Senior Backend Engineer: Machine Learning Infrastructure

As a Senior Backend Engineer, you’ll help build the infrastructure that enables machine learning teams to develop, deploy, and operate models reliably at scale.
You’ll own critical backend services, data pipelines, and platform capabilities supporting workloads ranging from traditional machine learning to LLMs.
The role combines distributed systems engineering, cloud infrastructure, API development, observability, and platform design.
You’ll work closely with ML and product teams to understand their needs and turn them into reusable, self-service infrastructure.
You’ll have significant autonomy to make technical decisions, evaluate trade-offs, and take solutions from design through continuous improvement.
The environment is remote-first, collaborative, and strongly oriented toward ownership, thoughtful engineering, and modern AI-assisted development.
This is an opportunity to have a direct impact on how teams build and operate machine learning products at scale.

• 5+ years of professional experience in backend engineering, platform engineering, or a closely related discipline.
• Extensive professional experience with Python, the primary programming language used in the environment.
• Hands-on experience developing and operating software on a public cloud platform such as AWS, GCP, or Azure, or working with self-managed Kubernetes; experience with AWS is particularly relevant.
• Strong experience designing and building distributed, high-load services and APIs.
• Solid understanding of data structures, algorithms, and the trade-offs involved in selecting and implementing them.
• Strong system-design mindset, with the ability to design solutions before implementation and understand the architectural implications of technical decisions.
• Experience working effectively with modern AI-assisted coding and development tools, combined with the judgment to understand their appropriate use and limitations.
• High level of ownership, initiative, and accountability, with a proactive approach to identifying and solving problems.
• Strong communication and collaboration skills, with a friendly and supportive approach to working with teammates and cross-functional partners.
• Experience with Rust, C, C++, or Go is a strong advantage.
• Previous experience developing or contributing to ML platforms or machine learning infrastructure is highly valued.
• Experience setting up and operating vector databases such as Qdrant, Milvus, Weaviate, OpenSearch, or pgvector is a plus.
• Experience with model serving or inference infrastructure, including LLM workloads, is advantageous.
• Experience with Infrastructure as Code tools such as Terraform is a plus.