Staff Machine Learning Engineer, Content Visual AI

Role overview

A senior technical lead role focused on advancing computer vision and multimodal AI capabilities for a large-scale content platform. The position blends ML research, distributed systems engineering, and cross-functional leadership to build foundational representations and signals that power discovery, search, personalization, and recommendations. It is well suited for an experienced ML engineer who can set technical strategy while remaining hands-on with model development, infrastructure, and scientific exploration.

Responsibilities

- Define multi-quarter technical strategy for visual representation learning, multimodal content understanding, and LLM-driven personalization systems. - Drive ambiguous, high-impact scientific initiatives from problem framing and research strategy through experimentation, production deployment, and adoption. - Build reusable embeddings, semantic signals, and ML infrastructure, advancing techniques such as vision-language models, large language models, sequence modeling, self-supervised learning, retrieval, and distillation. - Partner with product, data science, applied science, and engineering teams to translate foundational capabilities into user-facing outcomes and to shape cross-functional roadmaps and investments. - Establish rigorous practices for modeling, data pipelines, distributed training, inference, evaluation, experimentation, and operational excellence while mentoring engineers and scientists. - Influence stakeholders on priorities, trade-offs, and execution plans across modeling, data, and productionization roadmaps.

Requirements

- Minimum 7 years of relevant industry experience, including at least 2 years leading technical teams. - MS or PhD in computer science, machine learning, or equivalent practical experience. - Publications at top machine learning, multimodal, or data mining venues (e.g., NeurIPS, ICML, CVPR, ECCV, ACL, KDD, SIGIR) or substantial open-source ML contributions. - Hands-on experience with large-scale distributed training of generative models such as LLMs, VLMs, or sequence models. - Hands-on experience with distributed tooling for ML data pipelines, for example Spark, Hive, or MapReduce. - Demonstrated ability to lead ambiguous ML and research efforts in building, applying, and improving generative AI models for content understanding, search, or recommendation systems. - Strong cross-functional communication, collaboration skills, and experience aligning stakeholders on technical priorities.

Nice to have

- Prior background in user understanding, recommender systems, or visual representation learning for content platforms. - Experience bridging research and production in fast-moving, high-impact ML environments.

Benefits and work setup

- Remote-eligible role open to candidates based anywhere in the USA. - Light in-person collaboration expectations, approximately 1-2 times per quarter. - Relocation assistance is not provided for this position. - Base salary range of $189,308 to $389,753 USD, plus equity eligibility; final compensation depends on location, experience, and relevant skills. - US-based applicants only.