> Markdown version of [/jobs/ext/2647486-staff-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/2647486-staff-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Engineer - **Company:** reddit Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Airflow, Data Analysis, Automation of Tests, BigQuery, Graph Database, Python (Programming Language), Machine Learning, Systems Development Life Cycle, Recommender Systems, Tensorflow, Data Processing, Google Cloud, Pytorch, Large Language Models, Kubernetes, Data Analytics, Apache Kafka, Machine Learning Operations - **Published:** August 19, 2026 - **Apply:** https://job-boards.greenhouse.io/reddit/jobs/8018513 ## About the Role * 7+ years of hands-on experience with the full lifecycle of designing, training, evaluating, testing, and deploying industry-level models. * Demonstrated Staff-level technical leadership: mentoring engineers, driving standards and bar raising, leading complex cross-functional projects: from requirements, design to cross-team/functional alignment and execution without direct people-management authority. * Excellent communication skills, with the ability to translate complex technical concepts to different audiences, both verbally and in writing. * Strong track record of working on content rich NLP/CV problems at scale, and using embeddings as a tool to solve them. * Established data-driven approach for ML system development. Excitement about working with data and readiness to look behind the metric numbers. * Familiarity with the Ads domain and/or Search/Recommender systems. * Experience with mainstream DL frameworks: PyTorch or TensorFlow. Preferred Qualifications: * Experience with our stack (Python, Pytorch, Airflow, BigQuery, Ray, k8s, kafka, GCP) * Tech leadership experience: mentoring junior engineers and leading complex projects. * Hands-on experience with using/fine-tuning/building LLMs. ## Description * Knowledge Graph Embeddings - Building representations for the Knowledge graph entities, e.g., intellectual properties/brands, to be used for high-precision targeting & business insights. * User Intent Modeling - Leveraging various techniques to introduce user representations based on the content they interact with: batch & real-time sequence modeling, LLM summarization, etc. * LLM-based Representations - Leveraging LLMs, VLMs, and foundational models to build complex representations of Reddit entities that improve ranking outcomes The signals and features we create become a key piece in the Ads Delivery funnel, from targeting to the auction, as well as the Business Insights product and other advertiser-facing products such as Creative generation and optimization. As a Staff ML Engineer, you'll be in charge of setting the technical direction of multiple pillars the team owns. You will lead cross-functional ML projects end to end - from high-level business gap analysis to engineering execution. Roughly 50% of your time will be spent on technical leadership and mentorship (driving strategy & designs, cross-functional collaboration, raising the quality bar), another 50% being individual hands-on work (data analysis & engineering, modeling & automation)., * Providing technical leadership and mentorship to MLEs in the team: driving designs & their review, establishing best practices in analysis, modeling and engineering, keeping the bar high. * Working closely with team/org leadership developing technical strategy for content-based embeddings & relevance for Ads. * Developing new or iterating on existing embedding models for advertising use cases, ranging from aggregation pipelines to two-tower architectures and sequence models. * Working with local and 3rd-party LLMs/VLMs: extract representations, develop evaluation methodologies, prompt tune and fine-tune large models to build state-of-the-art embeddings. * Building data processing and inference pipelines for the models we develop. * Qualitative and quantitative evaluation of the various features we develop, end-to-end experimentation from internal benchmarks to downstream recommender system offline metrics to online experiments. * Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management. * Participating in modeling and coding reviews: You will review work by other team members and provide feedback to ensure that it meets the team's standards for quality and performance. * Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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