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.
Build the component layer around our layout synthesis engine, including API contracts, services, and evaluation gates.
Turn model retraining into a one-command job with built-in benchmarks and readable results for the whole team.
Own latency and cost budgets for learned capabilities and partner with infrastructure engineers on MLOps and deployment.
Higharc is a VC-backed startup that is changing how new homes are designed and built using spatial AI and generative floor plan technology. The company is fully remote, has raised over $175M, and values flexibility, collaboration, and asynchronous deep work.
Build inference systems powering RL research, scaling toward 1–3T parameter models on NVL72 clusters.
Optimize rollout throughput, multi-node MoE inference, and weight-update overhead for long-horizon agents.
Collaborate with model researchers on architecture choices balancing model quality and execution cost.
Prime Intellect is building the open superintelligence stack, unifying compute, environments, and deployment for frontier-scale AI post-training. Backed by $150M in funding, the team is mission-driven and hard-working, focused on accelerating open AI research.
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 maintain core infrastructure for Quora's ML platform, ensuring high availability, scalability, and performance.
Build and improve distributed systems serving ML models in production, from Large Recommendation Models to Large Language Models.
Work on GPU model serving, optimizing latency, throughput, and cost to support larger and more capable models.
Quora's mission is to grow the world's collective intelligence through two platforms: Quora for global knowledge sharing and Poe for AI agent collaboration. We are a remote-first company with passionate, collaborative, and high-performing global teams, rooted in transparency and experimentation.
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 end-to-end ML systems from data and training to evaluation, inference, and deployment.
Build and evolve training, fine-tuning, and inference infrastructure for large models.
Partner with research and application engineering to turn model capabilities into product improvements.
Next Step Systems is an IT recruiting and staffing agency that connects companies with machine learning and technology talent. They specialize in placing candidates in remote and on-site roles across the United States and emphasize a fast-paced, results-oriented culture.
Drive adoption of Tenstorrent's AI products by collaborating with enterprise customers and the sales team.
Provide technical expertise in AI/ML to translate complex concepts for non-technical stakeholders.
Partner with sales operations and product teams to shape future product enhancements from customer feedback.
Tenstorrent is an AI technology company developing high-performance RISC-V CPUs and AI platforms. The diverse team of technologists values collaboration, curiosity, and solving hard problems.
Own the inference stack end-to-end, including pipeline-parallel execution, placement and routing, transport, serving engine, and failure handling.
Invent and validate novel algorithms for fast inference on consumer hardware over the public internet, then put them into production.
Serve both RL training rollouts and final model serving in a permissionless, trustless setting.
Pluralis Research works on Protocol Learning: training and serving large models in a fully decentralized way on small consumer-grade devices connected via the internet. They are a world-class, deeply technical team backed by Union Square Ventures and other tier-1 investors.
Bring new open-source models into production with a focus on latency and throughput.
Optimize inference systems including batching, KV cache, speculative decoding, and quantization.
Design, deploy, and operate heterogeneous clusters across infrastructure vendors to improve reliability and cost efficiency.
They are an early-stage AI infrastructure startup making large language model inference faster and more cost-efficient. The small team focuses on low-level performance engineering and AI-assisted workflows, with a strong emphasis on technical fundamentals.
Lead the effort to make Runpod the fastest and most cost-efficient place for LLM inference, owning performance end to end.
Profile and diagnose performance bottlenecks across the serving stack, from scheduling to kernels, and implement fixes.
Work closely with product and infrastructure teams to shape how inference is offered, turning improvements into production-ready defaults.
Runpod is the AI Developer Cloud, providing a platform for over one million developers to experiment, train, fine-tune, deploy, and scale AI. We're a small, remote-first team that takes ownership seriously, moves fast, and has processed more than 20 billion inference requests.
Own optimization of the inference stack: throughput, latency, cold starts, and cost per token.
Tune SGLang/vLLM engines, cut TTFT, boost KV cache reuse, and kill cold starts on 1.5 TB checkpoints.
Set technical direction with data, benchmark every change, and scale inference across nodes.
Kimchi is an LLM inference platform serving open models like DeepSeek and GLM from its own GPU fleet via one API line. It's made by CAST AI, a globally distributed team across 34 countries that hit unicorn status in 2026.
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.
Build and optimize RL infrastructure for large-scale training.
Improve efficiency across compute, memory, networking, and scheduling.
Design low-level optimizations, from kernels to runtime.
Prime Intellect builds the open superintelligence stack, providing full-stack infrastructure for frontier-scale AI post-training and deployment. The company has raised $150M from top investors and is mission-driven, focused on open-source AI and accelerating science.
Own end-to-end technical strategy for major areas of the Voyager SDK’s application layer, including model deployment and pipeline development.
Set technical direction for image processing, create reusable architectures, and partner with the compiler team to resolve compilation issues.
Represent the Applications team’s capabilities to business stakeholders, mentor engineers, and drive documentation standards.
Axelera AI is a deep-tech company creating a next-generation AI platform to advance humanity. With $450 million raised and 250+ employees including 60+ PhDs, they have a strong business pipeline exceeding $100 million and are committed to innovation.
Design, build, and evolve systems for distributed model training and batch inference at scale.
Develop workflows for experiment tracking, model evaluation, and registry management.
Optimize training and inference performance across GPU utilization, data loading, and distributed communication.
NOETIK is a frontier therapeutics company using AI to understand human biology and improve oncology outcomes, building foundation models on proprietary multimodal data. It fosters a collaborative, interdisciplinary culture focused on solving hard problems together.
Design and build systems that improve the efficiency of ML training and inference workloads.
Develop tooling and benchmarking frameworks to help ML engineers debug, profile, optimize, and monitor model performance.
Partner with ML teams to optimize distributed training, reduce costs, and drive technical strategy for scalability and reliability.
Reddit is a community of communities where users share interests and authentic conversations. With 100,000+ communities and ~130 million daily visitors, it has a flexible-first culture supporting remote work.
Optimize software workloads across modern datacenter and agentic AI applications, identifying performance bottlenecks and developing software, compiler, runtime, and hardware-aware optimizations.
Profile real-world applications and investigate memory, concurrency, and CPU microarchitecture behavior to improve throughput, latency, and efficiency.
Collaborate with compiler, runtime, systems software, hardware, and performance modeling teams to influence future RISC-V CPU architecture.
Tenstorrent is leading the industry on cutting-edge AI technology, developing high performance RISC-V CPUs from scratch. Our diverse team of technologists values collaboration, curiosity, and a commitment to solving hard problems.
Architect GPU cluster topologies spanning compute, networking, storage, and control planes.
Model AI workloads like LLM training and inference to guide performance tradeoffs.
Collaborate with cross-functional teams to deploy and scale reliable AI infrastructure.
This role is with a partner company focused on designing next-generation AI infrastructure at massive scale. The company fosters a fast-moving, engineering-led culture with a collaborative, international team.