Atlas is building an AI-native creative platform for professional 3D and game-production workflows. Our product combines an agentic canvas, generative models, reusable workflows, and integrations designed to help creators move from ideas and references to usable 3D outputs. Atlas is built for real production workflows rather than isolated AI demonstrations.
We are looking for a highly capable generalist software engineer who is comfortable working across product, backend, frontend, infrastructure, AI systems, and applied experimentation.
We do not organize engineering around rigid frontend or backend boundaries. Modern AI-assisted development increasingly shifts the bottleneck away from manually writing every line of code and toward defining the right problem, establishing clear constraints, designing reliable systems, and validating outcomes. You will be expected not only to ship features, but also to improve how features are specified, generated, reviewed, tested, and maintained.
- Own complex product and engineering problems from initial definition through production deployment.
- Work across frontend, backend, infrastructure, data, and AI integrations as the problem requires.
- Translate ambiguous product goals into precise specifications, technical invariants, acceptance criteria, and testable system behavior.
- Use coding agents and large language models extensively to explore solutions, generate implementations, analyze unfamiliar systems, and accelerate experimentation.
- Review AI-generated and human-written code critically rather than treating generated code as inherently correct.
- Design clear boundaries around what a change may and may not affect, particularly in sensitive systems such as billing, permissions, usage limits, and distributed workflows.
- Build automation, tooling, evaluation systems, and reusable development patterns that improve the productivity of the entire engineering team.
- Run rapid experiments in applied AI and computer science, evaluate results rigorously, and turn successful experiments into reliable product capabilities.
- Identify recurring development bottlenecks and solve the underlying meta-problem instead of repeatedly addressing the same class of issue manually.
- Collaborate directly with product, design, machine-learning, and customer-facing teams.
- Strong computer-science and software-engineering fundamentals.
- Demonstrated ability to understand and modify complex systems beyond one narrow technical layer.
- Excellent problem decomposition and systems-design skills.
- Experience shipping reliable production software and taking responsibility for its behavior after release.
- Practical experience using AI coding agents or LLMs as part of a serious engineering workflow.
- Ability to define invariants, interfaces, failure states, edge cases, and validation strategies before implementation.
- Comfort moving between experimentation and production engineering.
- Strong judgment about when to reuse, automate, simplify, redesign, or deliberately constrain a system.
- Clear written communication and the ability to challenge assumptions constructively.
- Experience with agentic systems, model APIs, evaluation pipelines, or applied machine learning.
- Experience with distributed systems, billing, cloud infrastructure, developer tooling, or production observability.
- Familiarity with 3D, games, graphics, creative software, or node-based workflow systems.
- Evidence of building internal tools or automation that improved how an engineering organization operates.
- Personal experiments, open-source work, research projects, or competition results demonstrating unusual technical curiosity and initiative.
We expect engineers to use AI aggressively, but not uncritically. A successful engineer at Atlas does not simply ask an agent to generate a large pull request. They first establish the intended behavior, constraints, affected systems, prohibited changes, and validation criteria—then use AI to accelerate implementation within those boundaries.