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Ml Inference Jobs in Puerto Rico (NOW HIRING)

SOLX HOLDINGS LLC

PR · On-site

$99K - $117K/yr

Build data pipelines from cameras and machines to training and inference. * Deploy and monitor ... Experience with computer vision and ML (PyTorch / TensorFlow, OpenCV). * Comfortable shipping ...

SOLX HOLDINGS LLC

PR · On-site

Partner with CV/ML, controls, and quality to integrate inference into daily operations. What you bring * BS / MS in Computer Science, EE, or related -- or equivalent experience. * 3+ years in ...

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Ml Inference information

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

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What cities in Puerto Rico are hiring for Ml Inference jobs?

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Ml Inference jobs near you

Computer Vision-ML Engineer

SOLX HOLDINGS LLC

PR • On-site

$99K - $117K/yr

Full-time

Posted 26 days ago

Job description

Build the vision and analytics that inspect every module and make the line smarter — applied AI on a real production floor.

What you'll do
  • Develop computer-vision models for inline defect detection (EL, surface, alignment).
  • Build data pipelines from cameras and machines to training and inference.
  • Deploy and monitor models at the edge; close the loop with operations in real time.
  • Develop manufacturing analytics and dashboards for yield and process insight.
  • Partner with controls and quality to integrate inspection into stations.
  • Label, version, and continuously improve datasets and models.
What you bring
  • BS / MS in Computer Science, EE, or related — or equivalent experience.
  • Experience with computer vision and ML (PyTorch / TensorFlow, OpenCV).
  • Comfortable shipping models to production and the edge.
  • Strong Python and data-engineering skills; MLOps a plus.
  • Interest in manufacturing and hands-on problem solving on the floor.
  • Bilingual English / Spanish a plus.