Senior Research Engineer - AI-Native Online Datastore Systems

This is a greenfield opportunity. You won't be maintaining existing AI systems-you'll be the team's first dedicated AI-Native Research Engineer, balancing research exploration with hands-on engineering: studying how agents work best in online data storage contexts, and turning those insights into reusable infrastructure and workflows. Here, you will tackle world-class challenges in globalization, multi-region active-active, compliance, and cost efficiency-while using AI to redefine how storage systems are built and operated.

Responsibilities
- Design and build the AI Context & Knowledge Layer: Architect a centralized context layer for the online datastore systems team, integrating knowledge bases, runbooks, code repositories, and real-time system state so AI agents have grounded, traceable, team-specific domain knowledge. Continuously iterate on knowledge structure and retrieval strategies to improve answer quality and evidence-chain completeness.
- Research and implement Agentic Workflows: Explore agent applications across the development lifecycle (AI-assisted coding, automated testing, PR pre-review, deployment validation) and the operations lifecycle (oncall triage, structured troubleshooting, storage governance task submission). Design multi-agent orchestration frameworks with tiered permission and safety guardrails (read-only / confirm-then-execute / human-execute).
- Establish Agent Evaluation & Experimentation: Design offline eval sets and core metrics (hit rate, evidence-chain completeness, misoperation rate). Validate agent effectiveness through shadow mode or A/B experiments. Build a reproducible evaluation methodology to drive continuous improvement. Share findings through tech talks or internal write-ups.
- Storage Platform Skill-ification & Toolchain Integration: Encapsulate core online storage capabilities (metadata queries, workflow troubleshooting, storage replica governance, DDL changes, etc.) as reusable Skills. Integrate with gdpa-cli, lark-cli, and real-time query tools. Build standardized AI development environments.
- Drive AI-Native Adoption & Enablement: Accelerate team-wide adoption through pairing, workflow demos, architecture reviews, and best-practice documentation. Balance AI-driven speed with code quality and system safety. Serve as the technical advocate for AI-native transformation.

Qualifications

Minimum Qualification(s):
- Research background (required - one of the following):
- PhD in Computer Science, Artificial Intelligence, or a related field; or
- Thesis-based Master's degree with a research focus in AI, ML, software engineering, or systems.
- Proficiency in Python and Go (or C++ / Java), with the ability to translate research prototypes into production systems; AI-Native coding mindset.
- Deep expertise in LLM application engineering: RAG, tool use / function calling, structured outputs, prompt / context engineering; proven track record of engineering research into shipped systems.
- Solid distributed systems foundation; familiarity with online storage, caching, data sync, or metadata management is a plus.

Preferred Qualification(s):
- Peer-reviewed publications or top-tier conference acceptances in agent systems, LLM applications, AIOps, or SE4ML.
- Research-grade experience designing agent evaluation, offline benchmarks, or experimentation frameworks.
- Multi-agent orchestration and guardrail design for high-risk operations.
- Global online storage infrastructure background (multi-region active-active, cross-region sync, compliance governance).

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $212800 - $450000 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.

Client-provided location(s): San Jose, CA

Job ID: TikTok-7658238613570718005

Employment Type: OTHER

Posted: 2026-07-08T20:20:01