airflow, Spark, Snowflake, BigQuery, Databricks) and runs in the customer's own cloud. Features include:
- Pipeline Generation: AI generates structured pipeline plans and code based on infrastructure and schemas.
- LLM Compiler: A proprietary deterministic control layer that validates syntax, resolves dependencies, and normalizes tool interfaces, claiming to boost agent success rates from 40-50% to 85-90% globenewswire.com.
- Self-Healing: Detects pipeline failures and automatically creates GitHub pull requests to fix failing code.
- Modeling & Testing: Auto-generates dbt models, tests, and assertions with strong schema typing.
- Security: SOC2 Type 2 compliant, integrates with HashiCorp Vault for credential management, supports self-hosted deployment, and adheres to GDPR and RBAC. The platform never stores or accesses raw data, only metadata and code tensorstax.com.

Market Standing

Valuation/Market Cap: Not disclosed.
Key Metric: $5.3M in total funding.
Funding Round: Seed Round (announced May 12, 2025), led by Glasswing Ventures, with participation from Bee Partners and S3 Ventures globenewswire.com.
Headcount: ~4 employees linkedin.com.
LinkedIn Followers: Over 12,400 (YoY growth of +126%), indicating strong brand interest despite a tiny team linkedin.com.
Growth Signals: Very early-stage, post-Seed with a clear product-market fit hypothesis. High interest from the data community. The company’s technology (LLM Compiler) is a potential key differentiator in a hot market (AI for Data Engineering).

Competitive Advantages

Deterministic AI for a Strict Domain: Unlike general-purpose coding assistants, TensorStax’s proprietary LLM Compiler is purpose-built for the constraints of data engineering, resulting in much higher success rates in production environments (85-90% vs. 40-50% for generic models) globenewswire.com.
Deep Native Integration: The platform integrates directly with an enterprise's existing data stack, rather than requiring a new platform or re-architecting work. This is a major practical advantage for enterprise adoption.
Security-First Architecture: By never storing raw data and operating only on metadata, the platform solves the core security and compliance objection that kills many AI-for-data plays in larger enterprises.

Strategic Focus

Scaling the Engineering Team: Actively hiring for key technical roles (Research Engineer, Frontend Engineer, Interns) to build out the core platform and AI capabilities builtin.com.
Accelerating Product Development: The $5.3M seed round is explicitly earmarked for this purpose globenewswire.com.
Enterprise Adoption: Focused on demonstrating value to early adopters and building a repeatable sales motion for enterprise data teams.

Sources