Role Description We are seeking an Autonomous Learning Engineer to design, train, and deploy reinforcement learning (RL) solutions for complex decision-making systems. The ideal candidate will have expertise in: - Python - Deep learning - Reinforcement learning - Simulation environments - RLHF - Distributed training Experience building scalable, production-ready AI solutions is essential. Key Responsibilities - Design and implement reinforcement learning solutions for real-world applications. - Develop and optimize simulation environments and RL training pipelines. - Implement and evaluate modern RL algorithms and reward models. - Improve model performance, training stability, and sample efficiency. - Collaborate with AI and product teams to deploy production-ready RL solutions. - Monitor deployed models and ensure reliability, safety, and continuous improvement. Qualifications - 6+ years of experience in reinforcement learning research and engineering. - Strong proficiency in Python and modern deep learning frameworks. - Experience with RL libraries, simulation environments, GPU-based training, and distributed learning. - Strong understanding of reinforcement learning, optimization, and probability. Preferred Qualifications - Experience with RLHF, multi-agent RL, robotics, autonomous systems, or control systems. - Publications or open-source contributions in reinforcement learning. Benefits - 100% Remote (U.S.) - Full-time, Direct W2 - Salary Range: $100,000–$150,000 Annually Application Instructions Interested in this opportunity? Apply today for immediate consideration! - Email your updated resume: [email protected] - Call or Text: (908) 505-3545 - Learn more: www.bvteck.com Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

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