Staff Applied AI Scientist

About the Role:

In this role, you'll be responsible for designing, building, evaluating, and shipping products that use proprietary and open source LLMs. You'll be responsible for all aspects of the AI development lifecycle: framing the problem, designing the system, making decisions on frameworks, building, evaluation, and deploying. As an Applied AI Scientist, you'll own the customer impact end-to-end.

About the Team:

We're looking to accelerate AI investments across the business to solve customer pain points, and are growing our Applied AI team. You'll be a part of the core group of experts exploring big, innovative bets, quickly prototyping solutions and shipping to customers. You're also responsible for quickly iterating on improvements and eventually scaling to production-grade AI applications. You'll be working alongside a multidisciplinary group of Product Engineers, PMs, and Design.

Here's what you'll do day-to-day:

• Design, develop and deploy innovative AI solutions
• Improve LLMs through pre-training, fine-turning, and RL
• Build evaluation for LLMs and LLM based products, and assess best methods required to build LLMs suited for purpose
• Collaborate with the engineering team to determine technical requirements and SLAs for API-enabled services
• Research novel AI techniques and strategies that create magical customer experiences

Here's what we're looking for:

• At least 6+ years of software engineering experience (Python, Ruby, or Java), 8+ years of ML experience, and 3+ years designing and shipping Gen AI models
• Proven ability to design and prototype AI solutions quickly
• Experience working on AI agent systems to automate end to end workflows
• Experience building, deploying and maintaining LLM-based applications in production

Our cash compensation amount for this role is targeted at $245,000-$272,000 in Denver, $267,000-$296,000 for Los Angeles and most remote locations, $288,000-$321,000 for San Francisco, New York, and Seattle. Final offer amounts are determined by multiple factors including candidate experience, level, and expertise and may vary from the amounts listed above.