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Senior Machine Learning Scientist
- San Jose, Arizona, United States
- San Jose, Arizona, United States
Über
Responsibilities
Design, build, and evaluate multi‑step agentic AI systems, including autonomous agents capable of planning, tool use, memory management, and multi‑agent collaboration.
Research and implement state‑of‑the‑art techniques in agentic architectures, such as ReAct, reflection loops, chain‑of‑thought prompting, and tool‑augmented reasoning.
Develop and maintain agent orchestration frameworks, defining how agents decompose tasks, delegate to sub‑agents, and handle failure and recovery.
Integrate large language models (LLMs) with external tools, APIs, databases, and code execution environments to enable real‑world task completion.
Define and own evaluation frameworks for agentic systems, measuring task success, reliability, latency, cost, and safety across diverse benchmarks and production scenarios.
Collaborate closely with product, engineering, and research teams to translate business requirements into agentic system designs and deliver production‑grade solutions.
Identify and mitigate risks specific to agentic systems, including prompt injection, unintended actions, hallucination in long‑horizon tasks, and unsafe tool use.
Stay current with the rapidly evolving agentic AI landscape, synthesizing academic research and industry developments to inform the team’s technical direction.
Mentor junior ML engineers and scientists, providing technical guidance on agentic design patterns, LLM best practices, and experimentation methodology.
Minimum Qualifications
8+ years of related industry experience.
Demonstrated experience designing and deploying agentic or multi‑step AI systems (e.g., ReAct, tool‑calling agents, multi‑agent pipelines) in production or research settings.
Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX); experience with LLM APIs and orchestration libraries (e.g., LangChain, LlamaIndex).
Experience integrating LLMs with external tools, APIs, and structured data sources for real‑world task completion.
Solid understanding of prompt engineering techniques including chain‑of‑thought, few‑shot prompting, and structured output generation.
Experience defining and running evaluation frameworks for ML systems, including offline benchmarking and production monitoring.
Preferred Qualifications
PhD, MS, or BS in Computer Science, Machine Learning, Statistics, Engineering, or a related field; or equivalent professional experience.
Experience in the travel or e‑commerce industry.
Publications in top‑tier ML conferences or journals.
Patented inventions, pending and issued.
Contributions to open‑source ML projects.
Experience taking models from prototype to production in collaboration with Machine Learning Engineering teams.
Salary SanJose: $187,000.00–$261,500.00 (potential to increase up to $299,000.00 based on performance); Seattle: $173,000.00–$242,500.00 (potential to increase up to $277,000.00 based on performance). Starting pay will vary by location, budget, and individual experience.
Benefits Full benefits package includes medical, dental, and vision coverage; paid time off; Employee Assistance Program; wellness and travel reimbursement; travel discounts; and International Airlines Travel Agent membership.
Accommodation Requests If you need assistance during the application or recruiting process due to a disability or other physical or mental health conditions, please contact the Recruiting Accommodations Team.
Equal Employment Opportunity Expedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. This employer participates in E‑Verify. The employer will provide the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS) with information from each new employee’s I‑9 to confirm work authorization.
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Sprachkenntnisse
- English
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