Top Remote Machine Learning Jobs in Spokane, WA

Design, build, and manage distributed services and pipelines for Underwriting & Credit at Cash App. Lead cross-team projects, ensure high code quality, collaborate with various teams, and contribute to development capabilities through mentoring.

As a Generative AI Scientist at Mission Cloud, you will leverage generative AI models to build internal products, collaborate with teams, and contribute to groundbreaking AI solutions. Responsibilities include designing and deploying AI models, collaborating with interdisciplinary teams, and staying updated on AI trends. Requirements include experience in AI model implementation, programming skills in Python and AI frameworks, and AWS certifications. This remote position offers a rewarding package and career development opportunities.

Design, develop, and program methods, processes, and systems to analyze big data sources and generate actionable insights. Collaborate with product and service teams to identify questions and issues for data analysis and experiments. Develop software programs, algorithms, and automated processes to cleanse and evaluate large datasets. Communicate insights and findings to stakeholders.

Design, build, and enhance batch and real-time inference services and tooling that support machine learning use cases. Collaborate with ML modelers to encourage adoption of new tools and technologies and plan for future needs of the ML teams.

As a Staff Machine Learning Engineer at Striveworks, you will be responsible for leading machine learning projects, developing ML models and analytics solutions, automating data pipelines, and managing project execution teams. This is a customer-facing role with a remote work environment and some travel required. The anticipated base pay range is $200,000–$250,000/year.

Deepgram is seeking an experienced Research Scientist to work on building models in the voice AI domain. The role involves developing accurate and performant voice AI models using deep learning techniques. The candidate should have experience in areas such as automatic speech recognition, text-to-speech, diarization and speaker identification, language detection, and code switching. They should also have expertise in speech data curation, neural network architectures for speech, distributed training, and model optimization.

Work as a machine learning engineer in the Applied AI and Machine Learning team. Contribute to core recommender systems and machine learning models. Opportunity to work on a large-scale machine-learning system and continuously learn and improve the solution.