Applied AI Engineering Manager, Enterprise

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 8 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent Series F round, we're accelerating the usage of frontier data and models by building complex agents for enterprises around the world through our Scale Generative AI Platform (SGP). The SGP ML team works on the front lines of this AI revolution. We interface directly with clients to build cutting edge products using the arsenal of proprietary research and resources developed at Scale. As an AAI Engineering Manager, you'll manage a team of high-calibre Applied AI Engineers + MLEs who work with clients to train ML models to satisfy their business needs. Your team's work will range from training next-generation AI cybersecurity firewall LLMs to training foundation agentic action models making predictions about business-saving outcomes. You will guide your team towards using data-driven experiments to provide key insights around model strengths and inefficiencies in an effort to improve products. If you are excited about shaping the future of the modern AI movement, we would love to hear from you You will: Train state of the art models, developed both internally and from the community, in production to solve problems for our enterprise customers. Manage a team of 5+ Applied AI Engineers / ML Engineers Work with product and research teams to identify opportunities for ongoing and upcoming services. Explore approaches that integrate human feedback and assisted evaluation into existing product lines. Create state of the art techniques to integrate tool-calling into production-serving LLMs. Work closely with customers - some of the most sophisticated ML organizations in the world - to quickly prototype and build new deep learning models targeted at multi-modal content understanding problems. Ideally you'd have: At least 3 years of model training, deployment and maintenance experience in a production environment At least 1-2 years of management or tech leadership experience Strong skills in NLP, LLMs and deep learning Solid background in algorithms, data structures, and object-oriented programming Experience working with a cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment Experience building products with LLMs including knowing the ins and outs of evaluation, experimentation, and designing solutions to get the most of the models PhD or Masters in Computer Science or a related field Nice to haves: Experience in dealing with large scale AI problems, ideally in the generative-AI field Demonstrated expertise in large vision-language models for diverse real-world applications, e.g. classification, detection, question-answering, etc. Published research in areas of machine learning at major conferences (NeurIPS, ICML, EMNLP, CVPR, etc.) and/or journals Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kubeflow, TensorFlow, etc. Strong written and verbal communication skills to operate in a cross functional team environment Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $248,400—$310,500 USD PLEASE NOTE:Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. About Us: At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information. We comply with the United States Department of Labor's Pay Transparency provision. 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