Research Engineer, Applied Research

About Sierra

Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia.

Sierra’s Applied Research team advances the quality, speed, and cost efficiency of the models powering our agents across a wide range of tasks. Our work spans post-training open-weight models for specialized capabilities and pioneering new approaches to voice AI, turning research breakthroughs into reliable production systems.

What You’ll Do

• Tackle unsolved applied AI and ML problems, pushing the boundaries of post-training, voice AI, and long-horizon task execution to build agents that can reason, adapt, and reliably carry out complex tasks for customers.
• Determine when to build specialized models and when to use externally provided frontier intelligence, balancing quality, speed, and cost to deliver customer value.
• Turn ambiguous product and customer problems into well-framed machine learning problems.
• Develop, fine-tune, evaluate, and deploy models that improve Sierra’s customer-facing AI agents.
• Advance our post-training work, from supervised fine-tuning toward reinforcement learning and long-horizon optimization.
• Build rigorous evaluations that connect model performance to real-world agent quality.
• Work with large-scale, proprietary datasets spanning text and audio interactions.
• Collaborate across Sierra while taking ideas from experimentation through production deployment and customer impact.

What You’ll Bring

• Strong experience building and deploying production machine learning and AI systems (applied ML backgrounds including search, ranking, recommendations, advertising, language, and speech are welcome).
• Excellent judgment in framing product problems as ML tasks and selecting the simplest and most effective approach.
• Experience working deeply with data, experimentation, evaluation, and model iteration.
• A practical, evidence-driven approach to ML, with strong judgment around when a problem calls for modeling versus a simpler solution.
• The ability to operate with high agency and work across the stack when needed to deliver an outcome.
• Alignment with Sierra’s values, including Craftsmanship, Customer Obsession, and Competitive Intensity.
• A degree in computer science, machine learning, or a related field, or equivalent professional experience.

Even Better

• Experience with supervised fine-tuning or reinforcement learning.
• Experience in search, ranking, recommendations, or advertising systems.
• Experience training and deploying language, speech, or multimodal models.
• Experience designing model evaluations tied to real-world product outcomes.
• Experience working with large-scale consumer datasets or high-volume ML products.
• Contributions to applied ML research or benchmarks.