Design and evolve the translation pipeline's IR, analysis passes, and C++ code generation, keeping the core language-independent.
Build type inference and lowering for dynamic Python features like arbitrary-precision integers, exceptions, and generators with measurable semantic equivalence.
Integrate LLM-assisted generation and set engineering standards for tests, CI/CD, and code review.
Design and optimize compiler infrastructure using MLIR, TVM, and LLVM for Quadric's proprietary GPNPU architecture.
Develop graph-level optimizations, operator fusion, quantization, and memory allocation strategies for deep learning models.
Collaborate with hardware teams on instruction set extensions and validate compiler correctness on FPGA and ASIC platforms.
Quadric is building the world's first General-Purpose Neural Processing Unit (GPNPU), enabling edge devices to run neural network inference and conventional C++ code on a single programmable architecture. Founded in 2016 and based in Burlingame, California, the company has a collaborative, low-ego culture focused on innovative disruption with significant ownership and impact.
Work on Falcon, a JIT compiler based on LLVM, to push Java performance further.
Collaborate with a distributed, highly collaborative team of compiler experts.
Create new features and optimizations while contributing to open source.
Azul is the only company focused 100% on Java, building the Java platform for the modern cloud enterprise. It is a growing, global company and part of the Java and OpenJDK community, with a collaborative culture of top experts worldwide.
Own performance of Falcon JIT compiler for workloads, from analysis to implementation.
Design and implement optimizations in the middle end and back end, from inlining to vectorization.
Work in a distributed team of compiler experts and contribute to upstream LLVM.
Azul develops Java runtime platforms, including Azul Platform Prime with the Falcon JIT compiler. They operate as a distributed team of compiler experts focused on performance and production reliability.
Owns the architectural direction for Code Metal's AI platform and the team building it.
Builds production-grade inference serving, model gateways, agent harnesses, and context-engineering services.
Instruments the stack end to end and drives experimentation and observability for reliable AI systems.
Code Metal provides automated software engineering using AI and formal verification to produce provably correct code. Founded in 2023 and backed by Accel, Salesforce Ventures, and others, it serves defense and enterprise customers from offices in Boston and San Francisco.
Build and improve pipelines to parse clinical documents into validated executable modules.
Design and refine target representations for clinical guideline logic.
Develop deterministic validators using static analysis to make ambiguity explicit.
This healthtech team is building a production compiler that converts clinical standards into structured, executable decision logic. The team size is not specified, but the role emphasizes collaboration with clinicians and technical colleagues.
Implement tracker blocking that stops hidden trackers on every site without slowing page loads.
Bring VPN and Personal Information Removal into the browser through the DuckDuckGo subscription.
Catch startup and responsiveness regressions automatically, so the browser stays fast as it grows.
We're DuckDuckGo, the online protection company on a mission to raise the standard of trust online. We're a remote-first team of 400+ that's been profitable since 2014, with annual revenue over $100m and a culture of trust, inclusivity, and empowered project management.
Build and improve pipelines for parsing clinical documents and transforming guideline content into executable modules.
Evaluate open and closed models, fine-tuning approaches, and constrained decoding strategies for clinical fidelity.
Develop deterministic validators and benchmarks to assess correctness and expose ambiguities in clinical logic.
They build a production compiler that turns clinical standards into structured, executable decision logic. The team is research-focused and collaborative, working across engineering and clinical disciplines.
Build pipelines that parse clinical standards and transform them into executable modules using document and layout models.
Evaluate open and closed models, fine-tuning approaches, and constrained decoding strategies.
Develop datasets, benchmarks, metrics, and adversarial cases to assess clinical fidelity and correctness.
They build a clinical AI compiler that turns healthcare standards into validated, executable decision logic. The team is research-focused, collaborative, and works closely with clinicians to make recommendations traceable and reliable.