AI Solution Engineer
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This mid-level role focuses on designing, training, and deploying machine learning models across the full lifecycle, from problem definition through production monitoring. You'll need 3+ years of professional ML or software engineering experience with proven production ML systems, strong proficiency in Python and frameworks like TensorFlow or PyTorch, and hands-on expertise in end-to-end ML pipelines including data preprocessing, model serving, and monitoring. MLOps experience with AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker is required. The position is on-site in San Francisco.
Written from this posting by Neural Jobs AI. The full description is below.
This is a mid-level Machine Learning Engineer role at a small, fast-moving AI startup in the recruitment technology space. You will own the full ML lifecycle, from problem definition through production monitoring, and work closely with product and engineering to ship models that drive real business impact.
Design, train, and evaluate machine learning models for production use cases.
Implement end-to-end ML pipelines covering data preprocessing, model serving, and monitoring.
Collaborate with product and engineering teams to translate business requirements into ML solutions.
Debug and optimize model performance in production, iterating based on real-world feedback.
Write clean, maintainable code and contribute to ML infrastructure and tooling.
Participate in code reviews and share knowledge with the broader team.
3+ years of professional experience in machine learning or software engineering, with hands-on work building and deploying production ML systems.
Proficiency in Python for ML development and experience with at least one ML framework such as TensorFlow, PyTorch, or scikit-learn.
Experience implementing end-to-end ML pipelines, including data preprocessing, model serving, and production monitoring.
Strong ML fundamentals: model selection, evaluation metrics, feature engineering, and validation techniques.
Experience deploying and maintaining ML systems using MLOps tools or cloud platforms such as AWS SageMaker, GCP Vertex AI, Kubernetes, or Docker.
Experience with A/B testing or experimentation frameworks in production environments.
Background in startup or fast-moving product environments with rapid iteration cycles.
Comfort with ambiguity and the ability to prioritize for impact in a dynamic setting.
This role is on-site in San Francisco, California.
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43 more Machine Learning Engineer roles in San Francisco
203K – 265K
Our estimate — this employer did not publish a salaryOur estimate, not the employer’s. Worked out from the middle half of 45 comparable roles on Neural Jobs that did publish a salary, in the same field, country and experience band. The real figure for this job may be different.
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