Senior · Staff · Principal Machine Learning Engineer Job at Lead Allies Inc in N
Lead Allies Inc, New York, NY, United States
Senior / Staff / Principal Machine Learning Engineer
Location: Onsite New York (5 days onsite AND hybrid options)
We have multiple startups interested in talent. Here is a generic summary. Instead of a perfect job description, we present talented individuals to companies and allow them to share how that talent fits in the organization.
Key Responsibilities:
Model Development:
Designing and implementing ML algorithms and models, including deep learning models.
Data Handling:
Preprocessing, analyzing, and preparing large datasets for model training and evaluation.
System Integration:
Collaborating with software engineers to integrate ML models into production systems.
Performance Optimization:
Continuously improving and optimizing ML models for accuracy, efficiency, and scalability.
Monitoring and Maintenance:
Monitoring model performance in production, troubleshooting issues, and ensuring model reliability.
Staying Updated:
Keeping abreast of the latest advancements in ML, AI, and related technologies.
Collaboration:
Working with data scientists, software engineers, and other stakeholders to deliver effective ML solutions.
Essential Skills:
Programming Languages: Strong proficiency in Python, R, or other relevant languages.
ML Frameworks: Experience with frameworks like TensorFlow, PyTorch, or scikit-learn.
Data Science Fundamentals: Solid understanding of statistical analysis, data modeling, and machine learning algorithms.
Problem-Solving: Excellent analytical and problem-solving skills to address complex challenges.
Communication: Effective communication skills to convey technical information to both technical and non-technical audiences.
Collaboration: Ability to work effectively in a team environment.
Education and Experience:
A bachelor's or master's degree in computer science, engineering, mathematics, statistics, or a related field is typically required.
Several years of experience in machine learning, data science, or software development is often preferred.
Compensation: Market range and can include equity – details can be provided after the specific client is determined.
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In Summary: Model Development: Designing and implementing ML algorithms and models, including deep learning models . Data Handling: Preprocessing, analyzing, and preparing large datasets for model training and evaluation . Collaborating with software engineers to integrate ML models into production systems . Performance Optimization: Continuously improving and optimizing ML models .
En Español: Localización: En Nueva York (5 días en el lugar Y opciones híbridas) Tenemos varias startups interesadas en talento. Aquí hay un resumen genérico. En lugar de una descripción perfecta del trabajo, presentamos individuos talentosos a las empresas y les permitimos compartir cómo ese talento encaja en la organización. Responsabilidades clave: Desarrollo de modelos: Diseño e implementación de algoritmos y modelos ML, incluidos los modelos de aprendizaje profundo. Manejo de datos: Preprocesamiento, análisis y preparación de grandes conjuntos de datos para el entrenamiento y evaluación de modelos. Integración del sistema: Colaboración con ingenieros de software para integrar modelos ML en sistemas productivos. Optimización del rendimiento: mejora continua y optimización de modelos ML para precisión, eficiencia y escalabilidad. Monitoreo y mantenimiento: monitorización del desempeño del modelo en producción, resolución de problemas y garantía de la fiabilidad del modelo. Mantenerse actualizado: mantenerse al tanto informados sobre los últimos avances en tecnología ML, desarrollo tensamente determinada. La colaboración con científicos, software y otros grupos de interés puede proporcionar conocimientos técnicos específicos después de diseñar un marco matemático complejo.
Location: Onsite New York (5 days onsite AND hybrid options)
We have multiple startups interested in talent. Here is a generic summary. Instead of a perfect job description, we present talented individuals to companies and allow them to share how that talent fits in the organization.
Key Responsibilities:
Model Development:
Designing and implementing ML algorithms and models, including deep learning models.
Data Handling:
Preprocessing, analyzing, and preparing large datasets for model training and evaluation.
System Integration:
Collaborating with software engineers to integrate ML models into production systems.
Performance Optimization:
Continuously improving and optimizing ML models for accuracy, efficiency, and scalability.
Monitoring and Maintenance:
Monitoring model performance in production, troubleshooting issues, and ensuring model reliability.
Staying Updated:
Keeping abreast of the latest advancements in ML, AI, and related technologies.
Collaboration:
Working with data scientists, software engineers, and other stakeholders to deliver effective ML solutions.
Essential Skills:
Programming Languages: Strong proficiency in Python, R, or other relevant languages.
ML Frameworks: Experience with frameworks like TensorFlow, PyTorch, or scikit-learn.
Data Science Fundamentals: Solid understanding of statistical analysis, data modeling, and machine learning algorithms.
Problem-Solving: Excellent analytical and problem-solving skills to address complex challenges.
Communication: Effective communication skills to convey technical information to both technical and non-technical audiences.
Collaboration: Ability to work effectively in a team environment.
Education and Experience:
A bachelor's or master's degree in computer science, engineering, mathematics, statistics, or a related field is typically required.
Several years of experience in machine learning, data science, or software development is often preferred.
Compensation: Market range and can include equity – details can be provided after the specific client is determined.
#J-18808-Ljbffr
In Summary: Model Development: Designing and implementing ML algorithms and models, including deep learning models . Data Handling: Preprocessing, analyzing, and preparing large datasets for model training and evaluation . Collaborating with software engineers to integrate ML models into production systems . Performance Optimization: Continuously improving and optimizing ML models .
En Español: Localización: En Nueva York (5 días en el lugar Y opciones híbridas) Tenemos varias startups interesadas en talento. Aquí hay un resumen genérico. En lugar de una descripción perfecta del trabajo, presentamos individuos talentosos a las empresas y les permitimos compartir cómo ese talento encaja en la organización. Responsabilidades clave: Desarrollo de modelos: Diseño e implementación de algoritmos y modelos ML, incluidos los modelos de aprendizaje profundo. Manejo de datos: Preprocesamiento, análisis y preparación de grandes conjuntos de datos para el entrenamiento y evaluación de modelos. Integración del sistema: Colaboración con ingenieros de software para integrar modelos ML en sistemas productivos. Optimización del rendimiento: mejora continua y optimización de modelos ML para precisión, eficiencia y escalabilidad. Monitoreo y mantenimiento: monitorización del desempeño del modelo en producción, resolución de problemas y garantía de la fiabilidad del modelo. Mantenerse actualizado: mantenerse al tanto informados sobre los últimos avances en tecnología ML, desarrollo tensamente determinada. La colaboración con científicos, software y otros grupos de interés puede proporcionar conocimientos técnicos específicos después de diseñar un marco matemático complejo.