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.
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.
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.
Research and implement state-of-the-art techniques to accelerate AI inference: quantization, sparsity, distillation, speculative decoding, and caching.
Partner closely with hardware and compiler teams to ensure algorithmic improvements translate to real gains on custom silicon.
Build profiling tools and comprehensive benchmarking frameworks to measure model quality and efficiency.
EnCharge AI is building the next generation AI platform using novel in-memory-computing architecture. The team consists of experienced AI researchers, silicon & systems engineers, and architects backed by leading investors.
Build ML infrastructure for low-latency model deployment, distributed inference pipelines, and real-time telemetry.
Scale ranking systems by moving models from experimentation to production, optimizing latency and cost trade-offs.
Implement model CI/CD for automated versioning, canary releases, hot-swappable container rollouts, and zero-downtime rollbacks.
Sequen provides an integrated platform that pairs cutting-edge frontier ranking models with infrastructure to run them in production at sub-10ms latency and enterprise scale. They are a small, highly technical, early-stage team focused on turning recent AI advances into production-grade systems.
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.
Design and deliver production AI and agentic systems across document intelligence, workflow automation, and copilots.
Own architecture decisions for LLM-based systems, including retrieval, tool use, orchestration, memory, and evaluation.
Manage evals and observability for production AI, ensuring system accuracy and detecting regressions.
Maxwell is a mortgage technology and fulfillment company on a mission to make lending simpler, faster, and more accessible. It is a remote-first team that takes craft seriously and moves with intention, building a cutting-edge AI company in mortgage technology.
Develop and operate production-ready AI and machine learning systems for enterprise-scale products.
Build and optimize LLM-powered applications, RAG pipelines, and intelligent agents.
Implement software engineering best practices for AI development including CI/CD and testing.
Our partner is building enterprise-grade AI solutions that deliver measurable business impact. They offer a remote-friendly work environment with a collaborative engineering culture focused on innovation, quality, and continuous learning.
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.
Design and implement multi-agent AI systems using frameworks like LangChain and CrewAI, building agent-to-agent orchestration pipelines.
Fine-tune foundation models, integrate retrieval-augmented generation, and develop APIs and backend services for production deployment.
Containerize and deploy agents with Docker and Kubernetes, while collaborating with QA and product teams to benchmark accuracy and safety.
Innodata is a global data engineering company focused on enabling the responsible advancement of artificial intelligence by providing data, evaluation frameworks, and human expertise. With over 36 years of experience, the company delivers high-quality data solutions and services for Generative AI builders and adopters.
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.