Software Engineer (Bay Area)

Job Description

Job Description

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.

Leading companies including DoorDash, LinkedIn, Johnson & Johnson, and LiveRamp 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.

Role
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.

Responsibilities

• 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.

Qualifications
Must-Haves

• 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/ and a working mental model of distributed systems.
• High agency, high urgency, high ownership: You don't wait for a perfectly groomed ticket. You diagnose, you decide, you ship, and you communicate. You treat production issues as your problem regardless of whose code it was.
• The "builder" spirit: A GitHub repo, side project, technical blog, or open-source contribution that shows you love to tinker and learn.
• AI-native: You already use AI tools (Claude Code, Cursor, agentic workflows) as a multiplier on your own work, and you have opinions about where LLMs help and where they don't.
• Global collaboration window: Ability to overlap with evening PST working hours for syncs, design reviews, and collaboration with our India/Europe-based leadership and engineering teams.

Nice-to-Haves

• Experience with Ray, Arrow, Polars, DuckDB , or other modern Python-native data stacks.
• Hands-on experience building with LLMs - RAG pipelines, agentic systems, tool/function calling, evals, or MCP servers.
• Exposure to Kafka, JVM stacks (Java/Kotlin/Scala), Snowflake, Databricks, or Spark - useful context for our existing platform, but not required.
• Experience with Docker or Kubernetes .
• A background in competitive programming or contributions to open-source projects.

Why This Might Be Worth It

• Unmatched scale: Your code will process billions of rows for global brands on day one.
• Direct mentorship: Work in a small, elite team with direct access to senior leadership.
• AI-first engineering: We aren't just "using" AI; we are building the infrastructure that makes AI possible for the enterprise — connectors, runtimes, and agentic workflows.

Compensation:
Compensation for this role will be determined by overall skills, experience, and location. The salary range for a US-based Software Engineer will be $150,000–$180,000 USD. The package will also include benefits such as Medical, Dental, and Vision, 401k, and flexible PTO.

Why Build Your Future at Nexla? We are standing at the precipice of the GenAI revolution, but the biggest bottleneck isn't the models, it's the data. By joining Nexla, you aren't just entering a company; you are stepping into the critical layer of the modern data stack that powers the AI economy. We are the Data Fabric that enables industry titans like LinkedIn, DoorDash, and J&J to turn messy, siloed data into ready-to-use products for RAG and predictive models. This is your opportunity to move beyond simple tooling and build the actual infrastructure that democratizes data access for the next decade of innovation. If you want to solve the hardest problems in data engineering and own a piece of a market projected to hit billions, your career belongs here.