Senior / Staff Software Engineer, Full Stack Software Products

Antora Energy

Remote

Full Time

Posted August 19, 2026

$114k - $174k· Est.

Energy Management

Remote

Apply Now

Application opens on company website

Listing evidence and provenance

Source: Greenhouse View original listing
Freshness: Open in the current public read path; this is not an employer guarantee.Last observed October 5, 2026
Classification: Modern power-system classification not yet published for this listing.Legacy source industry field: Energy Management
Pay provenance: Estimated benchmark; not employer-provided

Job Description

Design, build, deploy, and maintain internal and external software products across the frontend, backend, data, and AI layers.

Key Responsibilities

Antora Energy is looking for a full-stack product engineer to build the software products that power our business - from external customer experiences to internal tools used by teams across Business Development, Operations, and R&D.
In this role, you’ll help shape product vision, design the underlying data models, and connect AI systems with the business logic that makes them useful. You’ll own products end to end, working directly with users to understand their workflows, translate ambiguous needs into practical solutions, and ship software that performs reliably in production.
You’ll also help build Antora’s long-term software capabilities. That means creating systems, standards, and guardrails that allow partner teams to safely modify existing tools and build new ones within our ecosystem - without engineering becoming a bottleneck.
This is a highly autonomous role with direct influence over how software is designed and built at Antora. We’re looking for deep expertise in one or more core areas - such as software engineering, data modeling, AI integration, or product development - along with the curiosity and practical judgment to work across the full stack.

Requirements

Strong product judgment. You can turn vague, shifting requests into a clear product direction, write an effective specification, and ship a solution. You know how to challenge a feature request that misses the underlying problem while maintaining stakeholder trust.
Production software engineering experience. You have strong Python and TypeScript skills and are comfortable working across frontend and backend systems. You have owned applications end to end, including deployment, observability, reliability, and maintenance.
Experience shipping AI-powered products. You have built and deployed LLM-powered systems used by real users - and have learned from the unexpected ways those systems failed in production.
Strong data fundamentals. You are comfortable with SQL, warehouse modeling, and tools such as dbt or equivalent transformation frameworks. You have shipped data changes through CI and think proactively about data quality, freshness, lineage, and testing.
A collaborative approach to building. You have a track record of building with non-engineers, not simply building for them. You’re comfortable pairing, teaching, reviewing someone’s first pull request, and helping users become increasingly self-sufficient.
Clear cross-functional communication. You can connect product, data, AI, and software engineering considerations - and communicate technical decisions in a way that allows everyone in the room to contribute.
Practical risk judgment. You can distinguish between controls that prevent meaningful failures and ceremony that slows teams down. You know where users can safely make changes independently and where review or stronger governance is required.
A growth mindset. We do not expect one person to be an expert in every discipline. We value deep expertise, intellectual curiosity, sound judgment, and a willingness to learn across the stack.
Experience building or maintaining semantic layers using tools such as dbt or Cube
Familiarity with ClickHouse, time-series data, or IoT systems
Experience with agent protocols such as MCP or with plugin and tool APIs
Background in energy, industrial technology, or manufacturing
Experience managing AI model usage, performance, and cost governance