13 часов назад
Machine Learning Engineer — AI Safety & Assurance
136 800 - 359 720$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Machine Learning Engineer — AI Safety & Assurance (AI Safety/LLM/Recommendation Systems): Building automated safety evaluation, governance, monitoring, and mitigation infrastructure for TikTok’s recommendation and General Purpose AI systems with an accent on model interpretability, adversarial robustness, and compliance. Focus on designing high-throughput evaluation engines, low-latency real-time safety filters, and observable, auditable ML pipelines.
Location: San Jose, United States; fully in-person schedule up to 5 days a week
Salary: $136,800–$359,720 annually
Company
operates data privacy, cybersecurity, trust and safety, and AI governance infrastructure for TikTok’s U.S. apps, user data, and recommendation systems.
What you will do
- Build automated safety evaluation engines for LLM and recommendation model outputs.
- Develop monitoring frameworks to identify adversarial vulnerabilities, bias, toxicity, misinformation, and other model risks.
- Architect governance-as-code metadata frameworks and control planes for ML pipelines, training data, and model weights.
- Create low-latency safety filters and circuit breakers for real-time recommendation streams.
- Build observability and lineage platforms that make model decisions and algorithmic promotions traceable, auditable, and compliant.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field.
- At least 2 years of production-level software development experience with Go, Python, or C++.
- Experience with distributed systems and high-volume ML data pipelines such as Spark, Flink, or Kafka.
- Strong foundations in data structures, algorithms, and machine learning, including deep learning, optimization, and evaluation metrics.
Nice to have
- Experience in AI alignment, model interpretability, explainable AI, or adversarial machine learning.
- Familiarity with LLM evaluation frameworks, safety fine-tuning, RLHF, DPO, or prompt-injection defense.
- Knowledge of content recommendation systems, including two-tower models, deep ranking, or reinforcement learning.
- Experience with Go for high-performance backend infrastructure and Python/PyTorch for ML modeling and research.
- Ability to translate legal and safety policies into scalable technical implementations.
Culture & Benefits
- On-site collaboration focused on speed, alignment, team development, and integrated execution.
- Medical, dental, and vision insurance from day one.
- 401(k) savings plan with company match, paid parental leave, disability coverage, and life insurance.
- Wellbeing benefits, 10 paid holidays, 10 paid sick days, and 17 days of paid personal time.
- Inclusive workplace with reasonable accommodations available during recruitment.
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