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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Systems Engineer - **Company:** reddit Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $292,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Airflow, BigQuery, Data Infrastructure, Data Systems, Distributed Data Store, Distributed Systems, Machine Learning, Azure Machine Learning, Snowflake, Apache Spark, Backend, Kubernetes, Apache Flink, Apache Kafka, Data Management, Machine Learning Operations, Data Pipelines, Databricks - **Published:** August 25, 2026 - **Apply:** https://www.workingnomads.com/job/go/1815950/ ## About the Role This is a senior technical leadership role for someone who can combine deep systems expertise, production ML experience, architectural judgment, and cross-team influence., * You have 8+ years of experience in infrastructure, distributed systems, ML platforms, data platforms, or large-scale backend systems. * You have 4+ years building or operating production ML infrastructure, feature platforms, training data systems, experimentation systems, or large-scale data pipelines. * You have led broad, ambiguous, multi-team platform initiatives from strategy through adoption. * You have built platforms used directly by ML engineers, data scientists, or product teams developing production ML systems. * You have deep experience in ML platform, feature platform, training data, experimentation, developer infrastructure, or distributed data infrastructure. * You have worked with distributed data and compute systems such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, Databricks, or similar technologies. * You can balance urgent customer needs with durable long-term architecture and reusable platform patterns. * You influence senior engineers and leaders through clear technical reasoning, RFCs, design reviews, decision frameworks, and operating mechanisms. * You are excited to shape how production ML systems are built, scaled, and operated, not only how models are trained. ## Description We are looking for a Senior Staff Machine Learning Systems Engineer to lead the technical strategy for the end-to-end Ads ML engineer lifecycle. The initial focus will be on the feature development and training iteration loop: making it faster and easier for ML engineers to build features, generate reliable training data, run experiments, and move from idea to validated model improvement. Over time, this scope will expand into serving and online experimentation workflows, creating a more seamless path from offline iteration to production impact., * Own the technical strategy for the end-to-end Ads ML engineer lifecycle, starting with feature development, training data, offline experimentation, and model iteration workflows. * Align Ads ML platform priorities with Reddit's broader ML Platform vision, translating Ads pain points into reusable platform capabilities where appropriate. * Define architecture and technical standards for ML feature and training-data systems across batch/streaming computation, backfills, lineage, quality, observability, and online/offline consistency. * Stay close to ML engineers and platform customers to identify high-leverage friction points and improve day-to-day development velocity. * Build platform abstractions and workflow automation that make ML development faster, safer, more reliable, and more self-service. * Over time, extend the platform strategy into serving and online experimentation workflows, creating a more seamless offline-to-online ML development experience. * Partner across Ads, ML Platform, Data Platform, modeling, product, and engineering teams to clarify ownership, resolve ambiguity, and drive durable execution. * Mentor Staff and senior engineers, raise the architecture and operational bar, and help grow the next generation of technical leaders. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Making Data Warehouses fast. A developer's story.](https://www.wearedevelopers.com/videos/302-making-data-warehouses-fast-a-developer-s-story) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)