--- title: 'Research Engineer, Reinforcement Learning at tensorstax.com' canonical: 'https://feeny.ai/job/research-engineer-reinforcement-learning-tensorstax-com-san-francisco-gys442sy11x4' type: 'job' last_seen: '2026-09-09' --- # Research Engineer, Reinforcement Learning at tensorstax.com - **Company:** tensorstax.com - **Location:** San Francisco, CA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2025-03-17 - **Last confirmed live:** 2026-09-09 - **Apply:** https://jobs.gem.com/tensorstax-com/am9icG9zdDoQG5-NDMBZ8s-Kvr21tIC2 ## Job description Research Engineer – Reinforcement Learning Location: San Francisco (Hybrid) ## About TensorStax TensorStax is building fully autonomous AI systems to manage and maintain mission-critical data infrastructure and pipelines. We leverage reinforcement learning to enhance language models' ability to reason over large-scale data lakes and warehouses, detect pipeline failures, construct new pipelines with high precision, and enable agentic behavior—allowing systems to proactively identify and resolve issues autonomously. As a Research Engineer specializing in Reinforcement Learning, you will: - Develop and refine reward functions to optimize agent behavior for complex data engineering tasks. - Create RL gym environments for language model agents. - Fine-tune language models using reinforcement learning techniques such as PPO, DPO, and KTO. - Stay at the forefront of research on RL for language models, incorporating advancements like GRPO, SWE-Gym, and SWE-RL into practical applications. - Curate and build high-quality datasets for supervised fine-tuning (SFT) and RLHF. - Design experiments to evaluate and improve the agentic capabilities of language models in data environments. What We’re Looking For: - Deep understanding of reinforcement learning, reward shaping, and optimization strategies. - Strong familiarity with LLM fine-tuning techniques (PPO, DPO, KTO) and their applications in reinforcement learning. - Knowledge of recent advancements in RL for language models (GRPO, SWE-Gym, SWE-RL). - Experience curating and constructing high-quality datasets for fine-tuning. - Strong problem-solving skills and a history of working on complex ML projects. - High agency—ability to work independently, experiment proactively, and drive research initiatives forward. Bonus Points: - Experience with distributed training in PyTorch (DDP, FSDP). - Hands-on experience designing RL environments for traditional RL problems. - Contributions to open-source projects in RL, LLMs, or ML infrastructure. - Familiarity with data lakes and warehouses (Snowflake, BigQuery, Redshift). Benefits: - 100% employer-covered health, dental, and vision insurance. - 401(k) with company match. - Access to Bay Club or Equinox in San Francisco. ## 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](https://www.globenewswire.com/news-release/2025/05/12/3078960/0/en/tensorstax-raises-5m-to-build-deterministic-ai-agents-for-data-engineers.html). ## 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](https://www.globenewswire.com/news-release/2025/05/12/3078960/0/en/tensorstax-raises-5m-to-build-deterministic-ai-agents-for-data-engineers.html). - **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](https://www.tensorstax.com/?tpcc=NL_Marketing). ## 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](https://www.globenewswire.com/news-release/2025/05/12/3078960/0/en/tensorstax-raises-5m-to-build-deterministic-ai-agents-for-data-engineers.html). - **Headcount**: ~4 employees [linkedin.com](https://linkedin.com/company/tensorstax). - **LinkedIn Followers**: Over 12,400 (YoY growth of +126%), indicating strong brand interest despite a tiny team [linkedin.com](https://linkedin.com/company/tensorstax). - **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](https://www.globenewswire.com/news-release/2025/05/12/3078960/0/en/tensorstax-raises-5m-to-build-deterministic-ai-agents-for-data-engineers.html). - **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](https://builtin.com/company/tensorstax/jobs). - **Accelerating Product Development**: The $5.3M seed round is explicitly earmarked for this purpose [globenewswire.com](https://www.globenewswire.com/news-release/2025/05/12/3078960/0/en/tensorstax-raises-5m-to-build-deterministic-ai-agents-for-data-engineers.html). - **Enterprise Adoption**: Focused on demonstrating value to early adopters and building a repeatable sales motion for enterprise data teams. ## Why Work Here - **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. ## Sources 1. [tensorstax.com](https://www.tensorstax.com/?tpcc=NL_Marketing) 2. [builtin.com](https://builtin.com/company/tensorstax/jobs) 3. [linkedin.com](https://linkedin.com/company/tensorstax) 4. [globenewswire.com](https://www.globenewswire.com/news-release/2025/05/12/3078960/0/en/tensorstax-raises-5m-to-build-deterministic-ai-agents-for-data-engineers.html) ## Other roles at tensorstax.com - [Frontend Engineer Internship](https://feeny.ai/job/frontend-engineer-internship-tensorstax-com-san-francisco-p2fsjzkacnkr) — San Francisco, CA - [Research Engineer Intern, Evaluations](https://feeny.ai/job/research-engineer-intern-evaluations-tensorstax-com-san-francisco-hbe0m5zncybc) — San Francisco, CA - [Backend Engineer, Data & Agent Platform](https://feeny.ai/job/backend-engineer-data-agent-platform-tensorstax-com-san-francisco-c5k22xqjdww2) — San Francisco, CA