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Ai Source Code Generation Jobs (NOW HIRING)

Code Metal

Senior Software Engineer - Agentic AI

Boston, MA · Hybrid

$133K - $175K/yr

At Code Metal, we are pioneering a new generation of AI-enabled transpilation tools that bridge the gap between algorithm development and deployment to embedded systems. As a Senior Software Engineer ...

Figma

Software Engineer, Code Platform

San Francisco, CA · On-site +1

From LLM-powered code generation to agentic design-to-code workflows, we're integrating AI deeply into how developers experience Figma. Our work powers products such as Make, Prompt to Edit, MCP, and ...

AMD

Fellow AI Compiler Engineer

San Jose, CA · On-site

... new code generation infrastructure, potentially including custom LLVM components for new or ... company in the open-source LLVM community, contributing patches, participating in design ...

SEACORP

Software Engineer IV - AI

Middletown, RI

... support source control, issue tracking, code reviews, sprint execution, and software delivery workflows. * Support AI-assisted code generation, debugging, test generation, refactoring, and ...

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Ai Source Code Generation information

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How much do ai source code generation jobs pay per year?

As of Jun 5, 2026, the average yearly pay for ai source code generation in the United States is $93,198.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,500.00 and $144,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI Source Code Generation Engineer, and why are they important?

To thrive as an AI Source Code Generation Engineer, you need strong programming expertise, knowledge of machine learning concepts, and experience with AI model development, often supported by a degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, version control systems such as Git, and experience using code generation APIs or tools are typically required. Creativity, problem-solving, and effective communication are crucial soft skills for translating requirements into robust AI-generated code solutions. These skills and qualifications are essential for developing accurate, maintainable, and innovative AI-driven code generation systems that meet user and business needs.

What is AI source code generation?

AI source code generation is the use of artificial intelligence, particularly machine learning models like large language models, to automatically generate computer program source code based on user input, specifications, or natural language instructions. This technology helps developers write code faster, reduce repetitive tasks, and can even assist non-programmers in creating simple applications. AI source code generation tools can support multiple programming languages and frameworks, making them versatile for a wide range of software development tasks. However, the generated code still typically requires human review to ensure quality, correctness, and security.

What is the difference between Ai Source Code Generation vs AI Software Developer?

AspectAi Source Code GenerationAI Software Developer
Required CredentialsKnowledge of AI models, programming, and data scienceComputer science degree, programming skills, experience with AI/ML
Work EnvironmentTools for AI model training, code generation platforms, cloud servicesSoftware development teams, IDEs, version control systems
Employer & Industry UsageTech companies, AI startups, research institutionsSoftware firms, tech companies, enterprise IT departments
Search & Comparison IntentUnderstanding AI-driven code tools, automation in codingDeveloping AI applications, coding best practices

Ai Source Code Generation focuses on using AI models to automatically generate code, streamlining development processes. AI Software Developers design, build, and maintain AI-powered applications, requiring programming expertise and AI knowledge. While both roles involve AI and coding, source code generation emphasizes automation tools, whereas AI software development involves creating AI solutions from scratch.

What are the typical collaborative processes between AI source code generation engineers and other development team members?

AI source code generation engineers often work closely with software developers, product managers, and data scientists to ensure that generated code aligns with project requirements and integrates smoothly with existing systems. Collaboration typically involves participating in sprint planning, code reviews, and regular stand-up meetings to discuss progress and address challenges. Open communication and feedback loops are essential, as engineers may need to adjust AI models based on user feedback and team input. This collaborative environment enhances code quality and accelerates the development lifecycle.
Infographic showing various Ai Source Code Generation job openings in the United States as of May 2026, with employment types broken down into 76% Full Time, 23% Part Time, and 1% Contract. Highlights an 92% Physical, 2% Hybrid, and 6% Remote job distribution, with an average salary of $93,198 per year, or $44.8 per hour.

Member of Technical Staff - Code Generation

Moonlake AI

San Francisco, CA • On-site

Full-time

Posted 27 days ago

Job description

Introducing Moonlake, AI for creating world simulations.
Overview
Moonlake is building the frontier of interactive world models: systems that generate, simulate, and reason over 3D environments for embodied AI, robotics and gaming. We develop the simulation infrastructure to build worlds (e.g., assets, scenes, digital twins) at scale.
Our team sits at the intersection of:
  • Embodied AI
  • Robotics simulation
  • Interactive 3D worlds
  • World models
  • Real-time generation
  • AI infrastructure

Moonlake is building the next generation of AI infrastructure for interactive digital worlds. Our mission is to enable anyone to create, simulate, and interact with rich environments using natural language and multimodal inputs, turning simple ideas into worlds with structure, logic, and agents that can perceive and act.
Our team has raised $28M in seed funding from NVIDIA Ventures, Threshold Ventures, AIX ventures and notable angels including Naval Ravikant and Jeff Dean to build the foundational layer for the future of AI - powering everything from creative tools and games to robotics training, simulations, and digital twins. Our goal is to make building and experimenting with these environments as accessible and scalable as publishing video on the internet.
We are looking for exceptional research engineers and applied researchers to help push the frontier of interactive AI.
What You'll Do
Depending on your strengths and interests, your work may include:
World Modeling
  • Train models that generate structured interactive environments
  • Improve multimodal reasoning across vision, language, and simulation
Code Generation Agents
  • Build agent systems that generate game logic and world structures
  • Design tool-using reasoning systems for interactive content generation
  • Work on post-training and fine-tuning of code generation models to improve reasoning, tool use, and environment construction
Diffusion Rendering
  • Develop real-time diffusion renderers for stylized game visuals
  • Improve performance and controllability of video diffusion models
Systems & Infrastructure
  • Build high-performance training and inference pipelines
  • Optimize large-scale training and distributed systems
Simulation & Embodied Intelligence
  • Build environments for training embodied agents
  • Explore reinforcement learning and multimodal reasoning
What We're Looking For
Strong Signals
  • PhD in Computer Science, Machine Learning, Robotics, Graphics, or related field
  • Experience working at frontier research labs or leading AI research groups
  • Strong publication record or impactful open-source contributions
Technical Skills
  • Deep learning frameworks (PyTorch, JAX, etc.)
  • Experience with large-scale training or distributed systems
  • Strong coding ability in Python/C++/CUDA
  • Experience fine-tuning or post-training large models (especially code generation or multimodal models)
Ideal Candidates
  • Researchers who want to move quickly from research to product
  • Engineers who enjoy building new systems from scratch
  • People excited about the intersection of AI + simulation + interactive worlds

We are committed to being an on-site, in-person team currently based in San Francisco.

Apply