What you’ll do
Tackle unsolved applied AI and ML problems. Pushing the boundaries of post-training, voice AI, and long-horizon task execution, building 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. We welcome backgrounds across applied ML, including search, ranking, recommendations, advertising, language, and speech.
Excellent judgment in framing product problems as ML tasks and selecting the simplest & most effective approach.
Experience working deeply with data, experimentation, evaluation, and model iteration.
A practical, evidence-driven approach to ML, with strong judgement around when a problem calls for modeling and when a simpler solution will deliver the best outcome.
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.