Research Scientist Graduate (Conversational AI) - 2027 Start (PhD)

TikTok; San Jose (CA); On-site; USD 162,000 - 388,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with company match

Paid parental leave

Short-term and long-term disability

Life insurance

Wellbeing benefits

10 paid holidays, 10 paid sick days,

17 days of Paid Personal Time

Job summary

TikTok is seeking a Research Scientist Graduate (Conversational AI) in San Jose for a 2027 start. You will work on agent runtimes, memory, tool orchestration, and safe, scalable conversational systems. The role emphasizes self-evolving loops, evaluation rigor, and production-grade guardrails.

Ideal candidates hold a PhD in CS/EE/Math/Stats, strong Python plus one of C++/Go/Rust/Java, and hands-on ML/NLP with LLMs or agents. Collaboration across teams and data-driven iteration is essential.

Qualifications

• PhD in CS/EE/Math/Stats or related field.
• Strong Python and one of C++/Go/Rust/Java.
• Solid ML/DL/NLP fundamentals with hands-on LLMs or agents.
• Basic statistical literacy: confidence intervals and p-values.
• Ability to read a paper or engineering blog and turn into code.

Responsibilities

• Architect agent runtime and loop control; implement safe pathways and guardrails.
• Develop memory and context management for long multi-turn conversations.
• Lead data flywheel: SFT/DPO/RLHF and distillation into smaller models.
• Manage tools, skills, and MCP with governance and on-demand loading.
• Design evaluation strategies: LLm-as-judge, cross-family panels, and canary tests.
• Build auditable pipelines: candidate registry, runtime read-back, change lineage.

Skills

Python

C++/Go/Rust/Java

ML/NLP fundamentals

Statistical literacy

Code-to-paper translation

Education

PhD in Computer Science, Electrical Engineering, Mathematics or Statistics

Tools

vLLM

TensorRT-LLM

KV / prompt caching

Job description

Research Scientist Graduate (Conversational AI) - 2027 Start (PhD)

Location:

San Jose

Employment Type:

Regular

Job Code:

A143674

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Responsibilities

• Agent runtime (harness / agent loop). Orchestrate skills, tools, and context; implement loop control & intervention, progressive disclosure, and behavior-level guardrails. Build the production safety layer — pre-flight budgets and timeout truncation, serve-time gates, shadow / swap-in answer delivery, and safe fallback paths.
• Context & memory for long multi-turn agents. Agentic memory (structured note-taking), context compaction / summarization, context editing / observation masking, and just-in-time (retrieve-then-load) retrieval. Treat context as an evolving, itemized playbook — with structured diffs and a deterministic curator — rather than an ever-growing prompt.
• Post-training & the data flywheel. SFT / DPO / RL to internalize rules into weights (so the prompt gets shorter, not longer), plus distillation to smaller serving models. Turn served conversations into training / eval / retrieval signals.
• Tools, Skills, and MCP. Tools-as-APIs, connectors, skill / tool search for large inventories, and skill-library governance — description conflicts, trigger evals, cross-skill mis-fire matrices, and on-demand loading instead of dumping every definition into context.
• Evaluation you can bet a launch on. LLM-as-judge with human-agreement calibration; statistical rigor — paired comparison, confidence intervals, repeated sampling, pass^k; held-out and time-rolling eval splits with overfitting alarms; cascaded scoring and cross-family judge panels to make evaluation affordable at scale.
• The self-evolving loop. Case mining → automatic root-cause → constrained candidate generation → replay verification against frozen regression sets → canary → flywheel. Build the plumbing that makes it auditable: candidate registry with exact runtime read-back, change lineage, and an archive of rejected candidates you can sample from next round.
• Online experimentation & causal readout. Shadow / canary / A-B, non-inferiority gates, traffic-split health, metric definitions that survive scrutiny, and off-policy counterfactual evaluation where live A/B isn't possible.

Minimum Qualification(s)

• Individuals who are completing or have recently completed a PhD degree in Computer Science, Electrical Engineering, Mathematics, Statistics or a related discipline
• Strong Python plus one of C++ / Go / Rust / Java
• Solid ML / DL / NLP fundamentals, with genuine hands-on experience with LLMs or agents (coursework, research, internship, competition, open-source, or a serious side project)
• Basic statistical literacy — you can compute a confidence interval, explain what a p-value does and doesn't mean, and tell the difference between "the number went up" and "the system got better"
• Able to read a paper or an engineering blog and turn it into working code

Preferred Qualification(s)

• Have built the runtime, not just called an API — even at research / hobby / competition scale: your own agent loop / harness, a memory / context-management system, a RAG or tool-use agent, or a fine-tuned / post-trained model
• Hands-on experience in any one of the following — depth in one is enough, breadth welcome:
  • Post-training: SFT / DPO / RLHF / RLAIF / RLVR, reward modeling, reward hacking and how to defend against it
  • Agent systems: harness, context engineering, MCP / Skills, sub-agents, tool search
  • Evaluation & experimentation: LLM-as-judge and judge calibration, pass^k, regression suites, A/B and non-inferiority testing, off-policy evaluation
  • Self-improving / evolutionary systems: evolutionary program search, candidate archives and parent sampling, automatic prompt / context optimization, multi-objective (Pareto) selection and credit assignment
  • Inference & serving: vLLM / TensorRT-LLM, MoE, KV / prompt caching, and the cost engineering that comes with it
  • Multilingual NLP
  • Publications (for PhD), strong competition results (ACM-ICPC / Kaggle / ML competitions), or notable open-source contributions
  • Taste for the honest number. We'd rather hire someone who says "this result isn't significant yet" than someone who ships a green dashboard. If you've ever killed your own experiment because the evidence didn't hold up, tell us about it.
  • e-Commerce or multilingual experience is a plus, not required

Job Information

The base salary range for this position in the selected city is $162000 - $387600 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

• 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;
• 3. Exercising sound judgment.

About TikTok

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us

Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.

We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

TikTok Accommodation

TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request

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