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.
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 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.
Develop RL algorithms for reasoning, coding, and long-horizon tasks.
Design and run scaling experiments to improve training recipes across model sizes.
Extend RL training frameworks for asynchronous workflows and reliability.
Prime Intellect builds the open superintelligence stack, offering a platform that unifies compute, training, and deployment for frontier AI teams. With $150M in funding and a mission-driven culture, they focus on open-source AI research and real infrastructure.
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.
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.
Lead the end-to-end machine learning lifecycle, from data preparation and model training to evaluation, inference, and deployment.
Build and optimize scalable training, fine-tuning, and inference pipelines for large-scale models on GPU infrastructure.
Establish production best practices for model quality, reliability, safety, and measurable performance improvements.
This company builds production-grade machine learning systems and large-scale GPU-powered inference pipelines. It is a fully remote team that values technical rigor, collaboration, and hands-on engineering.
Build and extend pretraining code to scale experiments into full training runs.
Evaluate model architecture changes and optimize training throughput and resilience.
Run controlled ablations and collaborate with data and evaluation researchers.
Prime Intellect is building an open superintelligence stack, providing the infrastructure and platform for frontier AI training and deployment. The company has raised $150M from top investors and boasts a mission-driven team working to decentralize AI.
Design and build challenging evaluations and executable environments for coding and long-horizon agents.
Develop scoring logic and verifiers to detect reward hacks and trace failures through model outputs and harness code.
Build and scale RL data pipelines for generating and curating challenging coding and agentic tasks.
Prime Intellect is building the open superintelligence stack, providing infrastructure for post-training frontier AI models and open-source models trained for long-horizon tasks. The company has raised $150M from top investors and is a mission-driven team passionate about accelerating science and AI.
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.
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.
Distributed pretraining: Implement and optimize model-parallel training on heterogeneous GPUs under low-bandwidth, high-latency links.
Performance optimization: Apply techniques that reduce communication overhead while maintaining model convergence in challenging network environments.
Elasticity and fault tolerance: Build robust checkpointing and recovery so runs survive node churn as participants join and leave.
Pluralis Research is a decentralized AI research company developing Protocol Learning to train large models on consumer devices across the internet. The team is world-class and deeply technical, backed by tier-1 investors, with a remote-first culture.
Build the Learning Gym, a sandboxed environment for training and evaluating AI agents on real Niural workflows with ground-truth reward signals.
Engineer verifiers, run reinforcement learning loops, and establish honest baselines to prove agent improvements generalize.
Write first-author research papers and internal technical reports, and represent the work externally through preprints and talks.
Niural is a global payroll, Employer of Record (EOR), Agent of Record (AOR), and Contractor Management platform. It is a relentlessly ambitious team focused on building foundational internet infrastructure for the AI era.
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.
Operate and expand Telnyx's own B300 GPU fleet to maximize inference throughput per GPU-dollar.
Design and implement serverless inference for open-weight models and dedicated enterprise deployments.
Work upstream in open-source technologies like vLLM, SGLang, and Kubernetes.
Telnyx is an industry leader building the future of global connectivity through a private, multi-cloud IP network and edge APIs. The company is financially stable and profitable, with a global team and a focus on innovation and continuous learning.
Build the RL post-training stack end-to-end: rollout ingestion, reward computation, policy updates, and returning updated weights to the network.
Invent algorithms for asynchronous, high-latency, partially trusted generation, including staleness tolerance and off-policy corrections.
Ship the first decentralized post-trained models, building evals and taking the release from run to public artifact.
Pluralis Research is developing Protocol Learning, a decentralized approach to training and serving large models on consumer devices. We are a small, deeply technical team backed by Union Square Ventures, with a remote-first culture and team members spread globally.
Build reinforcement learning and agent environments for real customer and Frontier lab use cases, including task specifications, scoring, and evaluation.
Develop benchmarks and evaluation harnesses to measure model and data quality across accuracy, robustness, safety, latency, and cost.
Run fine-tuning, adapter, and other model experiments to evaluate how data and methods influence model behavior, and deploy local or self-hosted models for evaluation and inference.
Appen has been a leader in AI training data for over 30 years, specializing in human-generated data to train, fine-tune, and evaluate models across generative AI, LLMs, computer vision, and speech recognition. They support model development through an AI-assisted data annotation platform and a global crowd of over 1 million contributors in more than 200 countries, fostering a culture of innovation, collaboration, and humility over ego.
Build, train, and evaluate machine learning models end to end, from data and experiment design through training and reporting.
Design reinforcement learning environments, reward functions, and verifiers, and fine-tune language models using methods like PPO, DPO, and GRPO.
Collaborate with domain experts, analyze datasets, and present findings clearly to both technical and non-technical audiences.
EnableComp provides specialty revenue cycle management solutions for healthcare organizations using its unified E360 RCM platform. It is a multi-year Top Workplaces recipient and has been named to the Inc. 5000 list of fastest-growing private companies for eleven years.
Build and ship LLM-powered applications and AI agent workflows.
Design systems for reasoning, planning, memory, tool use, and multi-step execution.
Optimize AI systems for quality, latency, and cost, and establish production practices for observability and evaluation.
Gina's Tech Jobs is a technology recruiting firm that connects companies with engineering and IT professionals. They focus on remote and onsite placements, offering personalized recruitment services and career support.