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Active Learning Jobs (NOW HIRING)

Deepgram

Engineering Manager, Active Learning

$180K - $220K/yr

Thrive in a fast-paced, impact-driven environment where learning new skills on-the-fly is not only encouraged but a regular necessity * Enjoy balancing decisions about product and feature maturity to ...

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How much do active learning jobs pay per hour?

As of Jul 5, 2026, the average hourly pay for active learning in the United States is $40.70, according to ZipRecruiter salary data. Most workers in this role earn between $29.57 and $52.88 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Active Learning Specialist, and why are they important?

To thrive as an Active Learning Specialist, you need a solid background in instructional design, educational theory, and teaching methodologies, often supported by a degree in education or a related field. Familiarity with learning management systems (LMS), digital collaboration tools, and assessment platforms is typically required. Creativity, strong communication, and adaptability are vital soft skills for engaging learners and fostering interactive environments. These competencies are crucial for effectively designing and facilitating learning experiences that promote deep understanding and learner participation.

What is the difference between Active Learning vs Data Analyst?

AspectActive LearningData Analyst
Required CredentialsTypically a degree in education, psychology, or related fieldsBachelor's or master's in statistics, data science, or related fields
Work EnvironmentEducational settings, training programs, corporate learning environmentsBusiness, finance, healthcare, or tech industries analyzing data
Employer & Industry UsageSchools, universities, corporate training departmentsCompanies, consulting firms, research organizations
Common Search & Comparison IntentUnderstanding teaching methods, training strategiesAnalyzing data, generating reports, insights

Active Learning focuses on engaging learners through interactive methods in educational or training settings, while Data Analysts interpret data to inform business decisions. Both roles require analytical skills but serve different industry needs and environments.

What is active learning?

Active learning is a teaching approach that engages students directly in the learning process through activities and discussion, rather than passively receiving information from a lecturer. It often involves problem-solving, group work, or hands-on activities that encourage critical thinking and deeper understanding. In the context of machine learning, active learning refers to algorithms that interactively select the most informative data points to label, improving model performance with fewer labeled examples. Both uses of the term aim to make learning more efficient and effective by focusing on engagement and targeted effort.

What are some common challenges faced by professionals in Active Learning roles, and how can they be addressed?

Professionals working in Active Learning roles often face challenges such as ensuring consistent student engagement, adapting instructional strategies to diverse learning styles, and keeping up with evolving educational technology. To address these, it's important to regularly solicit feedback from participants, collaborate with colleagues to share best practices, and participate in ongoing professional development. Additionally, leveraging data analytics can help tailor approaches to meet learners' needs more effectively, fostering a more inclusive and dynamic learning environment.
More about Active Learning jobs
What states have the most Active Learning jobs? States with the most job openings for Active Learning jobs include:
What job categories do people searching Active Learning jobs look for? The top searched job categories for Active Learning jobs are:
Active Learning jobs near you
Applied ML Scientist - Active Learning

Applied ML Scientist - Active Learning

Hexion, Inc.

Columbus, OH • On-site

Full-time

Posted 11 days ago

Job description

Company Overview
Imagine Everything. Build the Future with Hexion.
At Hexion, we push boundaries, rethink possibilities, and create real impact. We activate science to deliver progress-developing breakthrough solutions that strengthen industries, protect communities, and drive a more sustainable future.
This is where bold thinkers, problem-solvers, and innovators come together to shape what's next. Whether you're engineering advanced materials, transforming manufacturing technologies, or leading strategic innovation, your ideas and actions leave a lasting mark. We cultivate an inclusive culture of growth, collaboration, and accountability, ensuring every contribution propels us forward.
We don't follow the status quo-we challenge it, disrupt it, and improve it. Every role at Hexion is part of something bigger.
We invest in innovation, sustainability, and continuous development-equipping you with the tools, training, and opportunities to excel. With an unwavering commitment to safety, partnership, belonging, and impact, we empower you to lead change and strengthen industries worldwide.
Your Future Starts Here.
If you're ready to push limits, reimagine what's possible, and create the extraordinary, Hexion is where you belong.
Anything is possible when you imagine everything.
Job Responsibilities
  • Lead the design and execution of optimization and active-learning campaigns across chemistry, formulation, and process development.
  • Collaborate with R&D and manufacturing on framing optimization problems with design spaces, decision variables, objectives, and hard and soft constraints.
  • Design and coordinate sequential experiment campaigns using Bayesian optimization and active learning, accounting for operational variabilities and constraints.
  • Select and maintain surrogate models for acquisition, using model uncertainty to drive the search and respecting each model's domain of validity.
  • Drive closed-loop optimization that connects surrogate models to experimentation, with emphasis on decision quality, exploration versus exploitation, and actionable recommendations.
  • Partner with ML engineers, software engineers, and process engineers to deploy and monitor optimization systems.
  • Explore and adopt emerging ML methods, including LLM and agentic approaches, to advance optimization.
  • Communicate methods, results, and their limitations clearly to technical and non-technical audiences.

Minimum Qualifications
  • Bachelor's degree in Computer Science, Data Science, Statistics, Operations Research, or a related field, with substantial relevant experience in ML modeling or optimization experience for chemistry, formulation, process, or manufacturing problems.
  • 7+ years experience.
  • Demonstrated expertise in Bayesian optimization and Gaussian processes, including kernels, acquisition functions, and batch, multi-objective, and constrained settings.
  • Experience designing and running experiment campaigns or closed-loop optimization.
  • Experience in applied statistics and uncertainty quantification, with emphasis on calibrated posteriors that drive acquisition.
  • Strong Python skills and experience with mainstream Python-based ML and Bayesian optimization frameworks and tools.
  • Active use of AI-assisted coding and other AI tools in daily work, with familiarity with emerging ML methods including LLM and agentic approaches.
  • Strong communication, collaboration, and stakeholder management skills for working with R&D, manufacturing, and business teams.

Preferred Qualifications
  • Master's degree in Computer Science, Data Science, Statistics, Operations Research, or a related field.
  • Experience with active learning and physics-informed approaches for optimization in chemical synthesis, formulation, or process development.
  • Experience working with manufacturing, process, quality, or plant data, including issues such as batch-to-batch variability, raw-material variability, model drift, and changing operating conditions.
  • Familiar with ML engineering core tasks and techniques, such as data and optimization pipelines, model deployment, and MLOps.
  • Knowledge of chemistry ML core areas such as cheminformatics, molecular representation, predictive modeling, and chemistry foundation models.

Other
We are an Equal Opportunity, Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to gender, pregnancy, race, national origin, religion, age, sexual orientation, gender identity, veteran or military status, status as a qualified individual with a disability or any other characteristic protected by law.
To be considered for this position candidates are required to submit an application for employment through our career site and, be at least 18 years of age. Any offer of employment will be conditioned upon successful completion of a drug test and background investigation, as well as authorization for the Company to conduct additional periodic background checks as required by the Chemical Facility Anti-Terrorism Standards (CFATS) or regulations adopted by the department of Homeland Security or other regulatory agencies. A prior criminal record is not an automatic bar to employment, and the Company will conduct an individualized assessment and reassessment, consistent with applicable law, prior to making any final employment decision.

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