Role Description We're looking for a Machine Learning Engineer to own and advance the forecasting and predictive modeling capabilities at the heart of the Camus platform. This is an individual contributor role with real technical depth and product influence; you'll be responsible for the full lifecycle of ML model development, from exploratory analysis and model design through to production deployment and monitoring. This is not a role where the problem statements are handed to you. You'll work directly with Camus’ teams and external stakeholders to understand their data, define the right questions, and translate messy real-world signals into reliable, production-grade data driven analytics. You'll bring that ground-truth perspective back into product decisions, and work closely within the Engineering team to integrate ML models into our planning and operational workflows. The forecasting and predictive modeling problems we're solving often don't have off-the-shelf answers. We work as a tight, technical team that moves with urgency but builds with the discipline that production-grade software demands. If you want to do the most technically interesting ML work in the clean energy space while directly shaping how it becomes a product, this is the role. What You'll Do - Design, train, and evaluate predictive ML models with a focus on forecasting and time-series applications - Conduct exploratory data analysis, feature engineering, and statistical modeling across large structured and unstructured datasets - Collaborate with Engineering to define ML infrastructure requirements, and deploy and integrate ML models into operational workflows and decision-support tools - Work cross-functionally with Camus teams to define problem statements and translate business objectives into ML solutions - Communicate model performance, uncertainty, and limitations clearly to both technical and non-technical audiences - Champion ML best practices around reproducibility, versioning, and testing Qualifications - PhD with 3+ years of industry experience, Masters with 5+ years, or Bachelors with 8+ years in Machine Learning, Statistics, Computer Science, Applied Mathematics, or a related quantitative field - Demonstrated track record of delivering ML models into production environments - Experience with time-series forecasting methods — including classical approaches (e.g. ARIMA) and modern ML-based methods (e.g. gradient boosting or temporal neural networks) - Strong proficiency in Python and core ML/data science libraries (PyTorch, scikit-learn, statsmodels, pandas, etc.) - Experience with probabilistic forecasting, uncertainty quantification and backtesting - Ability to translate ambiguous business problems into well-scoped ML projects - Comfortable operating with autonomy in a small team, balancing speed of delivery with the engineering discipline that production-grade software demands. Nice to Have - Experience in the energy sector — e.g. load forecasting, renewable generation prediction, price modeling or grid operations - Experience with MLOps tooling and infrastructure: cloud platforms, containerization, and model serving patterns - Experience with data pipeline tooling, e.g. Airflow, Spark, or Databricks - Able to leverage AI code development tools to accelerate development Benefits - Competitive base salary - Comprehensive benefits, including FSA and 401k for full time employees - Fully remote workplace with options for in office work in the Bay Area - Flexible PTO, which we encourage you to use! - A real impact on climate change - we’re building the world we want to live in and we want you to join us! - The expected base salary for this role is $180,000 - $230,000 annually, depending on experience, skills, and qualifications.

Role Description We're working to advance care through data-driven decisions and automation. This mission serves as the foundation for every decision as we create the future of travel. We can't do that without the best talent - talent that is innovative, curious, and driven to create exceptional experiences for our guests, customers, owners, and colleagues. We seek an extraordinary Machine Learning Engineer to help build the algorithmic assets and features that Hyatt guests, members, customers, and internal users leverage to transform the guest experience and drive efficiencies across the operations of our business. In this role, you will: - Design and implement algorithmic product architectures to bring our machine learning models to life across the full lifecycle of the product including data ingestion, ML processing, and results delivery/activation. - Work cross-functionally with various data science teams, data engineering teams, and data architecture teams. - Serve as both solutions architect and hands-on implementation engineer, guiding the team towards best-in-class algorithmic product implementations. - Be a part of a ground-floor, hands-on, highly visible team which is positioned for growth and is highly collaborative and passionate about data science. - Apply the latest techniques and approaches across the domains of data science, machine learning, and AI. Qualifications - 5+ years of implementing software product solutions in a cloud environment with a focus on algorithmic/machine learning products, hospitality experience not required. - Expertise in AWS cloud services. - Expertise in Python, SQL, PySpark, Docker. - Experience with streaming and batch data architectures at scale. - Experience operating in an Agile Methodology environment. - Experience with DevOps and CI/CD concepts. - Excellent communication and teamwork skills. - Position will not require customer-facing interactions. Requirements - Stay up to date with the latest design patterns and AWS services with respect to Machine Learning Engineering. - Partner with data architecture, data governance, and security teams to ensure solutions meet required standards. Company Description

• Contribute to the development of scalable machine learning platforms and workflows that enable scientists and researchers across Amgen to build, deploy, and manage AI/ML models • Work closely with experienced engineers, data scientists, and domain experts to productionize machine learning solutions primarily in support of drug discovery and development • Deliver AI and ML-enabled applications, with deployed models ranging from classical ML models to natural language processing, protein language models, and large language models • Contribute to the development and maintenance of ML platform capabilities, including: • Data pipelines and feature engineering workflows • Model training, evaluation, and deployment pipelines • Experiment tracking and model registry systems • Model performance evaluations and monitoring • Partner in implementation of AI and ML Ops best practices, including CI/CD, infra as code, monitoring, traceability and reproducibility • Collaborate with cross-functional teams to transition standards and outcomes from experimentation to production-grade enterprise solutions • Build and maintain scalable and efficient, productionized MLOps solutions on cloud platforms • Develop and deliver training content and knowledge articles to educate resident scientists on model lifecycle management best practices

Role Description Surest is a consumer centric health plan designed to remove barriers to care through simple, transparent experiences. Our plans replace deductibles and coinsurance with clear, up front pricing that members can see before they seek care—without compromising coverage, quality, or affordability. The Surest Technology team is 100% dedicated to this product and its success in the market. Our work focuses on building and evolving the platforms that power the Surest experience end to end, from benefit configuration and provider data to member facing digital experiences. We prioritize solutions that reduce manual effort, improve accuracy, and integrate seamlessly across a complex healthcare ecosystem. You will enjoy the flexibility to telecommute* from anywhere within the U.S. as you take on some tough challenges. - Daily work in the areas of AI/ML modeling, data-oriented software development & infrastructure, programming for data analysis, and data visualization - Development and ownership of measuring the effectiveness of our product - Share responsibility for the data collection, pipelines, and reports/dashboards that allow democratized access to data - Influence roadmap and priorities by contributing data driven recommendations - Communicate complex analytical findings clearly to expert and non-technical audiences - Build with solid foundations for repeatable analyses - Enable the Data Science team to review and ingest new data as it becomes available - Participate in cross-functional implementation of data science products - Work with stakeholders and others on assigned projects to ensure that value is being delivered at a regular pace - Work with SME’s to understand even the most nuanced context behind what is recorded in our data - Work with technical partners (e.g., Data Engineers) to ensure that assets developed for analytical/modeling purposes are stable, reliable, and reproducible - Contribute readable, maintainable, and testable code to ensure that datasets created and models trained will deliver expected results - Read and contribute to others’ code to help it meet the same standards - Propose new applications of Data Science to business problems, or help develop these proposals - Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI Qualifications - High School Diploma/GED - 3+ years of experience in Data Science, Software Delivery, or a related role OR a combination of experience and education - 1+ years of experience in writing Python and/or Pyspark for software or Data Analysis - 1+ years of experience in writing SQL for Data Analysis, or programmatic Database Interactions - 1+ years of experience working on a CI/CD software project - 1+ years of experience working with code version control (e.g., GitHub) Requirements - Experience contributing to a large, CI/CD software project - Experience taking a model from concept to evaluation to production - Experience with model explainability and bias/fairness evaluation tools and processes - Advanced understanding of Pyspark - Experience with cloud tools (Databricks, AWS) - Experience with software development/deployment tools: (Python IDE(s), Pytest, Jira, GitHub, Jenkins) - Experience with additional programming tools: (Shell scripts, Terraform, Docker) - Familiar with software development principles Benefits - Comprehensive benefits package - Incentive and recognition programs - Equity stock purchase - 401k contribution (all benefits are subject to eligibility requirements)