Machine Learning Engineer
$137,700–$275,400 year
Hybrid · Seattle, Washington, United States
Job Summary
Machine Learning Engineer role focusing on building and deploying intelligent agent systems. Responsibilities include designing and implementing agent architectures that can reason about tasks, plan actions, and execute solutions; developing prompt engineering frameworks for reliable agent behavior; implementing safety mechanisms and oversight for autonomous systems; building and maintaining LLM-powered services using models such as GPT-4, Claude, and internal models; and establishing robust testing frameworks for agent behavior validation. Requires a Bachelor’s degree in a related field and 5+ years of ML engineering experience, with expertise in NLP and transformer architectures. Proficiency in Python, Java, C++, and ML frameworks (PyTorch, Transformers, LangChain; TensorFlow, Scikit-learn) and cloud tech (Kubernetes, Docker, AWS). Hybrid/Remote/In-Person work model with the Seattle, WA location; salary range $137,700–$275,400 with potential adjustments based on location and experience.
Required Qualifications
- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Cognitive Science, or a related field
- 5+ years of experience in ML engineering
- Expertise in NLP and transformer architectures
- Core stack: Python, Java, C++, or other programming languages for ML development
- Python or Java with deep learning frameworks (PyTorch, Transformers, LangChain)
- Experience with ML frameworks like TensorFlow, PyTorch, Scikit-learn
- Cloud infrastructure experience (Kubernetes, Docker, AWS)
Desired Qualifications
- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Cognitive Science, or a related field
- 5+ years of experience in ML engineering
- Expertise in NLP and transformer architectures
- Proficiency with Python, Java, C++, or other ML development languages
- Experience with deep learning frameworks such as PyTorch, Transformers, LangChain
- Experience with ML frameworks like TensorFlow, PyTorch, Scikit-learn
- Experience with cloud infrastructure (Kubernetes, Docker, AWS)
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