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Fundamental

Predictive analytics platform for structured data

Backend Engineer - Extensions

Backend EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listedRemote

About the Job

Requirements

  • At least 5 years of backend engineering experience, with a strong focus on Python.
  • Strong Python skills, including writing clean, idiomatic, well-tested code and deep knowledge of the language.
  • Experience designing and operating distributed systems at production scale.
  • Strong understanding of concurrency, task execution, and workflow orchestration patterns.
  • Experience building systems that are reproducible, observable, and easy to debug in production.
  • Ability to work closely with non-engineering counterparts and translate complex requirements into reliable implementations.
  • Ability to drive work end to end and focus on outcomes.

Responsibilities

  • Own the reliability, performance, and scalability of Extensions capabilities end to end.
  • Design and build distributed workflows that are efficient, reproducible, and observable in production.
  • Collaborate closely with Data Scientists on the team to understand requirements and translate them into engineering decisions.
  • Identify and resolve performance bottlenecks to ensure Extensions capabilities meet the latency and throughput demands of enterprise deployments.
  • Contribute to architectural decisions about how Extensions integrates with the broader NEXUS backend.
  • Write clean, well-tested Python code and uphold a high standard for code quality across the team.
  • Contribute to internal engineering documentation and help establish best practices for backend development within the team.

Desired Qualifications

  • Hands-on experience with workflow orchestration systems such as Temporal, Dramatiq, Celery, or equivalent.
  • Familiarity with machine learning infrastructure or experience working alongside data science teams.
  • Experience with high-throughput data processing pipelines.
  • Exposure to Kubernetes and container-based deployment environments.
  • Background in performance profiling and optimization of Python services.

About the Company

Fundamental

Predictive analytics platform for structured data

Fundamental offers an enterprise-grade predictive intelligence product called NEXUS, a Large Tabular Model that analyzes structured data from spreadsheets, databases, and CRM systems. It learns patterns directly from raw tabular data, eliminating the need for extensive feature engineering, and combines forecasting, classification, anomaly detection, and scenario simulation in one model. NEXUS is deployed through the AWS dashboard via a strategic partnership and was trained on Amazon SageMaker HyperPod to handle datasets with billions of rows, serving industries like financial services, healthcare, retail, and energy with seven-figure contracts for use cases like demand forecasting, price prediction, and churn analysis. The goal is to turn raw data into actionable insights that improve forecasting accuracy, reduce risk, and optimize business outcomes, setting Fundamental apart by focusing on structured data rather than unstructured text or images.

Company size
51-200
Company stage
N/A
Total funding
$255M
Headquarters
San Francisco, CA
Founded
2024

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