RWE · 2 days ago

Machine Learning Scientist

Seattle, WA

Full-time

Hybrid

Senior Level

3+ years exp

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Clean EnergyEnergy

Growth Opportunities

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Responsibilities

Experiment, design, develop, and implement novel neural network architectures to build state-of-the-art diffusion models and generative models

Collaborate with domain experts to ensure that models meet the requirements and constraints of targeted applications

Collaborate closely with data scientists, software engineers and machine learning engineers to integrate models into scalable pipelines

Stay abreast of the latest advancements in machine learning technologies, cloud services, and software engineering best practices

Develop reusable components, libraries, and frameworks to enable experimentation with different algorithms and to assess the performance and robustness of machine learning models

Contribute to research advances in the broader community via conference presentations, publications, open source code and/or blog posts

Contribute to a team culture where diverse viewpoints, backgrounds and expertise are welcomed

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine LearningNeural NetworksNLPLinear AlgebraProbability TheoryOptimizationPythonJuliaPyTorchTensorFlowJaxAWSAzureGoogle Cloud PlatformContainerizationOrchestrationDockerKubernetesGitCI/CDInfrastructure-as-CodeProblem-SolvingCommunicationCollaborationTeamworkMeteorologyClimate ScienceEnergy SectorDistributed SystemsParallel Computing

Required

Ph.D. or Master’s degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field and 3+ years of relevant industry experience

Proven track record of research and publications in machine learning, particularly in the areas of diffusion models, generative models, NLP, or related topics.

Solid understanding of neural network fundamentals, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms.

Strong mathematical background, including proficiency in linear algebra, probability theory, and optimization.

Proficiency in programming languages such as Python and Julia, and experience with developing large scale models using machine learning packages (e.g., PyTorch, TensorFlow, Jax).

Experience with cloud computing platforms such as AWS, Azure, or Google Cloud Platform

Experience with deploying machine learning models in production using containerization and orchestration tools (e.g., Docker, Kubernetes)

Familiarity with version control systems (e.g., Git), continuous integration/continuous deployment (CI/CD) pipelines, and infrastructure-as-code tools

A passion for innovation and staying abreast of the latest research in AI methods relevant to our work in the Lab

Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams and contribute to a positive team climate