Top Machine Learning Engineer Jobs in Seattle, WA

Design and productionize causal machine learning models, including uplift modeling and treatment-effect estimation. Analyze A/B tests and quasi-experiments, develop experimentation strategies, evaluate modeling tradeoffs, and build scalable infrastructure. Collaborate with product and engineering teams, conduct code reviews, maintain engineering standards, communicate technical insights, and mentor others.

Lead the technical vision and development of scalable ML and AI evaluation systems for Atlassian’s Rovo Chat. Build agentic capabilities, datasets, simulations, automated judges, experimentation platforms, and quality signals. Identify failure modes, ship interventions, and improve customer outcomes while balancing quality, latency, reliability, safety, and cost. Provide cross-organizational technical leadership, mentorship, design guidance, and strategic direction for AI products.

Lead design and development of scalable AI architectures and frameworks, collaborate with research and product teams, deploy and optimize ML/LLM systems in production, guide ML platform strategy, perform code reviews, mentor engineers, and evaluate emerging AI technologies for business impact.

As a Staff Machine Learning Engineer, you'll enhance credit products through advanced modeling techniques, data analysis, and ML operations, supporting robust decision-making processes.

Lead design, implementation, and scaling of end-to-end ML systems including feature pipelines, model training, evaluation, deployment, and monitoring. Partner with cross-functional teams to productionize models, apply MLOps best practices (automated retraining, CI/CD, drift detection), and mentor engineers to maintain high code and system quality.

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

Lead the delivery of ML initiatives, define roadmaps with cross-functional teams, and develop ML models for conversational agents and smart tools, enhancing customer support.

Design, train, and deploy ML and deep-learning models for sponsored ads: relevance, ranking, click-through prediction, auction and bidding optimization. Lead models from ideation through production, collaborate with product and engineering, set modeling standards, mentor junior scientists, and communicate results to leadership.

Build and operate reliable, scalable data pipelines and trusted data models for Layla’s AI-powered travel-planning and booking products. Own data quality, observability, experimentation, model-evaluation datasets, feature availability, and production monitoring. Partner with Product, Analytics, and ML teams to deliver dependable insights and support responsible AI/ML systems. Mentor engineers and promote testing, documentation, and reusable data-engineering practices.

Lead development and operation of scalable machine learning training platforms across AWS and Kubernetes. Optimize distributed GPU workloads, training performance, reproducibility, cost, and observability. Build CI/CD automation, standardized containers, governance, and self-service workflows for deep learning and generative AI training. Partner with platform and data engineering teams on security, access, and infrastructure standards. Lead responsible adoption of AI-assisted development tools and coach engineers on secure, compliant validation practices.

Design, build, and deploy large-scale machine learning models powering Snapchat features. Own full ML lifecycle from data analysis to production, prototype with cross-functional partners, apply modern ML techniques, and use AI tools while ensuring code correctness, security, and scalable system design.

Design, build, and deploy large-scale ML models powering core products; own full ML lifecycle from data analysis to production; partner cross-functionally to prototype and launch features; apply modern ML techniques and AI tooling while ensuring code quality, security, and scalable production services.

Design, build, and operate production ML models, services, and platforms for pricing, risk, and decision systems. Own end-to-end model pipelines, deployment, monitoring, and iteration. Collaborate with researchers and product to productionize prototypes, tackle data drift and sparsity, lead design reviews, and mentor teammates.

Design and deliver generative AI and computer vision models and on-device inference for consumer AR experiences. Prototype research advances, optimize models (compression, quantization, distillation), collaborate across teams, and productionize scalable inference pipelines for mobile and wearable platforms.

The Staff Machine Learning Engineer will lead ML initiatives, partnering with multiple teams to enhance customer support through AI-driven solutions, focusing on conversational agents and research in chatbot architectures.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

Designs, develops, and troubleshoots secure, scalable ML software systems and services. Builds production code, architecture artifacts, APIs, and distributed microservices; analyzes data to improve applications and system architecture. Uses AI-assisted development tools responsibly, validates generated outputs, and guides peers on secure engineering practices. Requires Python and ML framework expertise, cloud-native technologies, public cloud experience, distributed systems knowledge, and Cassandra or equivalent NoSQL experience. Preferred experience includes GPU workloads, model serving, distributed inference, model compression, and edge deployment.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Develops and productionizes deep learning and machine learning perception models for robotics and recycling sortation. Responsibilities include experimenting with neural network architectures, designing computer vision solutions, conducting statistical experiments, deploying successful models, collaborating with data, modeling, and cloud infrastructure teams, and improving ML infrastructure.

Lead applied research to advance continuous learning for agents: design and evaluate LLM post‑training and RL methods, implement and deploy experiments at scale, validate research on customer tasks, optimize GPU/distributed training, and mentor engineers while driving cross‑functional technical direction.

Drive applied research to enable continuous learning for agents: design and evaluate LLM post-training methods (fine-tuning, RL, distillation), run large-scale GPU experiments, validate approaches on customer tasks, and help deploy and scale models and infrastructure across the stack.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.