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 solutions
• Understand the tools available for optimizing performance around LLMs and how to most appropriately apply or combine them in different scenarios
• Be 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 manipulations
• Have 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 needs
• Have strong communication skills and the ability to explain technical concepts to non-technical stakeholders
• Push 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 customers
• Build deep relationships with technical stakeholders at all levels and across all roles, both internally and externally
• Be able and willing to multi-task and learn new technologies quickly

Ideally 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 setting
• At 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 results
• Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
• Experience operating in a fast-paced environment with ambiguity
• Proficiency 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 maintainability

Nice to haves:

• Strong knowledge of software engineering best practices
• Have experience with AI platforms and technologies, including generative models and LLMs
• Have built applications taking advantage of Generative AI in real, production use cases
• Familiarity with state of the art LLMs and their strengths/weaknesses