Software Engineer, AI Compiler at Normal%20computing%20ai

Normal%20computing%20ai

New York City·5h ago

Mid LevelFull-timeMachine LearningTensorFlowPyTorchAIML

Salary Range

$102,000 - $138,000 /year

Estimated

This salary is estimated based on similar roles. The actual salary may vary.

About the Role

We are looking for a ML Compiler Engineer to join Normal Computing and help build an AI accelerator from the ground up. This role involves creating a compiler toolchain that takes machine learning models from high-level frameworks down to efficient execution on our novel architecture.

Key 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

Requirements

✓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