Closed listing
Compiler Optimization Engineer
- Toronto, ON, Canada
This opening has closed.
The original listing is kept below for reference.
Job details
- Posting language
- English
- Posted
Similar jobs hiring now
Added
Sr. Software Development Engineer, Neuron Explorer
Amazon · Toronto, ON, Canada
Added
Compiler Engineering Graduate Intern
Intel Corporation · Toronto, ON, Canada
Added
Staff Deep Learning Compiler Engineer
quadric · Remote, Canada
Added
Compiler Engineering Intern
Intel Corporation · Toronto, ON, Canada
Original job description
Archived listing. The details below describe a past opening.
About Us
At Lemurian Labs, we're reimagining the foundations of computing to make AI accessible to everyone. Our mission is to remove the limits of scale, hardware, and cost that hold back innovation, so the people solving humanity's hardest problems can move faster.
We're building a new kind of software stack: a hardware-agnostic platform that makes every system - from a laptop to a supercomputer - feel like one seamless engine. Developers can write once, run anywhere, and get state-of-the-art performance across any chip, any cloud, at any scale. It's a complete rethink of how software and hardware interact - designed for the era beyond Moore's Law.
We're not looking for the comfortable or the conventional; we're looking for the bold. The engineers who crave frontier problems, who want to bend the limits of what's possible, who see infrastructure not as a constraint but as a canvas. If you want to build the foundation for the next era of AI and change what humanity can achieve in the process, join us.
About the Role
We're looking for a Graph Optimization Compiler Engineer to own the middle tier of our AI compiler stack - the layer where high-level model graphs are transformed, simplified, and made ready for efficient code generation. You'll design and implement the optimization passes that make the difference between a model that runs and a model that flies.
This role sits between our compiler front end and code generation backend. You'll work on graph-level transformations - fusion, layout optimization, dead code elimination, constant folding, and more - with a direct line of sight to the performance outcomes your work produces. If you think in data flow graphs and optimization passes, and you want that thinking to power the next generation of AI infrastructure, we'd love to talk.
What You'll Do
- Design, develop, and maintain the graph optimization layer of our heterogeneous AI compiler
- Implement and extend graph-level transformation passes including operator fusion, layout propagation, dead code elimination, constant folding, and algebraic simplification
- Define and evolve our intermediate representation (IR) to support new optimization opportunities as ML model architectures advance
- Analyze performance data to identify optimization gaps and drive measurable improvements in throughput and latency
- Collaborate with front end and code generation teams to ensure clean IR interfaces and well-structured optimization pipelines
- Propose and prototype new optimization strategies in response to advances in model design and hardware capabilities
- Contribute to testing and validation infrastructure to ensure optimization correctness across model types and hardware targets
Requirements
Essential Skills and Experience
- BS degree in Computer Science, Computer Engineering, or equivalent practical experience
- 4+ years of experience working with compilers, with a focus on intermediate representation design or optimization passes
- Deep knowledge of graph-level compiler optimization techniques - fusion, tiling, layout transformations, and related methods
- 4+ years of experience with C/C++
- Strong written and verbal communication skills; ability to write clear and concise technical documentation
Preferred Skills and Experience
- Master's or PhD in Computer Science, Computer Engineering, or equivalent
- Experience with polyhedral models or affine analysis for loop and tensor optimization
- Familiarity with hardware memory hierarchies and how layout decisions impact performance on GPUs or accelerators
- Experience working with MLIR, XLA, or similar graph-level IR frameworks
- Experience with ML framework internals - PyTorch eager/compile mode, JAX/XLA, or TensorRT
- Strong understanding of ML model architectures and their computational patterns (attention, convolution, normalization, etc.)
- Knowledge of quantization, sparsity, or other model-level optimization techniques
- Contributions to open-source compiler or ML infrastructure projects
Why Join Lemurian Labs
- Own a critical layer of our compiler stack where optimization decisions have direct, measurable impact on model performance
- Work on the hardest graph-level problems in AI infrastructure - across diverse hardware targets and model architectures
- Collaborate with a team that treats infrastructure as a canvas and optimization as a craft
- Competitive compensation including equity, medical/dental/vision, retirement savings, and wellness benefits
Lemurian Labs is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of gender identity, race, ethnicity, sexual orientation, disability status, age, or background.
Compensation depends on experience and geographic location and will be narrowed during the interview process. Additional benefits include equity, company bonus opportunities, medical, dental, and vision coverage, a retirement savings plan, and supplemental wellness benefits.
More about this job
Responsibilities
You will design and implement the graph optimization layer of a heterogeneous AI compiler, focusing on transformations like operator fusion and layout propagation. You will also analyze performance data to drive improvements in throughput and latency while collaborating with front-end and backend teams.
Requirements
Candidates must have a BS in Computer Science or related field and at least 4 years of experience with compilers and C/C++. Proficiency in graph-level optimization techniques and intermediate representation design is essential for this role.
Skills
- Compiler optimization
- C++
- C
- Intermediate representation
- Graph-level transformations
- Operator fusion
- Layout propagation
- Dead code elimination
- Constant folding
- Algebraic simplification
- MLIR
- XLA
- PyTorch
- JAX
- TensorRT
- Performance analysis
Benefits
- Equity
- Medical insurance
- Dental insurance
- Vision coverage
- Retirement savings plan
- Wellness benefits
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Data & Analytics
Keywords
- Compiler Optimization
- AI Infrastructure
- C++
- C
- Graph Optimization
- Intermediate Representation
- IR
- Operator Fusion
- Layout Propagation
- Dead Code Elimination
- Constant Folding
- Algebraic Simplification
- MLIR
- XLA
- PyTorch
- JAX
- TensorRT
- Polyhedral Models
- Affine Analysis
- Hardware Memory Hierarchies
- Quantization
- Sparsity
- Machine Learning
- Performance Engineering
- Heterogeneous Computing