Software Engineer (Bay Area)

Nexla•San Mateo, CA

2d•$150,000 - $180,000•Hybrid

About The Position

Nexla is a leading AI-powered integration platform that uses a metadata-driven approach to unify diverse integrations across Data, Documents, Agents, Applications, and APIs into a single design pattern. This accelerates the development of solutions for GenAI, Analytics, and Inter-company data, making data users and developers up to 10x more productive through a blend of no-code, low-code, and pro-code interfaces. Trusted by companies like DoorDash, LinkedIn, Johnson & Johnson, and LiveRamp, Nexla is recognized in the Gartner Magic Quadrant™ for Data Integration Tools and is headquartered in San Mateo, California. The company culture is built on core values of Empathy, Curiosity, Intellectual Honesty, Achieving Excellence, and Remembering to Relax, with a strong focus on customer centricity, a data-driven mindset, ownership, and teamwork. This role involves working on the next generation of Nexla's platform, focusing on AI-native connectors, distributed Python runtimes (Ray), and LLM-powered data workflows. The team operates with the intensity of a seed-stage startup, seeking builders who are proactive, own outcomes, and are not afraid to take initiative. As an early-career engineer, you will contribute to core code, debug production systems, and help ship intelligent, context-aware connectors and agentic workflows essential for the GenAI revolution.

Requirements

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 learning others.
Full-stack range: Comfortable working across backend, data/runtime, and with some exposure to frontend or infrastructure.
Solid understanding of CS fundamentals: Data structures, algorithms, concurrency, REST/HTTP, and distributed systems.
High agency, high urgency, high ownership: Proactive in diagnosing, deciding, shipping, and communicating; takes ownership of production issues.
A 'builder' spirit demonstrated through a GitHub repo, side project, technical blog, or open-source contribution.
Familiarity with AI tools (e.g., Claude Code, Cursor, agentic workflows) and an understanding of LLM applications and limitations.
Ability to overlap with evening PST working hours for collaboration with India/Europe-based teams.

Nice To Haves

Experience with Ray, Arrow, Polars, DuckDB, or other modern Python-native data stacks.
Hands-on experience building with LLMs, including RAG pipelines, agentic systems, tool/function calling, evals, or MCP servers.
Exposure to Kafka, JVM stacks (Java/Kotlin/Scala), Snowflake, Databricks, or Spark.
Experience with Docker or Kubernetes.
Background in competitive programming or contributions to open-source projects.

Responsibilities

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). Own code from development to deployment.
Design prompts, tool schemas, evaluation harnesses, and guardrails for LLM-backed features.
Work with distributed Python runtimes like Ray, Arrow, and modern data-processing stacks (Polars, DuckDB) as part of the agentic runtime initiative, learning to tune them for throughput, latency, and cost.
Build self-recovery mechanisms, agentic probes, and automated retries to ensure massive pipelines run without human intervention.
Work across the full stack, including backend services, runtime code, agent orchestration, and occasional frontend tasks.
Collaborate directly with the CTO and senior leads on architectural decisions, understanding design principles for multi-tenancy, low latency, and AI-native workflows.
Write SDK documentation and RFCs to ensure the platform and customers can build upon delivered features.