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Senior AI infrastructure engineer at Vantaca/HOAi who owns the systems, evaluations, and feedback loops behind millions of production AI tasks — building TypeScript/Node.js services for model routing, retries, and observability, and running experiments to maximize successful task completion per dollar across LLM providers.
Senior PM owning the AI model portfolio for Cerebras' ultra-fast inference platform — deciding which frontier and open-source models ship, partnering with AI labs, defining quality benchmarks, and leading launches. Requires deep PM experience plus Python/LLM ecosystem knowledge (PyTorch, Hugging Face, vLLM, SGLang).
Productionizes and optimizes Cerebras's GPU-accelerated AI inference serving stack in Toronto — building the GPU prefill path across custom inference APIs, vLLM, PyTorch, and AMD ROCm on rack-scale GPU infrastructure — while driving reliability, numerical correctness, and performance (time-to-first-token, throughput, tail latency).
ML engineer on the technical staff who owns the training and inference infrastructure that turns the company's AI research into production-scale systems: distributed training jobs, GPU scheduling, and model serving with vLLM/SGLang. Core stack includes PyTorch, Ray, GPU infrastructure, and cloud tooling (GCP/AWS, Terraform).
Staff engineer who productionizes and optimizes Cerebras's GPU-accelerated LLM inference serving stack — building the prefill path across custom inference APIs, vLLM, PyTorch, and the AMD ROCm stack on a rack-scale GPU fleet, owning reliability, numerical correctness, and performance (time to first token, throughput, tail latency).
Senior Solutions Architect on NVIDIA's Hospitality & Travel solutions team, guiding hotel and transportation customers in adopting NVIDIA's full-stack Gen AI platform. Day to day: hands-on technical mentoring, building Gen AI demos/POCs, deploying and optimizing LLM inference, and delivering workshops. Core tech: LLMs, PyTorch/TensorFlow, vLLM/TRT-LLM, GPU clusters, Docker/Kubernetes.
Senior ML Engineer at Osavul, whose AI-driven platform helps governments and NGOs detect hybrid threats. Day to day: build production NLP/information-extraction pipelines and knowledge graph architecture, combining classic ML/NLP with modern language models. Core stack: Python, FastAPI, Docker, Neo4j, distributed pipelines.
A 12-week on-site research internship at Microsoft Research NYC in machine learning and AI. The intern conducts research (e.g., RL for language models, science of deep learning, interpretability, reasoning, test-time scaling) with a mentor, collaborates with other researchers, and presents findings. Requires current PhD enrollment; experience with PyTorch, HuggingFace Transformers, and vLLM is pre
Senior Data Scientist in Washington DC (onsite 5 days/week) building proprietary foundation models and multi-agent AI systems for fintech use cases. Day-to-day involves LLM fine-tuning (PEFT/RLHF), agentic orchestration with RAG, and synthetic data engineering using tools like PyTorch, LangGraph, and LangChain.
Mid-level DevSecOps engineer on IDT's internal platform team that builds and maintains a secure software factory for defense software delivery. Day to day: designing Jenkins CI/CD pipelines, writing IaC with Ansible/OpenTofu, managing GitLab/Artifactory/SonarQube and Podman container clusters, and automating compliance (DISA STIGs, NIST 800-171, RMF) in air-gapped environments.
A hands-on senior engineer who designs, builds, and optimizes Recho's inference framework for speech models (ASR/TTS/speech-to-speech) and LLMs, owning throughput and GPU cost. Day to day: tuning vLLM/SGLang inference, dynamic batching for variable-length audio, async pipelines, and GPU orchestration with Kubernetes, Slurm, and Ray using Python and PyTorch.
Orcrist Technologies is seeking an Infrastructure Engineer to design, build, and operate bare-metal server fleets and data-center networking for their high-performance data intelligence platform. The role involves hands-on hardware commissioning, automation, and supporting GPU-based inference in both on-prem and air-gapped environments.
Designs and builds AI/ML infrastructure (NVIDIA Blackwell GPU cluster, hybrid cloud, data pipelines) for healthcare, ensuring HIPAA/HITRUST compliance and scaling pathology imaging workloads.
The ML Engineer will productionize NLP, audio, and document models for the Orcrist Intelligence Platform using Kubernetes, Triton, and KServe. The role involves building evaluation pipelines, managing inference infrastructure, and collaborating with research teams to deploy models for defense and enterprise clients.
Doctolib is hiring a Senior Agents Platform Engineer to build and run the internal platform that lets all product teams deploy production-grade, secure AI agents. Day to day: owning a declarative agent registry, a LiteLLM-based GenAI gateway, and observability/eval infrastructure using Python, Kubernetes, Google ADK, and multi-cloud LLM infrastructure.
Build and scale data pipelines that tag petabytes of autonomous-truck logs, turning raw sensor data into curated driving scenarios for ML and simulation teams using Python, Databricks, AWS, and CI/CD.
Senior/staff engineer on Qualcomm's Cloud AI team building a scalable LLM inference platform on their AI acceleration hardware: serving optimization (vLLM, SGLang, Triton, KV-cache, parallelism), kernel and workload profiling, and open-source contribution. Requires strong Python/PyTorch and distributed-systems skills.
Docusign is hiring an MLOps Engineer in Dublin to build and run its machine learning lifecycle and deployment platform: CI/CD/CT pipelines for models, monitoring for drift and performance, Kubernetes-based training/inference clusters, and productionizing LLM/GenAI systems. Core stack includes Python, Docker/Kubernetes, MLflow, Terraform, AWS/Azure/GCP, and Prometheus/Grafana.
Forward Deployed Engineers embed with clients to deploy production-grade AI systems using SeekrFlow, specializing in LLM fine-tuning, agentic workflows, and Kubernetes-based deployments across cloud/on-prem environments.
Build and operate next-generation AI/ML platforms (DevOps) for the de facto central bank and primary financial regulator of Hong Kong, handling monitoring, logging, deployment, and API integrations. Core stack: Python, Docker, Kubernetes, Terraform, Prometheus/Grafana, MLflow, and public cloud (AWS/Azure/AliCloud).
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