50 jobs for "Machine Learning Engineer - Multimodal Intelligence"

Responsibilities

Join the E-commerce Global Supply Chain and Logistics team at TikTok. We are building AI-native capabilities for global logistics, including logistics agents, address intelligence, context engineering, agent evaluation, and workflow automation for complex supply chain operations. This role is for candidates who want to apply LLMs, agents, reinforcement learning, retrieval systems, and software engineering to real logistics problems at global scale.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth.
Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities:
- Responsible for the development of large language models, agent systems, and related intelligent systems for the supply chain and logistics of the global E-Commerce business.
- Build and land domain LLM model capabilities for e-commerce supply chain and logistics, covering continued pre-training / CPT, SFT, preference optimization, reinforcement learning such as GRPO / PPO, reward or judge model design, model compression, inference cost and latency optimization, and business applications such as address correction, trajectory prediction, logistics cost analysis, customer service semantic understanding, and root-cause analysis.
- Develop multimodal and structured understanding capabilities for logistics and supply chain scenarios, focusing on unified modeling of text, numerical time series, events, product attributes, images, and documents to support scenario simulation, explainable prediction, and integration with existing forecasting or decision systems.
- Build core agent capabilities for team and business workflows, including AutoResearch for new solution exploration, Harness-based task decomposition and execution loops, RAG and knowledge retrieval, context understanding, skill / tool use, evidence grounding, and Clone & Adapt workflows for reusing proven solutions across markets and logistics scenarios.
- Design and improve agent architecture and engineering systems, including runtime orchestration, memory and state management, model / tool routing, permission-safe execution, observability, benchmark and Golden Set evaluation, badcase attribution, regression testing, online feedback loops, and continuous evolution mechanisms that improve context, skills, workflows, and model behavior over time.

Qualifications

Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD degree in in artificial intelligence, computer science, machine learning, natural language processing, data mining, software engineering or a related discipline.
- Experience with LLMs, agents, RAG, tool use, post-training, evaluation, or applied NLP systems, with the ability to connect model behavior to business and engineering requirements.
- Strong programming ability in Python and at least one production-oriented language such as Java, C++, Go, or TypeScript; comfortable building end-to-end systems, not only model experiments.
- Familiarity with machine learning and deep learning frameworks such as PyTorch, TensorFlow, JAX, vLLM, Hugging Face, LangChain, LlamaIndex, or similar ecosystems.
- Strong problem decomposition skills, data analysis capability, and communication skills; able to work with ambiguous business questions and convert them into executable technical plans.

Preferred Qualifications:
- Hands-on experience with coding agents, long-horizon agents, computer-use agents, agent harnesses, workflow orchestration, or automated evaluation frameworks.
- Experience with LLM post-training, including SFT, DPO, PPO, GRPO, RLHF, RLAIF, reward modeling, counterfactual data, or evidence-driven decision training.
- Experience building benchmarks from real production questions, including taxonomy design, golden answer construction, error attribution, and regression evaluation.
- Experience with logistics, e-commerce, operations research, data platforms, knowledge graphs, or enterprise knowledge management systems.
- Published papers or strong open-source work in LLMs, agents, NLP, data mining, machine learning systems, evaluation, or AI engineering.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $153900 - $300960 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.

Responsibilities

Qualifications

Qualifications

Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD degree in in artificial intelligence, computer science, machine learning, natural language processing, data mining, software engineering or a related discipline.
- Experience with LLMs, agents, RAG, tool use, post-training, evaluation, or applied NLP systems, with the ability to connect model behavior to business and engineering requirements.
- Strong programming ability in Python and at least one production-oriented language such as Java, C++, Go, or TypeScript; comfortable building end-to-end systems, not only model experiments.
- Familiarity with machine learning and deep learning frameworks such as PyTorch, TensorFlow, JAX, vLLM, Hugging Face, LangChain, LlamaIndex, or similar ecosystems.
- Strong problem decomposition skills, data analysis capability, and communication skills; able to work with ambiguous business questions and convert them into executable technical plans.

Preferred Qualifications:
- Hands-on experience with coding agents, long-horizon agents, computer-use agents, agent harnesses, workflow orchestration, or automated evaluation frameworks.
- Experience with LLM post-training, including SFT, DPO, PPO, GRPO, RLHF, RLAIF, reward modeling, counterfactual data, or evidence-driven decision training.
- Experience building benchmarks from real production questions, including taxonomy design, golden answer construction, error attribution, and regression evaluation.
- Experience with logistics, e-commerce, operations research, data platforms, knowledge graphs, or enterprise knowledge management systems.
- Published papers or strong open-source work in LLMs, agents, NLP, data mining, machine learning systems, evaluation, or AI engineering.

About TikTok

TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.

Size: 5,001 - 10,000 employees
Industry: Entertainment & Gaming

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