Job Description

Senior Software Engineer, AI Center of excellence



Uber's AI Center of Excellence for Security and Privacy is dedicated to building robust, scalable AI-driven products, engineering standards, and policies that proactively safeguard user data, fortify our security posture, and foster trust through transparent communication and innovation. As a leader on this team, you will:

1. Shape Strategy:

Define and drive the roadmap for AI/ML-powered security and privacy solutions that span user-facing applications, downstream services, and core infrastructure platforms.

2. Architect & Build:

Lead the end-to-end design, development, and deployment of high-impact tools and frameworks that integrate seamlessly across Uber's ecosystem.

3. Collaborate & Evangelize:

Partner with product, engineering, legal, and policy teams to translate complex security and privacy requirements into practical AI solutions-and champion best practices across the organization.

4. Mentor & Grow:

Coach engineers and data scientists in secure ML development, threat modeling, and privacy-preserving techniques, helping to elevate the team's technical expertise.



Basic Qualifications

1. End-to-End AI/ML & GenAI Expertise:

Proven track record designing, developing, and deploying production-quality AI/ML solutions-especially generative AI systems-spanning data ingestion, prompt engineering, model fine-tuning, retrieval-augmented generation (RAG), and inference optimization.

2. Large Language Model Proficiency:

Hands-on experience with foundation models (e.g., GPT, PaLM, LLaMA) and open-source alternatives; adept at prompt design, chain-of-thought engineering, embedding creation, and custom fine-tuning workflows.

3. Agent Development & Orchestration:

Experience building and deploying AI agents-designing multi-step workflows, tool integrations, and autonomous decision-making pipelines to solve complex tasks and drive business value.

4. GenAI Infrastructure & MLOps:

Familiarity with MLOps pipelines for GenAI (model versioning, CI/CD, monitoring, A/B testing), container orchestration (Docker/Kubernetes), and scalable deployment of LLMs in cloud or on-prem environments.

5. Data & Vector Store Management:

Ability to build and maintain scalable data pipelines and vector databases (e.g., FAISS, Pinecone, Weaviate) for efficient semantic search and knowledge retrieval.

6. Robust Software Engineering:

Strong coding skills in Python (and/or Java, Go) with experience in ML frameworks (TensorFlow, PyTorch, scikit-learn) and building secure, maintainable APIs and microservices.



Preferred Qualifications

1. LLM Fine-Tuning Expertise:

Hands-on experience fine-tuning large language models using techniques such as LoRA, PEFT, or full-parameter updates on both open-source and proprietary models.

2. Agent Frameworks:

Prior work building or extending agent frameworks (e.g., LangChain, LlamaIndex, or custom in-house solutions) for multi-step reasoning and tool orchestration.

3. Prompt Engineering Mastery:

Deep familiarity with prompt templating, few-shot/zero-shot strategies, chain-of-thought prompting, and mitigation of prompt drift or hallucinations.

4. GenAI Evaluation & Testing:

Experience designing evaluation protocols for generative models (automated metrics and human-in-the-loop testing), with an emphasis on security, bias detection, and reliability.



What the Candidate Will Do

1. Define & Drive AI Security/Privacy Solutions:

Architect and implement end-to-end GenAI and ML systems-agents, RAG pipelines, fine-tuned LLMs, and custom models-that proactively detect and mitigate security threats and privacy risks.

2. Build & Operate Platforms:

Develop scalable data pipelines, vector stores, and MLOps infrastructure (CI/CD, monitoring, model-versioning) to support continuous delivery of AI-driven security and privacy features.

3. Integrate with Uber Ecosystem:

Embed AI capabilities into user-facing products, downstream services, and core infrastructure tools-designing APIs, microservices, and tooling for seamless adoption by engineering teams.

4. Collaborate Cross-Functionally:

Partner closely with Product, Legal, Policy, and Security Operations to translate requirements into technical specs, ensure compliance with data-privacy regulations, and align on risk-mitigation strategies.

5. Develop & Maintain Agents:

Build autonomous AI agents and orchestration workflows that interface with internal systems and external tools, automating complex security and privacy tasks.