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Ml Inference Jobs in New York (NOW HIRING)

Deploying real-time ML inference pipelines processing millions of records at high throughput * Experience building end to end automated MLOps capabilities along with model and feature drift ...

Software Engineer - Infrastructure

New York, NY ยท On-site +1

$165K - $330K/yr

Develop infrastructure components for our ML inference platform using Python and Go * Implement and maintain Kubernetes deployments for model serving * Contribute to our inference orchestration layer ...

Machine Learning Engineer

Manhattan, NY ยท Hybrid

$145K - $180K/yr

The ML Engineer has hands-on experience building and optimizing ML inference systems that run in production environments. This role will develop and tune pipelines that transform millions of photos ...

Machine Learning Engineer

Manhattan, NY ยท On-site

$145K - $180K/yr

The ML Engineer has hands-on experience building and optimizing ML inference systems that run in production environments. This role will develop and tune pipelines that transform millions of photos ...

Senior ML Infrastructure Engineer

New York, NY ยท On-site

$118K - $161K/yr

Hands-on experience with managed ML inference and serving platforms such as AWS SageMaker and GCP Vertex AI. * A proven track record operating inference at large scale across a range of model types ...

Work with model developers to tune their neural networks for better inference efficiency and ... working with ML inference or linear algebra computation * C++ programming skills, including ...

Software Engineer, AI/ML

New York, NY ยท On-site

$165K - $225K/yr

Can be anywhere in that lifecycle, from training machine learning systems, to creating the interfaces users use to navigate inference results. * An interest and passion for AI/ML systems, if you ...

Software Engineer, AI/ML

New York, NY ยท On-site

$165K - $225K/yr

Can be anywhere in that lifecycle, from training machine learning systems, to creating the interfaces users use to navigate inference results. * An interest and passion for AI/ML systems, if you ...

Implement, refine, and productionize cutting-edge techniques (quantization, speculative decoding, kv cache reuse, chunked prefill and LoRA) for ML model inference and infrastructure. * Deep dive into ...

Technical Program Manager, Inference

Livingston, NJ ยท On-site

$140K - $182K/yr

The AI/ML TPM team owns delivery and execution across CoreWeave's AI/ML Platform Services ... The Inference team is responsible for building and operating highly scalable, reliable production ...

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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 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.

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 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.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.
What cities in New York are hiring for Ml Inference jobs? Cities in New York with the most Ml Inference job openings:

AI/ML Engineer (MLops)

iTech US, Inc.

Woodbridge, NJ โ€ข On-site

Other

Posted 19 days ago


Job description

AI/ML Engineer (MLOps)

Iselin, NJ

12 months

Technical Expertise
ML/Al Frameworks: PyTorch, TensorFlow, JAX, HuggingFace, LangChain, LangGraph, Llamalndex, DSPy, ONNX Runtime, TensorRT

GenAl & LLMs: GPT-4/Claude API, LoRA/QLoRA fine-tuning, RAG (FAISS, Pinecone, ChromaDB), prompt engineering, agentic orchestration Languages: Python, C/C++, Java, SQL, Scala, GoLang, JavaScript
Cloud & Infra: AWS (SageMaker, S3, Lambda), Google Cloud Platform (GKE, Vertex Al, BigQuery), Kubernetes, Terraform, Docker

Databases: PostgreSQL, MySQL, MongoDB, Neo4j, BigQuery, Pinecone, ChromaDB, Redis Libraries: Pandas, NumPy, Scikit-learn, OpenCV, Keras, Spark, Kafka Dev

Tools: Linux, Git, Docker, Kubernetes, Jenkins

Experience Required

  • Experience building production Al/ML systems at scale
  • Deploying real-time ML inference pipelines processing millions of records at high throughput
  • Experience building end to end automated MLOps capabilities along with model and feature drift monitoring
  • Experience with event-driven and streaming platforms such as Apache Kafka,

Education : At least a bachelor s degree (or equivalent experience) in Computer Science, Software Engineering, Electronics Engineering, Information Systems, or a closely related field is required for the project