Sr Software Engineer - Engineer

Uber Technologies Inc.; Bengaluru; On-site; INR 3,000,000 - 4,200,000; Full time

Benefits offered by this job

Equity

Bonuses

Benefits

In-office presence

Job summary

Uber Technologies Inc. is seeking a Senior Software Engineer to own major parts of the AI context platforms end-to-end, from prototype to production at monorepo scale.

You’ll work with partner teams to integrate foundational context products with agents running in production for workflows like root-cause analysis and code authoring. The role emphasizes building scalable Graph infrastructures, AI-generated docs, and memory layers, while driving standards and governance across modules.

Qualifications

• 8+ years of software engineering experience in production systems.
• Strong programming in Go, Java, or Python with tested code.
• Hands-on with LLMs and agent-based systems for prompts, retrieval, and tool usage.
• Experience understanding large codebases, static analysis, ASTs, parsers.
• Experience building reliable data pipelines and graph-like data stores.
• Familiarity with MCP and agent-to-agent protocols.
• Strong analytical skills and outcome-driven mindset.

Responsibilities

• Own major parts of the platform end-to-end from prototype to production.
• Scale code graphs across monorepos and microrepos with low-latency interfaces.
• Extend AI-generated documentation to mobile apps, libraries, and data pipelines.
• Design and implement Agentic Memory to persist decisions across sessions.
• Define governance standards for agent instruction files and token budgets.
• Build continuous evaluation for context quality and run A/B experiments.
• Develop MCP servers, skills, and tools for production workflows.
• Provide technical leadership through design reviews and mentoring.

Skills

Go/Java/Python

LLMs

Agentic Systems

Code Understanding

Data at Scale

MCP/Protocols

Problem Solving

Education

Bachelor's in CS

Job description

About the role and team

At Uber, AI agents are increasingly writing, reviewing, testing, and debugging code in production. They deliver high-quality results efficiently, without wasting tokens, only when they understand Uber's codebases. The AI Context Engineering team builds the context foundation that every software-development AI agent at Uber relies on:

• AI Context Graph: a unified graph of services, ownership, domain entities, and business flows


• Code Graph: a graph representation of Uber's monorepos built for agents to consume


• AI-Generated Documentation: documentation that stays fresh as code changes


• Agentic Memory: a memory layer that lets agents learn across sessions

As a Senior Software Engineer, you will own major parts of this platform end to end. You'll take them from prototype to production at monorepo scale, and work with partner teams to integrate foundational context products with the agents running in production for workflows such as root-cause analysis, code authoring, test planning, and library upgrades, etc

What will you do

• Context Graph and Ontology: Enrich the graph with new entity types, such as data streams, metrics, and observability signals, and link them to code so agents can reason across systems.


• Code Graph Infrastructure: Build and scale code graphs across multiple monorepos and microrepos, covering symbols, call edges, dependencies, and links between code and tests. Add incremental updates, versioned (historical) graphs, and function-level embeddings, and serve the graphs to agents with low latency through local and remote interfaces.


• AI-Generated Documentation: Extend automated documentation from backend services to mobile (screens, UI components), libraries, and data pipelines. Use refresh strategies that keep context current as code changes.


• Agentic Memory: Design the memory layer that lets agents persist and reuse decisions, past fixes, and team preferences across sessions.


• Standards and Governance: Define and roll out standards for agent instruction files (AGENTS.md), including structure, content, and token budget, and generate them systematically across modules.


• Context Quality Evaluation: Build continuous evals that measure the accuracy and freshness of every context product, and run A/B experiments that prove the impact on agent outcomes.


• Tooling and Integration: Build MCP servers, skills, and tools that deliver context to agents. Partner with AI Platform, Developer Experience, and product teams to integrate them into production workflows.


• Technical Leadership: Drive design reviews, mentor engineers, and turn ambiguous problems into well-scoped, measurable projects

What will you need

• Experience: 8+ years of software engineering experience building production systems, with a degree in Computer Science or a related field, or equivalent practical experience.


• Programming: Strong in Go, Java, or Python, writing clean, well-tested, maintainable code.


• LLMs and Agentic Systems: Hands-on experience building with LLMs and agents, including prompt and context design, tool use, and retrieval, and a good sense of what context makes agents succeed or fail.


• Code Understanding: Experience with static analysis, ASTs, parsers such as tree-sitter, or build and dependency systems in large codebases.


• Data at Scale: Experience building reliable data pipelines and working with graph or other large-scale data stores.


• Emerging Protocols: Familiarity with MCP and agent-to-agent protocols and how to apply them.


• Problem Solving: Strong analytical skills and a habit of measuring outcomes, not just shipping features.

Success Metrics

• More agentic workflows use curated context in production, with a measured improvement in each workflow's business metric, such as accuracy, cost, latency, or CI time.


• Agents provide higher-quality, better-grounded answers while using fewer tokens and tool calls than when they rediscover context from scratch.


• Context coverage grows across languages, repos, and entity types, with measurable accuracy and freshness.

Bonus Points If you have

• Graph Databases: Experience with Neo4j or similar graph databases, including query optimization and schema design.


• Embeddings and RAG: Experience with embedding generation, vector databases, and retrieval pipelines.


• Evaluation: Experience designing LLM evals, LLM-as-judge methods, or A/B experiments for AI systems.


• Developer Platforms: Experience in developer tooling, CI, or monorepo infrastructure.


• Observability: Experience with Prometheus, Grafana, Langfuse, or similar tools for monitoring AI systems.

Ready to Ride?

This isn't the kind of place where you follow a playbook — it's where you help write one. If you're driven by impact, energized by challenge, and ready to shape how the world moves — we'd love to hear from you.

You may be eligible for bonuses, equity, and other compensation, as well as a range of benefits. Explore our benefits.

Offices remain key to collaboration and Uber's culture. Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence. Ask your Recruiter for details about this role's requirements.

Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.