Software Engineer; Bay Area)

About Nexla

Nexla is the leading Integration platform, built with AI, for AI. Nexla takes a metadata driven approach to converge diverse integrations across Data, Documents, Agents, Applications, and APIs into a single design pattern. We accelerate the development of solutions for GenAI, Analytics, and Inter-company data. Nexla makes data users and developers up to 10x more productive by delivering a true blend of no-code, low-code, and pro-code interfaces.

Nexla is the leading Integration platform, built with AI, for AI. Nexla takes a metadata driven approach to converge diverse integrations across Data, Documents, Agents, Applications, and APIs into a single design pattern. We accelerate the development of solutions for GenAI, Analytics, and Inter-company data. Nexla makes data users and developers up to 10x more productive by delivering a true blend of no-code, low-code, and pro-code interfaces.

Leading companies including Door Dash, Linked In, Johnson & Johnson, and Live Ramp trust Nexla for mission-critical data. Named in the 2022, 2023, and 2024 Gartner Magic Quadrant™ for Data Integration Tools and top-rated by customers on Gartner Peer Insights, headquartered in San Mateo, California.

At Nexla, our culture is built around our core values:
Have Empathy
, Be Curious
, Be Intellectually Honest
, Achieve Excellence
, and Remember to Relax
. We put our customers at the heart of everything we do, foster a data-driven mindset, take ownership of our work, and believe in the power of teamwork to achieve ambitious goals.

We process 500+ billion rows daily, and we are now building the next generation of our platform around AI-native connectors, distributed Python runtimes (Ray), and LLM-powered data workflows. We operate with the intensity of a seed-stage startup and we aren't looking for "cogs in a machine." We are looking for builders who move fast, own outcomes, and don't wait to be told what to do.

As an early-career engineer at Nexla, you will write core code, debug production systems, and help us ship intelligent, context-aware connectors and agentic workflows that power the GenAI revolution.

• Build AI-native connectors (70%):
  Implement intelligent connectors that combine traditional integration primitives (pagination, schema evolution, rate-limiting, retries) with LLM-driven capabilities (semantic understanding, agentic recovery, natural-language tool design). You own your code from the first line to the final deployment.
• Work with LLMs in production:
  Design prompts, tool schemas, evaluation harnesses, and guardrails for LLM-backed features.
• Build on distributed Python runtimes:
  Dive into Ray, Arrow, and modern data-processing stacks (Polars, DuckDB) as part of our agentic runtime initiative. You'll learn how to tune them for throughput, latency, and cost.
• Solve "dirty" data problems:
  Real-world data is messy. You'll build self-recovery mechanisms, agentic probes, and automated retries that keep massive pipelines running without human intervention.
• Work across the stack:
  Move fluidly between backend services, runtime code, agent orchestration, and the occasional frontend touch-up. We don't believe in narrow swim lanes.
• Architectural growth:
  Work directly with our CTO and senior leads to understand why we make certain tech choices and how to design for multi-tenancy, low latency, and AI-native workflows.
• Documentation & quality: "Done" means documented. You'll write SDK docs and RFCs so the rest of the platform and our customers can build on what you ship.

• 2-5 years of software engineering experience.
• Polyglot fluency: Strong in at least one modern backend language (Python, Java/Kotlin/Scala, C++, Go, Rust) and comfortable picking up others as needed. We don't care which language you started in — we care that you can pattern-match across them.
• Full-stack range: Comfortable working across backend, data/runtime, and at least dipping into frontend or infra when the problem calls for it. Specialists who refuse to leave their lane are not a fit.
• CS fundamentals: Data structures, algorithms, concurrency, REST/HTTP, and a working mental model of distributed…