Machine Learning Engineer - Remote

Overview

You’ll be at the forefront of pioneering AI applications purpose-built for the modern news landscape. With access to an expansive dataset of 60,000+ articles ingested daily from over 50,000 global sources, you’ll push the boundaries of what Large Language Models can do, building features that enable our users to see every side of the story and think freely.

In Short

• Own and maintain production pipelines that process hundreds of thousands of news articles daily.
• Curate and catalog news content meticulously for use in downstream ML applications.
• Build state-of-the-art ML solutions to power features such as article clustering and content recommendations.
• Design and build robust evaluation datasets to benchmark model performance.
• Help manage dozens of microservices written and maintained by our ML team.
• Lead deployment processes, including blue-green deployments and rollbacks against real-time production data streams.

Requirements

• Proven hands-on experience in developing, deploying, and maintaining ML pipelines, including recommendation engines, clustering, and full MLOps workflows.
• Strong familiarity with infrastructure-as-code tools (Terraform) and orchestration with Kubernetes on AWS or GCP.
• Experience with data management through ElasticSearch and Postgres.
• Professional experience as an MLOps or DevOps engineer, with strong foundations in Python and cloud-native tooling (AWS or GCP, Kubernetes).
• 3-6 years of applied, hands-on experience in ML at scale, with end-to-end ownership across deployment, maintenance, and iteration.
• Expertise in autoscaling Kubernetes workloads using tools like KEDA and SQS.
• Proficiency in Python (required), Go (nice-to-have), and Node.js.
• Hands-on experience with LLM (OpenAI, Gemini, Llama3), including prompt engineering and local deployment frameworks like vLLM.
• Experience building semantic search pipelines, including information retrieval and document indexing via PGVector or equivalent tools.

Benefits

• Fast-paced, mission-driven environment where experimentation is encouraged.
• Autonomy to shape strategy and support from a collaborative team.
• Satisfaction of building a product that helps people become more informed and open-minded.
• Remote work flexibility.
• Competitive compensation reflecting market benchmarks and internal equity.