Senior Back End Engineer

Job Title: Senior Backend AI Engineer

Location: Fully Remote

Company Overview

Founded in 2019, Elife Transfer is a fast-growing startup headquartered in San Francisco, California, at the heart of Silicon Valley. We are an all-in-one global ground transportation marketplace, enabling travelers to book airport transfers, ride-hailing, shared rides, private cars and rail tickets. Trusted by over 40 million travelers across 182+ countries, we are rapidly scaling to become the world’s go-to platform for seamless, end-to-end ground mobility.

Key Responsibilities:

Agent Architecture & Pipeline Design

• Design and operate multi-agent systems with orchestrator and specialist agents covering planning, coding, testing, and review
• Build feedback loops so agents can detect failures, read error output, and self-correct without human intervention
• Define agent tool APIs: shell execution, code interpreter, file system access, git operations, CI triggers
• Implement sandboxed execution environments (Docker, Firecracker, E2B) for safe autonomous code execution

Automated Software Delivery

• Build pipelines where AI autonomously generates unit, integration, and regression tests from specifications
• Integrate agents with GitHub/GitLab: branch creation, PR lifecycle management, automated review bots
• Implement CI pipeline agents that interpret test results, triage failures, and propose fixes
• Automate code review: style checking, correctness analysis, security scanning — all agent-driven

LLM & Reasoning Stack

• Design prompting strategies for code generation, test synthesis, PR narration, and review response
• Build and maintain RAG pipelines over codebases using vector databases with AST-aware chunking
• Manage context windows for long-horizon tasks that span multiple files and subsystems
• Implement fine-tuning and RLHF pipelines to specialize models for domain-specific code generation

Evaluation & Quality

• Define success metrics for agent output before any system ships to production
• Build and maintain evaluation harnesses that test agent quality systematically across scenarios
• Benchmark agent performance regressions on each model update or pipeline change
• Track token costs, latency, and failure rates per agent run through structured observability

Safety & Reliability

• Apply least-privilege execution to every autonomous agent: scope permissions to the minimum required
• Implement human-in-the-loop gates for destructive or irreversible actions
• Defend against prompt injection in tool-call pipelines exposed to untrusted content
• Ensure all agent actions are idempotent — safe to retry without side effects
• Define and enforce token budgets and cost throttling per agent run

Requirements

Non-negotiable experience

• 5+ years of backend engineering in production systems
• 2+ years designing or building agentic AI systems (not chatbots or AI autocomplete)
• At least one production multi-agent system shipped and maintained end-to-end
• Direct experience with agent output where AI authored the majority of code diffs
• Hands-on with an LLM evaluation harness for code quality assessment

Technical skills

• Python as primary language; Go or Rust for performance-critical components
• API design for agent tool endpoints (REST, async queues, event-driven architectures)
• Kubernetes-based agent orchestration and scaling
• Vector databases and embedding pipelines (Weaviate, Qdrant, Pinecone)
• Distributed tracing and observability tooling (OpenTelemetry, Datadog, LangSmith)

Nice to have

• Experience with SWE-bench, HumanEval, or other code generation benchmarks
• Contributions to open-source agentic frameworks
• Knowledge of formal verification or property-based testing for agent output validation
• Experience deploying LLMs on custom hardware (A100/H100 clusters)
• Background in compiler design or static analysis (useful for AST-level code understanding)