Reward Modeling

Reward Modeling Engineer Jobs

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4d ago

Machine Learning Engineer - E-Commerce Recommendation Live

TikTok Seattle, WA

Seattle, WA

4d ago

$207k - $368k/yr Full time Data Pipeline Generative Ai Engineer Generative Recommendations

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Job Description

At TikTok, this Machine Learning Engineer role supports the end-to-end recommendation stack powering TikTok Shop LIVE. The work focuses on building and scaling recommendation systems that connect what people watch in live rooms with what they discover and buy, using foundation-model approaches across retrieval, ranking, reward modeling, and online optimization.

You will join the Global E-commerce Recommendation Live Algorithm team, which owns the full pipeline from retrieval and pre-ranking to ranking, mixed ranking, and long-term value modeling. The goal is to deliver consistent impact on GMV, buyer growth, and creator success across US and global markets, with a unified stack serving public discovery and private follower relationships.

Responsibilities

Scale Large Recommendation Models (LRM): Push the scaling frontier of a unified ranking foundation model, including model capacity, sparse MoE, ultra-long user sequences, pre-training, and teacher-student distillation. Translate offline scaling gains into online improvements under strict latency and compute budgets using dynamic compute allocation and on-device real-time inference.
Generative recommendation on multimodal Semantic IDs: Design semantic IDs that fuse visual, speech, text, collaborative, and product signals, including live context such as what is being showcased, pinned, or auctioned. Power generative retrieval with near-real-time SID serving.
LLM4Rec and MLLM cross-domain modeling: Use multimodal LLMs to place live rooms, short videos, and products into one semantic space. Inject world knowledge to transfer user interest across content, search, and shopping, and address cold start for new creators, new products, and new markets.
Reward models and post-training for recommenders: Build reward models for conversion, long-term value, and user experience. Align generative recommenders with SFT and DPO/GRPO-style RL post-training, generator-evaluator decoding, and listwise objectives.
Agent x Recommendation: Build LLM agents that auto-tune multi-objective value weights and traffic mechanisms. Design and read experiments, diagnose online cases, and move from hand-tuned heuristics toward agentic optimization and multi-step RL.
Long-term value and ecosystem: Model LTV, uplift, and multi-touch attribution. Design value and traffic mechanisms for live auctions, brands, and emerging creators while balancing short-term GMV with user experience and a healthy, growing creator ecosystem.
Own it end to end: Work across problem framing, data pipelines, large-scale training, online serving, A/B testing, and launch, partnering closely with engineering, product, and data science.

Requirements

Bachelor’s degree or above in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.
3+ years of industry experience in recommendation, search, advertising, or other large-scale applied machine learning, with a track record of shipped models that moved online metrics.
Strong machine learning fundamentals and hands-on depth in at least one area such as retrieval and ranking, sequential user modeling, multi-task learning, generative recommendation, or LLM/multimodal modeling.
Proficiency in Python and/or C++, plus solid experience with PyTorch or TensorFlow, including distributed training and model optimization.
Ability to turn ambiguous business problems into modeling solutions and to own iteration from offline experiments to online A/B results.

Technologies

Python
C++
PyTorch
TensorFlow

About the Team

Live commerce is described as a particularly challenging recommendation problem at TikTok.
The team owns the end-to-end recommendation stack for TikTok Shop LIVE, spanning retrieval and pre-ranking through ranking, mixed ranking, and long-term value modeling.
A unified stack supports seven core surfaces across public discovery and private follower relationships.
The team is rebuilding the system around foundation models, including large recommendation models, generative retrieval on multimodal semantic IDs, multimodal LLMs for content-commerce bridging, reward models with RL post-training aligned to long-term value, and AI agents that tune and evolve the system.
What the team ships is intended to directly drive GMV, buyer growth, and creator success for TikTok Shop in the US and globally.

Preferred Qualifications

Experience in e-commerce or live-streaming recommendation, ads ranking, or other real-time, conversion-driven systems.
Hands-on experience with large recommendation models or generative recommendation, including scaling laws, MoE, long-sequence modeling, pre-training, or distillation.
Experience with LLM/MLLM post-training (SFT, reward modeling, RLHF, DPO, GRPO, or other RL-based alignment), ideally applied to recommendation or ranking.
Experience building LLM agents (tool use, planning, automated experimentation, or parameter tuning) or applying reinforcement learning to multi-step decision-making such as traffic allocation.
Experience with cross-domain transfer, LTV/long-term value modeling, uplift modeling and causal inference, or multi-objective optimization.
Experience with large-scale training and inference efficiency (mixed precision, model parallelism, GPU serving optimization).
Publications at top venues such as NeurIPS, ICML, ICLR, KDD, SIGIR, WWW, RecSys, ACL, or CVPR, or strong results in major ML competitions.

Compensation and Location

Location: Seattle, WA (onsite)
Salary: USD 207,480 - 368,220 per year
Job code: A254886

Benefits

Day one access to medical, dental, and vision insurance
401(k) savings plan with company match
Paid parental leave
Short-term and long-term disability coverage
Life insurance
Wellbeing benefits
10 paid holidays per year
10 paid sick days per year
17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure)
Additional discretionary bonuses/incentives (eligible may apply)
Restricted stock units (eligible may apply)

Additional Information

Employees may be eligible for additional discretionary bonuses/incentives and restricted stock units; benefits may vary by employment nature and country work location.
For Los Angeles County (unincorporated) candidates: qualified applicants with arrest or conviction records will be considered in accordance with applicable laws, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Criminal history may relate to certain job duties and could result in withdrawal of a conditional offer. The role may involve interacting with internal/external clients or colleagues, handling confidential information, and exercising sound judgment.

Accommodation and Company Background

TikTok is committed to reasonable accommodations in recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs, or other reasons protected by applicable laws. Assistance can be requested at https://tinyurl.com/RA-request.
TikTok is described as a leading destination for short-form mobile video, with global headquarters in Los Angeles and Singapore, and offices in multiple other cities.

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1mo ago

Machine Learning Engineer, Human Centered AI - Evaluations & Insights

Apple Seattle, WA

Seattle, WA

1mo ago Closed

$142k - $263k/yr Full time Adversarial Ai Evaluation Ai Evaluation Artificial Intelligence

This position is no longer accepting applications (closed Sep 29, 2026).

Job Description

On Apple’s Human-Centered AI team, this role focuses on evaluating and improving Foundation Models and generative AI experiences with an emphasis on how people perceive quality. You will build evaluation frameworks and MLOps pipelines that turn model behavior into measurable insights, helping ensure AI systems are reliable, safe, and aligned with human expectations.

In this onsite position in Seattle, you will partner across engineering, research, product, and Responsible AI teams to refine model performance based on evaluation telemetry and human-centric measures.

Responsibilities

Architect and execute evaluation suites for LLMs and multimodal models, identifying edge cases across multi-step reasoning, factuality, adversarial robustness, safety, and alignment.
Build deterministic, heuristic, and LLM-assisted evaluation frameworks (including LLM-as-a-judge and reward modeling) to quantify human-perceived quality signals such as helpfulness and hallucination rates.
Convert qualitative failure modes into quantifiable loss patterns, programmatic guardrails, and actionable data-mixture adjustments for training and inference.
Collaborate with engineering teams to refine model behavior using evaluation telemetry to guide prompt engineering, Retrieval-Augmented Generation (RAG) strategies, and model fine-tuning.
Apply advanced ML techniques such as embedding-based clustering, representation learning, and perturbation analysis to map error taxonomies and latent failure manifolds.
Develop robust MLOps workflows to define evaluation metrics, automate regression testing across model checkpoints, and integrate human-centric assessments into ML CI/CD pipelines.
Architect scalable, distributed inference and processing pipelines (for example, Ray and vLLM) to support high-throughput evaluation, automated annotation, and output analysis.
Define quantitative evaluation frameworks that capture nuanced human factors, including trust calibration, conversational state tracking, and interpretability.
Build automated evaluation pipelines that use LLMs to assess outputs at scale and improve correlation with human baseline annotations.
Work with ML researchers, software developers, and product managers across Apple to translate product requirements into scalable, reliable, and efficient evaluation infrastructure.

Requirements

Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design.
5+ years of relevant industry experience in ML Engineering or Applied Research.
Advanced proficiency in Python and modern deep learning ecosystems including PyTorch, JAX, and Hugging Face.
Experience building scalable ML inference pipelines, model evaluation workflows, and structured rating frameworks for large-scale AI systems.
Ability to interpret unstructured model outputs (text, transcripts, embedding spaces) and synthesize qualitative findings into actionable engineering guidance and training objectives.
Hands-on experience developing, fine-tuning, or evaluating LLMs, multimodal models, and NLP systems.
Familiarity with AI quality metrics, hallucination detection techniques such as SelfCheckGPT, model alignment methods including RLHF and DPO, and LLM-as-a-judge frameworks such as G-Eval and DeepEval.
Experience building internal tools or automated pipelines for ML workflows using MLflow, Weights & Biases, or similar platforms.
Strong familiarity with prompt engineering, RAG architectures (vector databases, semantic search), and fine-tuning.
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Cognitive Science, or a related technical field.

Technologies

Python, PyTorch, JAX, Hugging Face
Ray, vLLM
MLflow, Weights & Biases
SelfCheckGPT, RLHF, DPO, G-Eval, DeepEval
LLM-as-a-judge, Retrieval-Augmented Generation (RAG)
Vector databases, semantic search

Benefits

Comprehensive medical and dental coverage.
Retirement benefits.
Range of discounted products and free services.
Reimbursement for certain educational expenses, including tuition, for formal education related to advancing your career.
Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs.
Eligible for discretionary restricted stock unit awards.
Option to purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan.
May be eligible for discretionary bonuses or commission payments and relocation.

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Reward Modeling Engineer Jobs

Machine Learning Engineer - E-Commerce Recommendation Live

Machine Learning Engineer, Human Centered AI - Evaluations & Insights

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