Senior Backend Engineer: Machine Learning Infrastructure

Accountabilities

• Design, build, deploy, and operate high-load distributed backend services and APIs that support machine learning infrastructure.
• Take end-to-end ownership of core ML services and associated data pipelines, from system design and implementation through deployment, observability, maintenance, and continuous improvement.
• Build reliable, scalable, and reusable infrastructure components that make machine learning workloads easier for product and ML teams to run and operate.
• Partner closely with ML and product engineers to understand their requirements and translate them into effective platform capabilities and services.
• Make and communicate technical decisions by evaluating architectural options, trade-offs, scalability, reliability, and operational requirements.
• Maintain a high standard for service reliability, performance, observability, and maintainability.
• Proactively identify technical and operational problems and take ownership of resolving them rather than allowing issues to remain unaddressed.
• Use modern AI-assisted development tools thoughtfully while maintaining strong ownership of system design, engineering decisions, and code quality.
• Contribute to a collaborative engineering culture by sharing knowledge, supporting teammates, and helping others solve technical challenges.

Requirements

• 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.

Benefits

• Fully remote working environment, allowing you to choose where you live.
• Unlimited vacation time, with employees strongly encouraged to take at least three weeks of vacation each year.
• Home-office stipend to help you create a productive and comfortable remote workspace.
• Apple laptop provided for new employees.
• Annual training and professional development budget.
• Maternity and paternity leave for eligible employees.
• Competitive base salary of $80,000–$120,000 USD, depending on knowledge, skills, experience, and interview results.
• Stock options offered in addition to the base salary.
• Regular team offsites providing opportunities for collaboration and connection.
• Opportunity to work with experienced colleagues and contribute to meaningful, technically challenging projects.