Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. At Scale, our Enterprise team works with a variety of customers looking to be at the forefront of incorporating Generative AI capabilities into their services. Forward Deployed ML Engineers (FDMLEs) work directly with our customers to build and own robust, production-grade services which directly integrate into their products. This exciting role lies at the intersection of customer delivery and ML engineering, providing you with a wealth of experience and stimulating both sides of your brain. In this role, your daily tasks may include engaging in discussions with customers and understanding their generative AI needs and using platform tools and packages to finetune and iterate on modeling experiments using Large Language Models (LLM) or Retrieval Augmented Generation (RAG). Your experiments should be focused around data and metrics that drive continuous improvement. This includes designing data-driven experiments focused on optimizing model performance by deeply understanding the training data and model outputs to systematically move key metrics. If you are excited about shaping the future of the data-centric AI movement, we would love to hear from you!You will:Own, plan, and optimize our Enterprise customer's Generative AI problems, thereby becoming the ML voice in the room that our customers turn to for solutionsUnderstand the tools available for optimizing performance around LLMs and how to most appropriately apply or combine them in different scenariosBe analytically rigorous by asking probing questions of the data and results to root out model weaknesses Demonstrate strong proficiency for writing, testing, and debugging Python code, capable of solving programming problems such as basic algorithms and data structure manipulationsHave experience gathering business requirements and translating them into technical solutions Meet regularly with customer teams onsite and virtually, collaborating cross-functionally with all teams responsible for their data and ML needsHave strong communication skills and the ability to explain technical concepts to non-technical stakeholdersPush production code in multiple development environments, writing and debugging code directly in both our customer's and Scale's codebases.Deeply understand the AI strategy, goals, and needs of the customersBuild deep relationships with technical stakeholders at all levels and across all roles, both internally and externallyBe able and willing to multi-task and learn new technologies quicklyIdeally you'd have:Strong engineering background: a Bachelor's degree in Computer Science, Mathematics, or another quantitative field or equivalent strong engineering background.3+ years of engineering experience, post-graduation in a client-facing settingAt least 2 years of model training experience, specifically in translating business problems into data/model problems.Deep familiarity with a data-driven approach when iterating on machine learning models and how changes in datasets can influence model resultsExperience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environmentExperience operating in a fast-paced environment with ambiguityProficiency in Python to write, test and debug code using common libraries (ie numpy, pandas) and create functions to break down problems into modular components focused on robustness, readability and maintainabilityNice to haves:Strong knowledge of software engineering best practicesHave experience with AI platforms and technologies, including generative models and LLMsHave built applications taking advantage of Generative AI in real, production use casesFamiliarity with state of the art LLMs and their strengths/weaknesses
