Top Remote Senior Level Machine Learning Engineer Jobs in Seattle, WA

Build and deploy AI-powered, LLM-based solutions in client environments: implement integrations, develop production-ready code, support cloud deployments, prepare data and prompts, troubleshoot, and collaborate with client and cross-functional teams while documenting and transferring knowledge to enable sustainable production adoption.

Lead architecture and technical vision for integrations that ingest external data (e.g., Salesforce, HubSpot) into Jasper's IQ layer. Design scalable, secure data ingestion, define context primitives, drive cross-team alignment, and deliver staff-level initiatives that improve reliability and AI-driven product quality.

Serve as the primary technical owner for customer engagements, design and build integrations, write production-quality code, troubleshoot and ship scalable production solutions, capture precise product feedback, and partner with product and engineering to convert field learnings into roadmap improvements.

Develop production-grade multimodal AI systems for L3/L4 autonomous vehicles. Build sensor-fusion and spatiotemporal world models combining camera, LiDAR, radar, navigation, and vehicle-state data. Create multitask models for road understanding, occupancy, perception, prediction, and planning; develop scalable training and evaluation pipelines; investigate foundation-model approaches; optimize models for real-time NVIDIA hardware; and collaborate across AV engineering teams to deploy robust systems.

Design, optimize, and deploy large language model training and post-training pipelines. Improve model quality through fine-tuning, reinforcement learning, preference optimization, evaluation, and experimentation. Build PyTorch-based infrastructure, optimize distributed multi-GPU training, diagnose performance and convergence issues, and develop production-ready AI systems. Collaborate with researchers, infrastructure engineers, platform teams, and customers while contributing to open-source projects and reusable training capabilities.

Develop and optimize GPU kernels, compilers, runtimes, and distributed inference systems for large language model serving. Improve latency, throughput, reliability, and hardware efficiency through profiling and low-level performance optimization. Integrate scalable improvements into production systems, collaborate across model, infrastructure, compiler, and hardware teams, and mentor engineers. Principal-level contributors additionally lead cross-stack initiatives and create reusable performance capabilities across models and hardware generations.

Founding engineer responsible for architecting and building core product systems, writing scalable Python backend code, designing and maintaining AI/ML infrastructure, instituting best development practices (testing, CI/CD), collaborating on product roadmap, and mentoring future engineers.

Leads and develops an engineering team building, deploying, and operating machine-learning models and services for fraud prevention. Owns roadmap delivery, technical decisions, reliability, testing, monitoring, and stakeholder communication. Partners with data scientists, ML engineers, product, risk, and engineering teams. Drives responsible AI-tool adoption, recruits and coaches engineers, manages priorities, and ensures scalable, high-quality production systems.

Develop, optimize, and deploy scalable predictive ML models to inform content strategy, valuation, scheduling, and performance. Own end-to-end model lifecycle (feature engineering, training, evaluation, deployment, monitoring) and partner with analytics, data engineering, MLOps, and content teams to scale production solutions and influence ML infrastructure.

Build and maintain production AI/ML models and pipelines for an NDR platform; develop scalable, secure backend services; optimize detection algorithms and reduce false positives; integrate ML features into product architecture; mentor engineers; participate in code reviews, technical strategy, and documentation; operate cloud-native, containerized services and manage infrastructure as code.