Mondo · 2 days ago

Machine Learning Engineer

United States

Full-time

Remote

Mid Level

2+ years exp

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Responsibilities

Design, develop, and deploy complex ML models aligning with business goals.

Architect and implement robust, scalable ML solutions using PyTorch, TensorFlow, etc.

Fine-tune and optimize large language models for specific use cases.

Experiment with NLP, NLU, and NLG techniques to enhance conversational AI products.

Monitor and optimize model performance in production environments.

Collaborate with software engineers to integrate ML models into production systems.

Utilize tools like Docker, Kubernetes, ONNX, Kubeflow, MLflow for deployment optimization.

Stay updated on ML research advancements and explore emerging technologies.

Communicate technical concepts effectively to both technical and non-technical audiences.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine LearningDeep LearningPythonTensorFlowPyTorchJAXAWSGCPDockerKubernetesStatisticsNLPNLUNLGMLOpsDeploymentMonitoringOrchestrationTFServingTensorRTTorchServeONNXKubeflowMLflowProblem-SolvingPragmatic Approach

Required

A minimum of 2 years of professional experience as a Machine Learning Engineer.

Bachelor’s degree or higher in Computer Science, Machine Learning, AI, Mathematics, or a related field (Master’s preferred).

Excellent problem-solving abilities and a pragmatic approach to building scalable and robust machine learning systems.

Strong foundation in machine learning and deep learning, including embedding methods, supervised and unsupervised learning, and deep learning architectures.

Strong programming skills in Python and proficiency with machine learning libraries such as TensorFlow, PyTorch, or JAX.

Experience with cloud platforms (e.g., AWS, GCP) and containerization technologies (e.g., Docker, Kubernetes).

Strong foundation in statistics and an understanding of machine learning concepts, especially in NLP, NLU, and NLG.

Familiarity with the MLOps lifecycle, including deployment, monitoring, and orchestration of ML models in production settings.

Experience with model deployment tools and platforms like TFServing, TensorRT, TorchServe, ONNX, Kubeflow, and MLflow.