Summary Yelp engineering culture is driven by our values: we’re a cooperative team that values individual authenticity and encourages creative solutions to problems. All new engineers deploy working code their first week, and we strive to broaden individual impact with support from managers, mentors, and teams. At the end of the day, we’re all about helping our users, growing as engineers, and having fun in a collaborative environment. Yelp’s mission of connecting people with great local businesses requires the use of cutting-edge Machine Learning (ML) and Artificial Intelligence (AI) to scale across a vast and diverse base of users and businesses spanning various geographical locations. As a Staff-level ML Engineer on the Content Contributor Intelligence team, you will help build connections across millions of users and business listings. Your work will involve using cutting-edge industry tools, including neural networks (NNs), large language models (LLMs), and various embedding techniques for text, images, and videos. Additionally, you will apply traditional ML methods such as XGBoost and linear models to enhance our systems. You’ll be responsible for turning raw data into valuable signals and building ML systems end-to-end. This includes the full ML lifecycle from training models to deploying them in production, as well as contributing to the ML platforms these models rely on. This opportunity is fully remote and does not require you to be located in any particular state within the US. We welcome applicants from throughout the US. We’d love to have you apply, even if you don’t feel you meet every single requirement in this posting. At Yelp, we’re looking for great people, not just those who simply check off all the boxes. What you'll do: - Conduct end-to-end analyses, wrangling data via SQL or Python, to statistical modeling, to hypothesizing and presenting business ideas. - Mentor and guide junior engineers, fostering a culture of learning and technical excellence. - Work with large and complex textual and visual datasets. - Support the development and deployment of projects involving machine learned models for offline, batch-based data products as well as models deployed to online, real-time services. - Work in the contributor and visual intelligence team on text and visual understanding, along with fine tuning transformer models to derive embeddings for multiple input types - Productionize and automate model pipelines within Python services. - Drive and advocate adoption of best practices in ML development and operations, and mentor newer engineers in those practices. What it takes to succeed: - Experience developing and productionizing machine learning models, particularly in neural networks, computer vision and LLMs including their supported data pipelines. - Experience with machine learning using packages such as PyTorch, TensorFlow, Spark MLlib, XGBoost, and Sklearn. - Strong coding skills in Python or equivalent (Java, C++). - Solid understanding of engineering and infrastructure best practices. - The curiosity to uncover promising solutions to new problems, and the persistence to carry your ideas through to an end goal. - We highly value experience of working with LLMs, utilizing LLM APIs (OpenAI, Bedrock, etc), prompt engineering and evaluation. - A Bachelor’s Degree or an equivalent work experience is required What you'll get: - There are a variety of factors that go into determining a salary range, including but not limited to external market benchmark data, geographic location, and years of experience. Based on the anticipated level of experience we are seeking, we expect the compensation range for this role to be between $112,000 and $269,000. You may also be offered a bonus, restricted stock units, and benefits. - This opportunity has the option to be fully remote in all locations across the US. - You can find more information about Yelp's five star benefits here! Closing At Yelp, we believe that diversity is an expression of all the unique characteristics that make us human: race, age, sexual orientation, gender identity, religion, disability, and education — and those are just a few. We recognize that diverse backgrounds and perspectives strengthen our teams and our product. The foundation of our diversity efforts are closely tied to our core values, which include “Playing Well With Others” and “Authenticity.” We’re proud to be an equal opportunity employer and consider qualified applicants without regard to race, color, religion, sex, national origin, ancestry, age, genetic information, sexual orientation, gender identity, marital or family status, veteran status, medical condition or disability. Actual salary offered may vary based on multiple factors, including but not limited to, an individual's location and experience. We will consider for employment qualified candidates with arrest and conviction records, consistent with applicable law (including, for example, the San Francisco Fair Chance Ordinance for roles based in San Francisco, the Los Angeles County Fair Chance Ordinance for roles based in the unincorporated areas of Los Angeles County, and the California Fair Chance Act for roles based in California). Where required by law, a criminal background check will not be conducted until after a conditional offer of employment is made, and any evaluation of a candidate's criminal background check will be subject to an individualized assessment that takes into account the candidate's specific criminal records and the responsibilities and requirements of the particular role. We are committed to providing reasonable accommodations for individuals with disabilities in our job application process. If you need assistance or an accommodation due to a disability, you may contact us at accommodations-recruiting@yelp.com or 415-969-8488. Note: Yelp does not accept agency resumes. Please do not forward resumes to any recruiting alias or employee. Yelp is not responsible for any fees related to unsolicited resumes. #LI-Remote Recruiting and Applicant Privacy Notice

• Build and productionize reusable MLOps components supporting scalable and reliable ML workflows. • Establish strong ML lifecycle practices including experiment tracking, evaluation, and reproducibility. • Enable robust and monitored ML systems aligned with healthcare-grade reliability and compliance requirements. • Deliver reliable production inference workflows powering real-world outcomes for Neko members. • Partner across data, platform, and clinical teams to support scalable ML adoption across multiple use cases.

Join our team at Workiva as a Staff Machine Learning Engineer! As a pivotal member of our Machine Learning (ML) team, you'll spearhead the architecture and delivery of groundbreaking machine learning solutions across our platform. Your expertise will be instrumental in leading projects that demand innovative problem-solving, including the integration of cutting-edge Generative AI into our products. In this role, you'll have the chance to develop robust tools, systems, and infrastructure to bolster the development, monitoring, and management of our machine learning solutions. Leveraging your engineering prowess, you'll tackle challenges related to availability and scaling, ensuring the long-term stability of our systems. If you're passionate about pioneering the possibilities of Generative AI and want to be part of a team driving innovation at Workiva, we invite you to join us! Learn more about Workiva's Generative AI and be part of shaping the future of ML with us. What You’ll Do Architect and Develop Solutions - Architect and deliver cutting-edge ML solutions using MLOps and best practices, fostering creativity in project execution - Design systems to enable rapid ML development, high availability, and clear observability - Develop tools, systems, and automation to support ML solutions, ensuring efficiency, scalability, and rapid development Collaborate and Lead - Collaborate closely with product teams to develop APIs, maintain ML infrastructure, and integrate machine learning features into products - Provide technical leadership, mentor less experienced ML engineers and scientists, and define team best practices and processes - Lead in the ML space by introducing new technologies and techniques, and applying them to Workiva's strategic initiatives - Communicate complex technical issues to both technical and non-technical audiences effectively - Collaborate with software, data architects, and product managers to design complete software products that meet a broad range of customer needs and requirements Ensure Reliability and Support - Deliver, update, and maintain machine learning infrastructure to meet evolving needs - Host ML models to product teams, monitor performance, and provide necessary support - Write automated tests (unit, integration, functional, etc.) with ML solutions in mind to ensure robustness and reliability - Debug and troubleshoot components across multiple service and application contexts, engaging with support teams to triage and resolve production issues - Participate in on-call rotations, providing 24x7 support for all of Workiva’s SaaS hosted environments - Perform Code Reviews within your group’s products, components, and solutions, involving external stakeholders (e.g., Security, Architecture) What You’ll Need Required Qualifications - Bachelor’s degree in Computer Science, Engineering or equivalent combination of education and experience - Minimum of 4 years in ML engineering or related software engineering experience - Proficiency in ML development cycles and toolsets Preferred Qualifications - Familiarity with Generative AI - Strong technical leadership skills in an Agile/Sprint working environment - Experience building model deployment and data pipelines and/or CI/CD pipelines and infrastructure - Proficiency in Python, GO, Java, or relevant languages, with experience in Github, Docker, Kubernetes, and cloud services - Proven experience working with product teams to integrate machine learning features into the product - Experience with commercial databases and HTTP/web protocols - Knowledge of systems performance tuning and load testing, and production-level testing best practices - Experience with Github or equivalent source control systems - Experience with Amazon Web Services (AWS) or other cloud service providers - Ability to prioritize projects effectively and optimize system performance Working Conditions - Less than 10% travel - Reliable internet access for remote working opportunities How You’ll Be Rewarded ✅ Salary range in the US: $163,000.00 - $261,000.00✅ A discretionary bonus typically paid annually ✅ Restricted Stock Units granted at time of hire ✅ 401(k) match and comprehensive employee benefits package The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors. Employment decisions are made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic. Workiva is committed to working with and providing reasonable accommodations to applicants with disabilities. To request assistance with the application process, please email talentacquisition@workiva.com. Workiva employees are required to undergo comprehensive security and privacy training tailored to their roles, ensuring adherence to company policies and regulatory standards. Workiva supports employees in working where they work best - either from an office or remotely from any location within their country of employment. #LI-MJ2

• Design and build AI- and machine learning–enabled performance intelligence systems that continuously analyze operational performance data and identify emerging risks, degradation patterns, and improvement opportunities. • Design and implement analytical services, pipelines, and tooling in Python that incorporate AI/ML methods and transform operational data into continuously updated performance intelligence. • Build cloud-deployed analytical tools and services that enable automated or semi-automated detection of performance issues tied to contractual Service Level Requirements (SLRs). • Translate messy operational challenges into practical analytical solutions, combining statistical methods, machine learning techniques, and domain-informed logic. • Engineer reusable analytical capabilities, frameworks, and software components that strengthen the team’s long-term ability to diagnose and improve operational performance. • Collaborate with performance analysts, engineers, and program stakeholders to frame problems and design data-driven approaches to improving program outcomes. • Investigate systemic performance issues and engineer tools that surface root causes, prioritization signals, and improvement opportunities. • Communicate technical insights and analytical findings clearly to both technical teams and program leadership. • Support broader Navy performance initiatives by extending analytical methods and tooling beyond individual SLR use cases when appropriate.