Senior Infrastructure Engineer (Backend/Data Performance)

Harnham is a mission-driven AI research organization operating at the cutting edge of large-scale data infrastructure and machine learning. As a Senior Infrastructure Engineer, you'll be responsible for designing and optimizing core data pipelines and backend systems that power advanced AI research at scale.

Responsibilities:

• Design and optimize high-performance data pipelines for distributed training and large-scale storage using tools such as Arrow, DuckDB, LanceDB, BigQuery, and vector databases
• Drive low-level performance optimization across the stack — latency, throughput, GPU utilization, and reliability
• Build and maintain monitoring and observability tooling for data quality, pipeline performance, and experiment tracking
• Optimize distributed AI workloads for efficiency and scale across cloud infrastructure (GCP-primary)
• Architect public-facing data infrastructure capable of serving large, heterogeneous, multimodal datasets to a global research community
• Scope and supervise projects so that interns, PhD students, and post-docs can contribute effectively
• Set engineering standards and best practices across the infrastructure function
• Support technical hiring and help shape the growth of the engineering team
• Act as a bridge between research and engineering — translating prototype workflows into production-grade systems

Requirements:

• 5+ years of backend or infrastructure engineering experience
• Strong Python programming skills (Go is a strong plus; C++, Rust, or CUDA is a bonus)
• Proven experience building and supporting ML/AI infrastructure in production environments
• Hands-on experience with containerization and IaC — Docker, Kubernetes, Terraform
• Experience with cloud platforms (GCP preferred, AWS or Azure also considered)
• Proficiency with high-performance data tools such as DuckDB, Apache Spark, or Delta Lake
• Experience with distributed systems and large-scale data storage
• A backend-first, performance-obsessed mindset
• Experience mentoring junior engineers or researchers and breaking down complex technical problems
• GPU orchestration and large-scale model training experience
• HPC infrastructure experience (Slurm, K8s clusters)
• Familiarity with ML platforms (Vertex AI, SageMaker) and frameworks (PyTorch, JAX)
• Monitoring stack experience (Prometheus, Grafana)
• Background in multimodal, audio, or large-scale scientific data
• Full-stack exposure (React or similar) sufficient to guide others