• Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems • Collect, analyze, and synthesize findings from data and build intelligent data-driven model • Write clean, efficient, and sustainable code • Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across growth, discovery, ads and search • Scope and independently solve moderately complex problems

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description AJU is building a pool of adjunct faculty to design and teach fully asynchronous, online courses to satisfy general education requirements. Courses include: - Life Science (e.g., Introductory Biology for nonmajors; with at home‑/virtual lab) - Physical Science (e.g., Earth Science, Physical Science survey, Chemistry or Physics for nonmajors; with at home‑/virtual lab) - General Mathematics (e.g., Mathematical Ideas, Statistics for Decision Making, College Algebra/Quantitative Reasoning) Faculty will collaborate with AJU’s academic leadership and instructional design team to align outcomes, assessments, and learning activities with Intersegmental General Education Transfer Curriculum (IGETC) standards and best practices for adult online learners. Key Responsibilities - Course Design & Delivery - Develop or refine a fully asynchronous Canvas course using backward design, inclusive pedagogy, and authentic assessments aligned to IGETC outcomes and AJU standards. - Implement online laboratory experiences for science courses (e.g., virtual simulations, remote lab kits, data analysis labs) that meet learning objectives and academic integrity requirements. - Create clear course modules with measurable objectives, short video microlectures, interactive content, and accessible materials (ADA/508 compliant). - Teaching & Student Support - Actively engage adult learners through timely announcements, discussion facilitation, and substantive, rubric guided feedback that motivates student success. - Hold consistent virtual office hours and be available via LMS messaging/email within published response-time standards. - Use data (e.g., LMS analytics, early alerts) to support persistence; submit grades and progress reports on schedule. - Assessment & Continuous Improvement - Align assessments to IGETC and course outcomes; maintain gradebook transparency. - Participate in course reviews, incorporate feedback, and collaborate with program leadership on iterative improvements. - Community & Compliance - Model AJU’s commitment to equity and inclusion; apply culturally responsive teaching practices supportive of diverse, adult, and returning learners. - Adhere to academic policies, student privacy (FERPA), and accessibility standards. Qualifications - Master’s degree in the teaching discipline (Biology, Chemistry, Physics, Earth Science, Mathematics/Statistics, or closely related field), or a master’s degree with at least 18 graduate credits in the discipline taught. - Demonstrated online or adult learner teaching experience (higher education preferred); experience in asynchronous modalities strongly preferred. - For science instructors: experience designing/teaching online labs using virtual simulations. Preferred Qualifications - Experience aligning general education courses to IGETC outcomes (Area 5: Physical and Biological Sciences with lab; Area 2: Mathematical Concepts and Quantitative Reasoning). - Record of excellence in engaging adult and returning learners (frequent, motivating interaction; constructive, rubric based feedback). - Community college or open access institution teaching experience; commitment to equity minded, inclusive pedagogy consistent with sample statements. - Experience with Learning Management Systems (Canvas by Instructure, preferred). - Familiarity with universal design for learning (UDL), OER curation, and accessible course authoring. Workload & Modality - Asynchronous online sections ≈ 16 weeks, with defined weekly cadence. - Standard student contact expectations (announcements, discussion facilitation, feedback within 72 hours, grading turnarounds, and virtual office hours). Compensation & Classification - Part‑time adjunct; compensation equal to $1,666.67 per credit. $5,000 stipend for each course designed. - This position is not benefits eligible. Application Materials - CV/Resume - Cover Letter outlining IGETC relevant teaching experience and online lab/math course design - Unofficial Graduate Transcripts (official required upon hire) - One sample of an online module or syllabus (with an example of an asynchronous lab or math assessment) - List of three references (no letters required at application) Equal Employment Opportunity & Accessibility AJU is an equal opportunity employer. We welcome applications from all qualified individuals and do not discriminate on the basis of race, color, national origin, age, marital status, sex, gender identity or expression, sexual orientation, disability, religion, ancestry, veteran status, or any other protected status. We are committed to providing reasonable workplace accommodations for qualified individuals with disabilities.

Role Description At E Source, we help utilities make sense of complexity in a rapidly changing landscape, and we’re looking for an AI / ML Engineer to help shape how that impact shows up in the world. Joining E Source as a Senior AI/ML Engineer is an exciting opportunity to work with a dynamic and talented team, leveraging your skills to drive meaningful impact in the utility industry. You’ll contribute to the next generation of AI and ML solutions that blend predictive modeling, optimization, and generative reasoning to empower sustainable decisions. As a Senior AI/ML Engineer at E Source, you’ll play a crucial role in our machine learning engineering team. Collaborating with data scientists and software engineers, you’ll contribute to the development of cutting-edge ML and AI products that support our mission of building a sustainable future with utilities. Your expertise will be instrumental in building tools and pipelines for developing and scaling machine-learning models, including emerging AI system design components and agentic architectures. You’ll work across a range of use cases—like electrification, network reliability, geospatial analysis, time series forecasting, image and text processing, and AI-driven decision systems—using both traditional ML and modern generative AI approaches. In this role, you will: - Collaborate with cross-functional teams to design, develop, and deploy scalable software products that incorporate machine learning and AI models. - Build reusable Python packages to support the implementation of ML/AI algorithms and data-processing pipelines. - Contribute to the design of AI systems, including components for retrieval-augmented generation (RAG), LLM integration, and agent-based workflows. - Develop agentic evaluation and monitoring frameworks to assess model reasoning, consistency, and fairness. - Evaluate database design and create optimized performance queries for efficient data processing and retrieval. - Break down complex MLE and AI tasks into manageable user and technical stories, ensuring efficient and effective implementation. - Ensure high-quality test coverage of ML code and participate in peer reviews to provide valuable recommendations. - Stay updated on the latest advances in machine learning engineering, generative AI, and AI system orchestration, and incorporate relevant practices into our workflows. - Contribute to continuous delivery and Agile development processes, adhering to best practices in ML and AI engineering. Qualifications - Master’s degree in computer science, software engineering, data science, or a related field (PhD preferred). - Minimum of 7 years of professional experience designing, developing, and deploying machine learning software products independently and collaboratively. - Strong programming skills in Python, with experience developing reusable packages and automation tools. - Familiarity with Databricks for scalable data processing and collaborative analytics. - Solid understanding of machine learning systems design concepts, including model lifecycle management, MLOps, and scalable inference. - Hands-on experience with cloud infrastructure (Azure, AWS, or GCP), containerization, and CI/CD pipelines. - Proficiency with distributed computing frameworks, machine learning packages, and both relational and nonrelational databases. - Familiarity with generative AI tools and frameworks (e.g., AutoGen, Hugging Face, LangChain, LangGraph, LlamaIndex) and their integration into enterprise pipelines. - Experience developing or evaluating agentic AI systems, AI orchestration, or AI-assisted decision-making workflows is an asset. - Excellent problem-solving and analytical skills, with the ability to break down complex tasks into actionable steps. - Strong communication and collaboration skills, with a track record of working effectively in cross-functional teams. - Knowledge or experience in the utility, power, or energy sectors is a plus. - Deep knowledge in Databricks tech stack for AI and data engineering is a plus. Benefits - Excellent insurance options, including medical, dental, and vision plans; company-paid life insurance; company-paid long- and short-term disability insurance; medical and dependent-care flexible spending plans, and paid parental leave. - A flexible time off (FTO) policy that provides paid time away from work, approved by your manager, while ensuring business needs, workload commitments, and appropriate coverage are maintained. - A 401(k)/RRSP plan with a 3% employer match. - The budgeted salary for this position is $115,000–$130,000 USD + annual bonus. Actual pay will be adjusted based on experience. - This role will be 100% remote, with infrequent travel (generally 1–2 times per year).

• Leading the exploration and application of Large Language Models and Generative AI, venturing into new areas within these fields • Translating the latest research into high-performing systems and models that can be practically applied to enhance user experiences • Help set the team's strategic direction, cultivating an environment that encourages innovation and professional growth • Actively engaging in all aspects of development, from ideation and experimentation to implementation and deployment • Collaborating with various teams and product managers to develop and implement ML based solutions, ensuring performance optimization and alignment with broader business goals