Full-Stack Engineer — Temporal AI Platform

Abel AI; Northern (KY); Hybrid; USD 110,000 - 160,000; Full time

Job summary

Abel AI in the United States is seeking a Full-Stack Engineer to build a platform for large-scale temporal foundation models. The role spans backend systems, data infrastructure, cloud services, and product engineering to enable ingestion, model inference, forecasting, and results delivery.

You will work with researchers to turn new temporal capability into robust production features, designing APIs and infrastructure for reliable end-to-end systems in a fast-moving environment.

Qualifications

• Build production-grade backend APIs and services.
• Design systems to handle large datasets with reliable performance.
• Collaborate across ML, data, and frontend teams to ship features.

Responsibilities

• Build the core temporal AI platform—from data ingestion to model inference and results delivery.
• Create backend systems connecting large temporal models with data pipelines, APIs, and apps.
• Develop scalable, low-latency inference services for forecasting and model workloads.
• Build data-intensive infrastructure for time-series, tabular, and structured data.
• Deploy, monitor, and operate production cloud workloads with reliability.
• Collaborate with ML researchers to translate capabilities into production features.
• Ship end-to-end systems across backend, data, ML infra, and deployment.

Skills

Backend API design

Distributed systems

Frontend development

Cloud services

ML system familiarity

Data handling (time-series)

Agile collaboration

Production-grade software

Tools

FastAPI

Flask

Django

Node.js

Docker

Kubernetes

Terraform

CI/CD

Observability tools

Job description

Build the platform that brings large-scale temporal foundation models into real-world use.

About the role

We’re looking for an exceptional Full-Stack Engineer to build the platform that brings large-scale temporal foundation models into real-world use.

Our mission is to build temporal superintelligence: AI systems that learn from observations across time, understand temporal dynamics, forecast what comes next, and support better decisions about the future.

You’ll work at the intersection of ML systems, data infrastructure, cloud services, and product engineering, building the systems that allow users to work with large temporal foundation models—from ingesting and managing data to running model inference and analyzing forecasting results.

This is an end-to-end engineering role. You’ll work across backend systems, data infrastructure, inference infrastructure, and cloud operations, collaborating closely with researchers to turn new model capabilities into reliable production systems.

What you’ll work on

• Build the core temporal AI platform. Develop the systems that put temporal foundation models into users’ hands—from data ingestion and management through model inference, forecasting, and results delivery.
• Build infrastructure for large foundation models. Design backend systems that connect large temporal models with data pipelines, model-serving infrastructure, APIs, and production applications.
• Develop scalable inference systems. Build reliable, low-latency services for running forecasting and foundation-model inference across different datasets, models, horizons, and users.
• Build data-intensive systems. Develop infrastructure for ingesting, storing, querying, transforming, and processing large-scale time-series, tabular, and structured datasets.
• Build and operate cloud infrastructure. Deploy, monitor, and operate production services and ML workloads in the cloud, with attention to reliability, observability, scalability, and cost.
• Work closely with ML researchers. Translate new temporal foundation-model capabilities into production features and design the APIs, services, and infrastructure needed to expose them reliably.
• Ship end-to-end systems. Work across backend, data, ML infrastructure, and deployment rather than being constrained to a single layer of the stack.

What we look for

• Strong software engineering fundamentals and experience building production systems.
• Experience building backend services and APIs using frameworks such as FastAPI, Flask, Django, Node.js, or similar technologies.
• Experience designing systems around large datasets, including data ingestion, storage, querying, and transformation.
• Familiarity with cloud infrastructure and operations, including services on AWS, GCP, or Azure, containers, deployment, monitoring, and production debugging.
• Experience working with databases and data systems such as Postgres, object storage, data warehouses, caches, or analytical databases.
• Comfort working close to ML systems. You don’t need to be an ML researcher, but you should understand model inputs, outputs, inference workflows, latency and throughput constraints, and common model failure modes well enough to build reliable systems around them.
• Ability to work with ML engineers and researchers to turn experimental models and research code into robust production services.
• Strong product instincts—you think about the complete user workflow rather than simply implementing individual tickets.
• Comfort working in a fast-moving environment where research capabilities, infrastructure, and product requirements evolve quickly.
• Experience building ML platforms, developer tools, data platforms, analytics products, or AI applications.
• Experience deploying or serving large deep-learning or foundation models.
• Familiarity with GPU infrastructure, model serving, batching, asynchronous inference, job queues, caching, or distributed inference.
• Experience operating cloud infrastructure using technologies such as Docker, Kubernetes, Terraform, CI/CD, and observability platforms.
• Experience working with time-series, tabular, or other structured data.
• Familiarity with modern ML frameworks such as PyTorch and the practical requirements of running ML models in production.
• Experience building infrastructure for data processing, forecasting, experimentation, or model evaluation.
• Experience with LLM or agentic applications, including tool use, retrieval, streaming APIs, or long-running asynchronous workflows.