Software Engineer, AI Compiler

Normal Computing | Incredible Opportunities

The Normal Team builds foundational software and hardware that help move technology forward - supporting the semiconductor industry, critical AI infrastructure, and the broader systems that power our world. We work as one team across New York, San Francisco, Copenhagen, Seoul, and London.

Your Role in Our Mission:

We're building an AI accelerator from the ground up, and we need a strong ML compiler engineer to be at the heart of hardware-software co-design. This isn't about inheriting a mature compiler stack - it's about creating one.

You'll join at the architecture definition stage, directly influencing ISA design and the trade-offs that determine what our hardware can do. As we progress toward hardware bringup, you'll build the complete compiler toolchain that takes machine learning models from high-level frameworks down to efficient execution on our novel architecture.

This role offers the rare opportunity to shape both silicon and software simultaneously. You'll work alongside hardware architects and researchers to co-design compiler strategies that unlock the full potential of our accelerator, building infrastructure that bridges the gap between ML model graphs and custom ISA primitives. Your compiler decisions will directly inform hardware features, and hardware capabilities will open new optimisation frontiers for your toolchain.

If you want to architect a compiler stack from first principles, optimise ML workloads on new hardware, and see your decisions realised in silicon, this is the role.

Responsibilities:

• Work across the full stack with software, systems, and hardware teams to ensure correctness, performance, and deployment readiness for real workloads

• Contribute to shaping the long-term compiler architecture and tooling strategy in a fast-moving startup environment

• Design and implement parts of the compiler stack targeting our novel AI accelerator, including front-end lowering, IR transformations, optimization passes, and backend code generation

• Build and evolve MLIR/LLVM based infrastructure to support graph lowering, hardware-aware optimizations, and performance-centric code emission

• Collaborate closely with hardware architects, microarchitects, and research teams to co-design compiler strategies that align with evolving ISA and hardware constraints

• Develop profiling and analysis tools to identify performance bottlenecks, validate generated code, and ensure high throughput/low latency execution of AI workloads

• Enable efficient mapping of high-level ML models to hardware by working with model frameworks and graph representations (e.g., ONNX, JAX, PyTorch)

• Drive performance tuning strategies including kernel authoring, schedule generation, and hardware-specific optimization passes

What Makes You A Great Fit:

• BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, or related field

• 4+ years of hands-on ML compiler or systems engineering experience

• Demonstrated experience building and owning an end-to-end compiler stack (front-end, IR, optimization, and backend code generation)

• Experience working with machine learning models, neural network graphs, and graph optimizations as part of lowering and acceleration, using frameworks like TVM, XLA, or Glow

• Comfortable collaborating with hardware teams to map novel architectural primitives from IR to efficient lowerings, kernel implementations, and runtime support

• Strong understanding of compiler performance trade-offs, profiling, bottleneck analysis, and optimization strategies for ML workloads

Bonus Points For:

• Prior experience on compilers for AI/ML accelerators, GPUs, DSPs, or domain-specific architectures

• Contributions to LLVM, MLIR, XLA, TVM, or related open-source compiler projects

• Experience in kernel performance optimization and accelerator-specific code generation

• Demonstrated work in hardware-software co-design where compiler insights shaped ISA or architectural decisions

• Experience building or contributing to cycle-accurate simulators for performance modeling

• Prior work building profiling tools, performance evaluation suites, or bottleneck analyzers for compiler or runtime stacks

• Familiarity with deep learning frameworks and model formats (e.g., JAX, ONNX**, PyTorch, TensorFlow**) and graph transformations

• Experience designing custom IR dialects, optimization passes, and domain-specific lowering transformations

Equal Employment Opportunity Statement

Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.

Accessibility Accommodations

Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accomodations@normalcomputing.ai.

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