Senior Applied AI Engineer

Alembic•San Francisco, CA

11h•Onsite

About The Position

Alembic is an applied science company building GPU-resident distributed data systems that deliver 10–100x performance for Fortune 500 clients including NVIDIA and Delta. They are Series B ($145M raised), approximately 60 people, headquartered in San Francisco with a New York office and their SV11 compute facility. Their stack runs on a 256-petaflop NVIDIA DGX cluster with NVL72 GPU infrastructure, combining Spiking Neural Networks, Graph Neural Networks, and causal inference to deliver real-time analytics. The company is hiring a Senior Software Engineer onto their Applied AI team to build and extend the backend systems that power their platform. This is a hands-on role on a small team where work ships to production quickly and directly shapes what their largest customers see. The role involves working across Python-heavy backend services, data systems, and the infrastructure layer that connects them to their GPU-resident compute. The "AI" in Applied AI refers to the causal, graph-based, and neural systems their science team builds, and the engineer's job is to make them fast, reliable, and usable in production. This role is not for prompt engineering or LLM fine-tuning. It is not a spec-in, spec-out role; the engineer will operate with ambiguity, make calls on tradeoffs, and partner directly with senior engineers and leadership on what to build and how.

Requirements

5+ years of backend software engineering experience in production environments
Strong Python fundamentals and experience building and operating backend services
Demonstrated ability to work across adjacent parts of a stack (data, infrastructure, APIs) rather than staying in a narrow lane
Track record of shipping in fast-moving, ambiguous environments
Clear written and verbal communication — you can articulate tradeoffs, explain decisions, and collaborate across functions
Experience designing and operating distributed systems
Comfort with performance-sensitive code and systems where latency and throughput matter
Exposure to data-intensive applications — pipelines, storage systems, or analytical workloads

Nice To Haves

GPU or accelerator-adjacent engineering experience
Background in high-scale or high-performance computing environments
Experience partnering closely with applied science or research teams
Familiarity with causal inference or graph-based systems

Responsibilities

Build production backend services in Python — APIs, data services, and the glue between our compute layer and the products customers use
Work across the stack as needed — touch whatever part of the system the problem requires, from service code to data pipelines to integration layers
Ship iteratively against real customer needs — work directly with data products, science, and customer-facing teams to turn requirements into working systems
Own what you build — take responsibility for reliability, performance, and evolution of the services you stand up
Raise the bar for how we engineer — contribute to code quality, technical direction, and mentorship of earlier-career engineers