• Architect, build, and deploy large-scale ML systems powering recommendations, search, messaging, and content understanding • Lead projects from ideation → modeling → experimentation → production → iteration • Design and improve recommender systems and ranking models across surfaces (feed, search, notifications) • Optimize for user engagement, discovery, and long-term value • Build next-gen AI-powered search and recommendation experiences, including LLM-integrated systems • Develop pipelines that help users find high-quality answers and content across Reddit’s corpus • Build and optimize content embeddings and representation models for users, communities, and content • Leverage and advance LLMs and multimodal models for deeper understanding and personalization • Evaluate model performance, improve accuracy, and reduce bias • Partner with Product, Data Science, Infra, and UX teams to solve complex problems • Translate ambiguous business needs into scalable ML solutions • Mentor engineers and raise the bar across the organization • Establish best practices for ML development, experimentation, and responsible AI • Act as a thought leader across teams and domains

• Design, build, and operate Samsara’s end-to-end ML platform spanning training, experimentation, batch and online inference, and edge deployment • Partner with product and applied ML teams to design, launch, and iterate ML-powered features driving measurable improvements in safety outcomes, feature reliability, and cost efficiency • Lead throughput and cost estimation for new ML features, informing roadmap and go/no-go decisions • Collaborate on experiment design and evaluation • Evolve shared training and experimentation infrastructure and standardize experiment tracking, evaluation, and regression testing • Design and operate scalable online and batch inference systems, including deployment patterns, observability, and SLOs • Partner with firmware and edge teams to define workflows for packaging, validating, and deploying models • Own the reliability, observability, and security posture of ML systems • Provide Staff+/Senior-Staff-level technical leadership • Drive strong developer experience through documentation and best practices • Own or co-own end-to-end technical delivery for high-priority initiatives.

Job Description: A Machine Learning Engineer helps our learners discover content that is relevant to their interests and goals, providing them with experiences tailored just to their needs, and ensure they are receiving the right communication and assistance with their journey at the right time. They develop robust, scalable production machine learning algorithms and recommendation systems. Who you’re committed to being: - You enjoy learning and are open to new ways of doing things. - You are not afraid to be yourself, experiment, make mistakes and learn from them, ask questions, or voice your concerns. - When communicating you are self-aware, insightful, and proactive. What you’ll do: - Work closely with Data Scientists to take prototype algorithms and models and turn them into customer-facing solutions - Build solutions to novel problems in software development, data engineering, and machine learning - Build data pipelines; transform and convert data streams into structures needed for algorithm input - Collaborate with Product Managers and UX Designers to better understand the customer, provide valuable input into functional design and usability - Evaluate the efficiency of user experiences and ML algorithms, determining what data is needed and how to collect it, with an understanding of how these metrics are connected with the desired outcomes Experience you’ll bring: - Experience building production machine learning systems services as part of a product development team - Experience in Python, TensorFlow, and proficiency in data structures and database fundamentals - Experience building good code with speed and efficiency and balancing the trade-offs of quality vs speed - Ability to explain machine learning solutions to developers and other team members, regardless of their technical background - Experience working with continuous integration and delivery - Familiarity with cloud platforms and deploying ML models as scalable services - Experience with containerization and orchestration tools such as Docker and Kubernetes - Knowledge of software design patterns and best practices, including RESTful API design - Ideal experience in using machine learning in the context of recommendations and/or personalization. - Experience with Node and Typescript preferred Requirements: - Requires a minimum of 5 years of related or equivalent experience; or 3+ years with an advanced degree - This is a remote role; however, applicants located within 45 miles of our Westlake/Dallas, TX office should expect to work on-site Tuesday through Thursday, with remote flexibility on Mondays and Fridays. This approach enables more effective collaboration, quicker decision-making, and a stronger culture, while still providing flexibility. Why you’ll love working here: - We work in a blended environment that supports collaboration, flexibility, and connection across teams. - We are mission-driven, shaping the future of tech upskillling and delivering impact that matters. - We foster a culture of inclusion and belonging, where everyone can contribute and thrive. - We are always learning, creating an environment where you can take on new challenges, expand your skills, and grow with purpose. - Benefits include competitive compensation, bonus eligibility, comprehensive medical coverage, unlimited PTO, wellness reimbursement, professional development funds, and more. About us: Pluralsight provides the only learning platform dedicated to accelerating the technology skills and capabilities of today’s tech workforce. Thousands of companies, government organizations and individuals around the world rely on Pluralsight to support critical technology skill development in areas that are crucial to innovation including artificial intelligence, cloud computing, cybersecurity, software development, and machine learning. We offer highly curated content developed by vetted technology experts, industry leading skill assessments, and hands on, immersive learning experiences designed to help individuals skill-up faster. Physical Requirements: This role is primarily performed in an office or home office setting and involves standard computer-based work. EEOC & Accommodations Statement: Bring yourself. Pluralsight is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or veteran status. We also consider qualified applicants with criminal histories, consistent with EEOC guidelines and local laws. If you need an accommodation to apply, interview, or perform essential job functions, please visit the bottom of our website to learn how to request an accommodation. Learn more about our commitment to diversity, equity, inclusion, and belonging in our DEIB Report. Pay Transparency: The annual US base salary range for this role is $121,600 - $160,000 USD. Actual compensation will depend on location, skills, experience, and other factors. Additional benefits and bonuses may apply. Pay Transparency: The annual US base + variable range for this role is $[OTE min] - $[OTE mid] USD. Actual compensation will depend on location, skills, experience, and other factors. Additional benefits and bonuses may apply. Applications must be submitted within 90 days after the initial posting date to be considered. Recruiting Scam Notice: Please be aware of recruiting scams. We’ll only contact you from an @pluralsight.com email or verified channels. We never ask for sensitive personal info or payments as part of the hiring process. All openings are posted on our Careers page. #LI-DB1

• Build and improve inventory demand forecasting models using ML and statistical methods. • Own ML models end-to-end: data collection → feature engineering → training → deployment → monitoring → iteration. • Develop decision systems that support inventory planning, pricing, and demand decisions. • Build and maintain data pipelines and API integrations for external and internal data sources. • Work with messy real-world data to ensure model reliability through rigorous validation and testing. • Implement LLM/AI-agent workflows to translate domain logic into automated processes. • Operate independently in a small team, setting priorities, unblocking challenges, and communicating tradeoffs clearly.