Own LLM and VLM support on Metis and Europa AIPUs, from studying new models to shipping them in the Voyager SDK.
Use AI coding agents daily to build, test, and verify your work, combining LLM/VLM knowledge with solid software engineering.
Productize results into supported Voyager SDK features with stable APIs, tests, documentation, and release gates.
Axelera AI is a deep-tech company creating the next-generation AI platform to advance humanity and improve the world. They have raised $450M, employ 250+ people (60+ PhDs) across 21 countries, and foster an open, innovative, and inclusive 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.
Build AI applications, agents, skills, connectors, and workflow automations that solve business problems.
Translate ambiguous business needs into AI-native workflows and own engineering from discovery to production launch.
Work closely with internal teams to embed AI solutions and drive measurable gains in quality, speed, and cost.
Reddit is a community of communities built on shared interests, passion, and trust. With 100,000+ active communities and about 130 million daily active visitors, it is one of the internet's largest sources of information.
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.
Own end-to-end performance optimization for production ML workloads, from bottleneck identification to rollout.
Diagnose ML performance bottlenecks across model execution, frameworks, runtimes, memory, and CPU/GPU utilization.
Design and implement improvements in inference execution, runtime behavior, batching, memory management, and precision.
Reddit is a community of communities, hosting open and authentic conversations across the internet. With over 100,000 active communities and approximately 130 million daily active users, it is one of the web's largest sources of information.
Design high-performance training platform components including orchestration, observability, and performance tuning.
Deliver end-to-end ML pipelines from dataset curation to training, validation, and deployment.
Drive technical direction across ML Platform, Infrastructure, Autonomy, and Safety teams.
Stack is developing revolutionary AI and advanced autonomous systems for safer, more reliable, and efficient operations, focusing on autonomous trucking. The Stack team brings decades of experience in real-world systems and is committed to a culture of inclusion, entrepreneurship, and innovation.
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.
Develop, fine-tune, and optimize self-hosted large language models for personalized AI conversations.
Work across the full model development lifecycle, from dataset preparation to GPU-based experiments and production deployment.
Collaborate with cross-functional teams to enhance conversational quality, memory, and multilingual interactions.
This company builds innovative consumer-facing AI products powered by self-hosted large language models. They operate a remote-first culture across Europe, emphasizing collaboration, continuous learning, and technical excellence.
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.
Develop the architecture and infrastructure needed to scale the product to a large user base.
Build and operate the agent runtime, including orchestration, memory, verification, and supporting systems.
Take projects end to end across backend, infrastructure, frontend, and AI agents.
They are an early-stage AI infrastructure startup building scalable infrastructure for AI-powered service businesses. As the first engineer, you'll work directly with the founder in a fast-moving, ownership-driven culture.
Own agent quality end-to-end for Bliro's chat and voice agents, including prompting, context, and model evaluation.
Build eval sets, LLM judges, and benchmarks to measure quality, latency, and cost with statistical rigor.
Establish a repeatable process for improving agent quality and set the long-term technology roadmap.
Bliro builds an AI assistant that handles desk work for field sales teams, with over one million customer touchpoints documented. Backed by leading investors and trusted by German Mittelstand companies, it's a small, high-performing team that values ownership and continuous improvement.