ML Engineer
Greenwich, CT · On-site
... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...
ML Engineer
Greenwich, CT · On-site
... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...
ML Engineer
Stamford, CT · On-site
... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...
ML Engineer
Stamford, CT · On-site
... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...
AI/ML Development Analyst
Norwalk, CT · On-site
$100K - $150K/yr
Job Title AI/ML Development Analyst Job Summary Commonfund is seeking a highly motivated AI/ML ... Develop and maintain data pipelines, model training workflows, and inference services . * Analyze ...
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AI/ML Development Analyst
Norwalk, CT · On-site
$100K - $150K/yr
Job Title AI/ML Development Analyst Job Summary Commonfund is seeking a highly motivated AI/ML ... Develop and maintain data pipelines, model training workflows, and inference services . * Analyze ...
Principal AI Architect
Stamford, CT · On-site
$141 - $222.20/hr
Design and architect AI/ML platforms and infrastructure for scale (training, inference, monitoring, deployment) * Define MLOps and model lifecycle management practices (versioning, governance ...
Principal AI Architect
Stamford, CT · On-site
$141 - $222.20/hr
Design and architect AI/ML platforms and infrastructure for scale (training, inference, monitoring, deployment) * Define MLOps and model lifecycle management practices (versioning, governance ...
AI Engineer Consultant
Stamford, CT · Hybrid
Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...
AI Engineer Consultant
Stamford, CT · Hybrid
Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...
Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...
Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...
Google AI Lead Architect
$59 - $80.75/hr
Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...
Google AI Lead Architect
$59 - $80.75/hr
Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...
Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...
Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Ml Inference information
See Bridgeport, CT salary details
$38.1K - $52.8K
2% of jobs
$52.8K - $67.5K
3% of jobs
$67.5K - $82.2K
6% of jobs
$82.2K - $96.9K
9% of jobs
$101.6K is the 25th percentile. Wages below this are outliers.
$96.9K - $111.6K
15% of jobs
The median wage is $121.4K / yr.
$111.6K - $126.3K
22% of jobs
$134.4K is the 75th percentile. Wages above this are outliers.
$126.3K - $141K
32% of jobs
$141K - $155.7K
3% of jobs
$155.7K - $170.4K
4% of jobs
$170.4K - $185.1K
1% of jobs
$185.1K - $199.8K
2% of jobs
$38.1K
$124.8K
$199.8K
How much do ml inference jobs pay per year?
As of Aug 14, 2026, the average yearly pay for ml inference in Bridgeport, CT is $124,796.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,200.00 and $138,300.00 per year, depending on experience, location, and employer.
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?
| Aspect | ML Inference | Data Scientist |
|---|---|---|
| Required Credentials | Knowledge of machine learning models, programming skills | Degree in data science, statistics, or related fields |
| Work Environment | Deploying models in production, real-time data processing | Data analysis, model development, research |
| Industry Usage | AI product deployment, software companies | Research 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 are popular job titles related to Ml Inference jobs in Bridgeport, CT?
For Ml Inference jobs in Bridgeport, CT, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Bridgeport, CT look for?
The top searched job categories for Ml Inference jobs in Bridgeport, CT are:
What cities near Bridgeport, CT are hiring for Ml Inference jobs?
Cities near Bridgeport, CT with the most Ml Inference job openings:
Full-time
Re-posted 29 days ago
Job description
Job Summary:
Catalyst Labs is a leading talent agency specializing in Applied AI, Machine Learning, and Data Science. They are seeking an ML Engineer to design, build, and deploy production-grade ML systems, focusing on audio data and real-time audio understanding, while collaborating with cross-functional teams to drive innovation in AI applications.
Responsibilities:
• Design, build, and deploy production-grade ML systems with end-to-end ownership of the model lifecycle from conception to deployment and maintenance.
• Architect and deliver AI-powered solutions enabling natural speech interaction and real-time audio understanding.
• Develop and optimize ML models focused on audio data to extract business-critical insights from previously unstructured voice data.
• Build agents capable of operating natively on real-world audio inputs.
• Collaborate with cross-functional teams to shape the foundations of the AI stack, improve tooling, and drive innovation in LLM and audio ML applications.
• Work directly with customers to identify needs, gather feedback, and deliver impactful real-world solutions.
• Handle the entire AI lifecycle, including data acquisition, preprocessing, model training, deployment, inference, and monitoring in production environments.
• Participate in continuous improvement of the ML infrastructure and processes for scalability and performance.
Qualifications:
Required:
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 1-6 years of professional experience in ML engineering.
• Strong programming skills in Python (TypeScript experience is a plus).
• Hands-on experience with ML frameworks such as PyTorch or TensorFlow.
• Familiarity with cloud environments and infrastructure (preferably AWS).
• Strong understanding of data pipeline design, real-time inference, and model monitoring.
• Excellent communication skills with the ability to engage directly with customers and stakeholders.
• Proven experience building and deploying ML models into production environments.
• Demonstrated ability to own the full model lifecycle from data ingestion and model development to deployment and monitoring.
• Experience with audio-focused ML projects or similar domains involving unstructured data.
• Proficiency in building scalable data pipelines for model training and evaluation.
• Solid grasp of ML systems architecture, feature engineering, evaluation strategies, and deployment best practices.
Preferred:
• Familiarity with FastAPI, OpenAI APIs, Baseten, LiteLLM, LiveKit, PostgreSQL, Redis, and S3 is a plus.
Company:
Welcome to Catalyst Labs – Powering Catalytic Growth At Catalyst Labs, catalytic growth isn't just a concept, it's our driving force. Founded in , the company is headquartered in London, GB, , with a team of 11-50 employees. The company is currently Early Stage.
Catalyst Labs is a leading talent agency specializing in Applied AI, Machine Learning, and Data Science. They are seeking an ML Engineer to design, build, and deploy production-grade ML systems, focusing on audio data and real-time audio understanding, while collaborating with cross-functional teams to drive innovation in AI applications.
Responsibilities:
• Design, build, and deploy production-grade ML systems with end-to-end ownership of the model lifecycle from conception to deployment and maintenance.
• Architect and deliver AI-powered solutions enabling natural speech interaction and real-time audio understanding.
• Develop and optimize ML models focused on audio data to extract business-critical insights from previously unstructured voice data.
• Build agents capable of operating natively on real-world audio inputs.
• Collaborate with cross-functional teams to shape the foundations of the AI stack, improve tooling, and drive innovation in LLM and audio ML applications.
• Work directly with customers to identify needs, gather feedback, and deliver impactful real-world solutions.
• Handle the entire AI lifecycle, including data acquisition, preprocessing, model training, deployment, inference, and monitoring in production environments.
• Participate in continuous improvement of the ML infrastructure and processes for scalability and performance.
Qualifications:
Required:
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 1-6 years of professional experience in ML engineering.
• Strong programming skills in Python (TypeScript experience is a plus).
• Hands-on experience with ML frameworks such as PyTorch or TensorFlow.
• Familiarity with cloud environments and infrastructure (preferably AWS).
• Strong understanding of data pipeline design, real-time inference, and model monitoring.
• Excellent communication skills with the ability to engage directly with customers and stakeholders.
• Proven experience building and deploying ML models into production environments.
• Demonstrated ability to own the full model lifecycle from data ingestion and model development to deployment and monitoring.
• Experience with audio-focused ML projects or similar domains involving unstructured data.
• Proficiency in building scalable data pipelines for model training and evaluation.
• Solid grasp of ML systems architecture, feature engineering, evaluation strategies, and deployment best practices.
Preferred:
• Familiarity with FastAPI, OpenAI APIs, Baseten, LiteLLM, LiveKit, PostgreSQL, Redis, and S3 is a plus.
Company:
Welcome to Catalyst Labs – Powering Catalytic Growth At Catalyst Labs, catalytic growth isn't just a concept, it's our driving force. Founded in , the company is headquartered in London, GB, , with a team of 11-50 employees. The company is currently Early Stage.