Staff Machine Learning Engineer
New
J
JobgetherMachine Learning
Based in United StatesFull-TimeStaff
Salary206,261 - 330,017 USD per year
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Job Details
- Experience
- 10+ years of software engineering experience, including 4+ years working with production machine learning systems at scale
- Required Skills
- PythonMachine LearningPyTorchDeep LearningDistributed Systems
Requirements
- 10+ years of software engineering experience, including 4+ years working with production machine learning systems at scale.
- Proven experience designing, deploying, and operating distributed machine learning systems.
- Strong understanding of ML model deployment patterns, data pipelines, and production reliability practices.
- Experience working with programming languages such as Python.
- Experience with machine learning tools and platforms such as PyTorch, HuggingFace, AWS SageMaker, or similar technologies.
- Ability to work with large-scale data processing systems and complex, noisy datasets.
- Strong problem-solving skills with a data-driven approach to evaluating solutions and making technical decisions.
- Demonstrated ability to influence engineering practices and technical direction across teams.
- Excellent communication skills with the ability to collaborate effectively in a remote environment.
- Curiosity, empathy, adaptability, and a continuous learning mindset.
Responsibilities
- Design, build, deploy, and optimize machine learning models that process large volumes of complex, unstructured data.
- Develop and maintain scalable ML pipelines capable of supporting millions of documents and diverse customer requirements.
- Lead technical initiatives from early experimentation through production implementation and ongoing improvement.
- Create reliable model deployment strategies while ensuring performance, scalability, and operational stability.
- Improve ML system accuracy, efficiency, and latency through advanced techniques such as deep learning, transfer learning, and model optimization.
- Partner with product managers, engineers, and business stakeholders to understand customer needs and deliver impactful AI solutions.
- Establish and promote ML engineering standards, best practices, and repeatable development processes across teams.
- Influence technical decisions and contribute to architectural direction for machine learning platforms.
- Support production systems by applying reliability engineering principles and proactively resolving technical challenges.
- Communicate technical trade-offs, risks, and opportunities clearly across technical and non-technical teams.
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