Design and maintain scalable ML infrastructure including data pipelines, training workflows, and model deployment systems.
Own end-to-end ML lifecycle operations, ensuring reliable delivery of models into production at scale.
Implement monitoring, telemetry, and feedback loops for ML models running across large-scale device fleets.
Our partner company develops ML systems for connected hardware products used by customers worldwide. They operate in a fast-paced, product-driven environment with a collaborative and technically ambitious culture focused on real-world ML impact.
Build and operate production-grade model serving infrastructure using vLLM, TGI, or Triton frameworks.
Design and implement auto-scaling, multi-model architectures, and intelligent request routing for ML inference.
Optimize GPU utilization, memory efficiency, and observability to ensure low-latency, cost-effective systems.
They are a distributed cloud infrastructure startup building AI-native cloud services with GPU-powered compute. The company is well-funded, fast-scaling, and operates in a remote-first environment with a focus on sustainability and decentralization.
Build and operate the real-time inference service for the risk decision engine with low latency and high availability.
Own model deployment infrastructure including CI/CD, shadow mode, and staged rollouts.
Build model observability and partner with Risk Data Science for production operation.
Mercury is a fintech company that provides banking services for startups via partner banks. The company is committed to creating a safe environment and values diversity, with a growing team focused on innovation.
Build and operate the ML lifecycle platform, including tooling for experiment tracking, model registry, and versioned pipelines.
Own CI/CD and deployment for ML workloads, building automated pipelines from notebook to production.
Make models observable and reliable in production with monitoring for latency, drift, data quality, and cost signals.
dv01 provides a data analytics platform for the structured finance market, offering transparency into investment performance and risk for lenders and Wall Street investors. With over 400 clients and coverage of over 100 million loans, dv01 is a data-first company with a diverse and innovative culture.
Design and operate core AI platform components for training, deploying, and serving ML models at scale.
Own model serving and inference workflows end-to-end, optimizing for reliability, latency, throughput, and cost.
Collaborate with product, infrastructure, and security teams to build scalable platform capabilities for AI-powered features.
Mozilla Corporation is the non-profit-backed technology company behind Firefox and Pocket, with over 225 million monthly users. A wholly-owned subsidiary of the Mozilla Foundation, the company is mission-driven, employee-owned, and focused on privacy and open standards.
Collaborate with data scientists and engineers to build scalable ML pipelines, troubleshoot infrastructure issues from Linux to Kubernetes, and optimize model performance.
Drive high engineering standards, design on-premises MLOps solutions, and maintain tools for deployment and monitoring.
Refine CI/CD workflows, incorporate ML model training and evaluation into testing, and ensure seamless handover between research and production.
Learneo is a platform of builder-driven businesses, including Course Hero, CliffsNotes, LitCharts, Quillbot, Symbolab, and Scribbr, focused on supercharging productivity and learning. The company supports high-growth businesses with centralized corporate operations and has a virtual-first culture with employees across multiple countries.
Own the ML serving API and deploy models to production with CI/CD and infrastructure as code.
Build monitoring, alerting, and reliability for NBA models and LLM agents.
Drive architectural decisions and mentor engineers on MLOps patterns.
Clutch is a vertical SaaS company backed by Andreessen Horowitz, revolutionizing how credit unions engage with members via fintech lending software. The company is small and ambitious, with a lean data team of five that values pragmatism and fast shipping.
Collaborate with data scientists and software engineers to build scalable data pipelines and ML deployment systems.
Troubleshoot issues across the ML infrastructure stack, from Linux and Docker to Kubernetes and model serving.
Drive high engineering standards through code reviews, testing, and CI/CD enhancements.
Quillbot helps students and professionals strengthen their writing with AI-powered tools. We serve over 56 million users globally and foster a collaborative, virtual-first culture.
Design and build systems that improve the efficiency of ML training and inference workloads.
Develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance.
Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements.
Reddit is a community of communities built on shared interests, passion, and trust, hosting the most open and authentic conversations on the internet. With over 100,000 active communities and approximately 126 million daily active users, Reddit is one of the internet's largest sources of information.
Own the technical design and delivery of subsystems in a high-throughput, low-latency inference platform.
Develop robust API layers and SDKs that abstract complex distributed inference orchestration.
Build and harden a multi-tenant control plane for metering, rate limiting, and tenant isolation.
Stack develops revolutionary AI and autonomous systems to enhance safety and efficiency in trucking. The team has decades of experience deploying real-world systems and is committed to inclusion, entrepreneurship, and innovation.
Own and scale AI compute and deployment platforms including Kubernetes and GitOps pipelines.
Build inference infrastructure and observability stacks for LLM-powered workflows.
Drive security, compliance, and governance at the systems level in a regulated healthcare environment.
Hims & Hers is a leading health and wellness platform focused on making healthcare accessible and personal. As a publicly traded company on the NYSE (HIMS), it offers flexible/remote work and a culture centered on innovation and employee well-being.
Optimize production LLM serving with vLLM and SGLang to maximize throughput and minimize latency through batching and quantization.
Profile training runs to find bottlenecks and resolve them with attention implementations like FlashAttention on H200 and GB200 hardware.
Deploy and operate multiple models on shared GPU clusters with autoscaling, bin-packing, and efficient handling of mixed workloads.
Egen is a fast-growing technology company with a data-first mindset, partnering with clients on Google Cloud and Salesforce to drive action through data and insights. We are a team of dedicated engineers who thrive on solving tough problems and continually innovate to achieve fast, effective results.
Design and build scalable ML training, deployment, and inference pipelines using CI/CD and cloud infrastructure.
Implement MLOps for model versioning, monitoring, and automated retraining to detect drift and performance degradation.
Partner with Data Scientists and Product teams to productionise models and integrate ML into customer-facing products.
We develop solutions that make an impact for companies around the globe. Our culture embraces openness, acts with respect, shows grit & guts, and combines employment with enjoyment.
Own availability, latency, and throughput SLOs across a large fleet of generative media model APIs serving production traffic at scale.
Build monitoring, alerting, and observability to catch ML-specific failures, output quality degradation, and model regressions before customers do.
Harden model deployment workflows with canary releases, shadow testing, automated rollbacks, and validation gates to ship new model versions safely.
Fal is the generative media ecosystem powering the next generation of AI products, providing infrastructure, tools, and model access for developers and enterprises. As a unified platform for high-performance inference, orchestration, and observability, fal is becoming the ecosystem ambitious teams build on in a market projected to grow by hundreds of billions over the next decade.
Design, train, evaluate, and ship ML systems for governance and security, starting with prompt injection detection and behavioral anomaly detection.
Build supporting infrastructure including data pipelines, feature stores, model serving, and evaluation harnesses.
Set technical direction for ML work, own architecture, evaluation methodology, and model lifecycle.
Docker provides developer tools for building, sharing, and running applications across Docker Desktop, Docker Hub, and Docker Scout. With over 20 million monthly users and a globally distributed remote-first team, Docker is trusted by solo founders to the world's largest companies.
Design, build, and ship ML models that power content generation and quality eval scoring for Canva's generated element and template library.
Own the full ML lifecycle — from data pipelines and training through to deployment, monitoring, and iteration.
Partner with Content Engine, CORE AI Research, AI Media, and Discovery teams to align ML work with the broader content strategy.
Canva is redefining how the world experiences design with its intuitive design platform. We serve hundreds of millions of users globally and foster a culture of flexibility, inclusion, and innovation.
Evolve and maintain our Kubeflow, Feast, and Spark-on-Kubernetes ML infrastructure.
Design tools and APIs empowering teams to transition from centralized bottlenecks to self-service excellence.
Collaborate with Data Science teams to apply software engineering best practices to ML workflows.
Wellhub revolutionizes workplace wellness by connecting employees to partners for fitness, mindfulness, therapy, nutrition, and sleep in one subscription. Headquartered in NYC with team members across the globe, we value wellbeing, collaboration, and different perspectives.
Architect and maintain production high-traffic LLM serving systems.
Optimize throughput, latency, and cost for leading open-source LLMs.
Debug and optimize major inference engines like SGLang, vLLM, or TensorRT using PyTorch and CUDA.
We are building decentralized and confidential machine learning infrastructure to enable user-owned AI. Our team is focused on highly scalable and efficient infrastructure for open-source AI at a global scale, with a culture that values innovation and performance.
Own reliability, latency, and performance for AI platform services and data infrastructure on AWS.
Design and maintain CI/CD pipelines, infrastructure-as-code, and observability frameworks across the stack.
Partner with AI and data engineers to ensure secure, cost-optimized, and scalable deployment of platform components.
HHAeXchange is the leading technology platform for home and community-based care, providing an end-to-end homecare solution for people who are aging or have disabilities. Founded in 2008, the company is passionate about transforming healthcare by connecting patients, providers, managed care organizations, and states.
Build and maintain scalable machine learning solutions in production.
Train and validate deep learning and statistical models for real-world applications.
Partner with product managers and engineers to define requirements and drive ML roadmap.
Twilio is a cloud communications platform that empowers businesses to build personalized customer experiences through APIs. With thousands of employees worldwide, the company champions a remote-first culture focused on inclusion and innovation.