Top Tech Jobs & Startup Jobs in Seattle, WA

Manage daily office operations, catering, supplies, maintenance, vendors, facilities, access, space planning, and building relationships for the Seattle office. Coordinate events with People Operations, assist with HR administration, support executive calendaring and travel, and help maintain information security policies. The role requires strong organization, communication, adaptability, and enthusiasm in a fast-paced environment.

Build robust, scalable frontend applications for Deploy’s data and enterprise products. Collaborate with design, product, and engineering teams to deliver high-quality user experiences, participate in product decisions, improve team efficiency, and secure code using OWASP practices. The role requires strong JavaScript, HTML, CSS, React, Redux, software design, testing, communication, and independent decision-making skills.

Leads and mentors an engineering team developing large-scale AI infrastructure. Responsibilities include setting roadmaps, participating in architecture and technical design, guiding the full software development lifecycle, delivering product milestones, ensuring quality, managing security practices and risks, and collaborating with product, machine learning, QA, sales, and recruiting teams.

Manage sales operations by analyzing key metrics, identifying funnel improvements, optimizing CRM and go-to-market processes, and delivering executive reporting. Partner cross-functionally with sales and marketing leaders to improve productivity, maintain data quality, and execute operational initiatives in a fast-changing AI startup environment.

Build, train, deploy, and optimize production-scale machine learning models and neural networks. Design scalable data pipelines, distributed training systems, backend improvements, and high-throughput inference solutions. Conduct metric-driven research, improve model accuracy and performance, maintain secure and high-quality code, mentor junior engineers, and collaborate cross-functionally. The role focuses on deep learning, computer vision or NLP, and taking ML products from concept through production.

Design, build, and improve scalable backend systems, REST APIs, microservices, data-processing systems, and distributed job-processing platforms. Write secure, maintainable, high-performance code; improve engineering practices and tooling; apply test-driven development; debug production issues; and participate in on-call support. The role requires expertise in distributed systems, backend APIs, relational databases, and Node.js, along with strong communication and problem-solving skills.

Develop and deploy production-scale machine learning models, from data gathering and neural network design through training, optimization, scalable deployment, and continuous performance improvement. Build highly available distributed systems and backend APIs, improve engineering standards and tooling, conduct metric-driven research, mentor engineers, collaborate cross-functionally, and help define product strategy and roadmaps.

Design, build, and improve scalable backend systems, including REST APIs, microservices, data-processing pipelines, and distributed job systems. Write secure, maintainable, high-performance code; contribute to engineering standards and tooling; practice test-driven development; debug production issues; and participate in on-call rotations. The role requires experience with highly available distributed systems, scalable backend APIs, technical leadership, and solving ambiguous, high-impact problems.

Leads and mentors machine learning engineers while setting team roadmaps, guiding technical architecture, and delivering production-scale models. Oversees the full ML lifecycle, including data collection, labeling, experimentation, training, testing, deployment, and quality measurement. Collaborates with product, engineering, DevOps, sales, recruiting, and labeling teams. Also manages security practices, risk, compliance, hiring, onboarding, and engineer development.

Develop and deploy deep learning models into production at scale. Responsibilities include designing neural networks, collecting and refining data, training and tuning models, building scalable data pipelines, deploying high-throughput systems, analyzing results, improving model performance, and collaborating with Backend, DevOps, and data-labeling teams. The role also includes applying secure coding practices and protecting information assets.