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Machine Learning Ai Jobs (NOW HIRING)

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug delivery. The research intern will be in a fast-paced start-up environment playing a crucial technical ...

Nanite Inc.

Machine Learning Engineer

Boston, MA

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug delivery. The research intern will be in a fast-paced start-up environment playing a crucial technical ...

Metric5

AI Learning Specialist

Windsor Mill, MD · Remote

$150K - $170K/yr

AI Learning Specialist Are you passionate about helping others discover the potential of machine learning and artificial intelligence (AI)? Do you enjoy demystifying complex concepts and making them ...

Metric5

AI Learning Specialist

Baltimore, MD · On-site

$150K - $170K/yr

AI Learning Specialist Are you passionate about helping others discover the potential of machine learning and artificial intelligence (AI)? Do you enjoy demystifying complex concepts and making them ...

BeeGenius

AI/Machine Learning Engineer

Sunnyvale, CA · On-site

BeeGenius is building the future of work, and they are seeking an AI/Machine Learning Engineer to join their team. In this role, you will be responsible for developing and implementing machine ...

WorkNovas LLC

Machine Learning Engineer

Richmond, VA · On-site

Key Responsibilities Design and implement agentic AI systems capable of planning, tool use, memory ... machine learning, AI engineering, or applied ML Strong proficiency in Python for ML and backend ...

Resolve Tech Solutions

AI & ML Intern

Addison, TX

Artificial Intelligence & Machine Learning (AI & ML) Intern Position Title: AI & ML Intern Reporting to: Director, AI Strategy & Architecture Position Summary The AI & ML Intern at Juno Labs will ...

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Machine Learning Ai information

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$25.5K

$42.6K

$88K

How much do machine learning ai jobs pay per year?

As of May 30, 2026, the average yearly pay for machine learning ai in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning AI Engineer, and why are they important?

To thrive as a Machine Learning AI Engineer, you need a strong background in mathematics, statistics, programming (typically Python), and a relevant degree in computer science or a related field. Familiarity with machine learning frameworks like TensorFlow and PyTorch, as well as cloud platforms and data processing tools, is essential, and certifications in these areas can be advantageous. Strong problem-solving, communication, and collaboration skills help you effectively translate business needs into technical solutions and work well within multidisciplinary teams. These skills ensure you can develop robust AI models that address real-world challenges and deliver meaningful business impact.

What are some common challenges faced when collaborating with cross-functional teams as a Machine Learning AI professional?

As a Machine Learning AI professional, you’ll often collaborate with data engineers, software developers, and product managers. A common challenge is bridging the gap between complex AI models and practical business requirements, ensuring your solutions are both technically sound and aligned with user needs. Effective communication is key, as you’ll need to explain technical concepts to non-technical stakeholders and adapt your models based on feedback. Building trust and fostering a collaborative environment will help ensure successful project outcomes and foster continual learning.

What is a Machine Learning AI specialist?

A Machine Learning AI specialist is a professional who develops algorithms and models that enable computers to learn from and make predictions or decisions based on data. They work with large datasets, train and evaluate machine learning models, and often collaborate with software engineers and data scientists to integrate AI solutions into products and services. Their work is crucial in fields like natural language processing, computer vision, and predictive analytics, helping organizations automate tasks, gain insights, and improve efficiency.

What is the difference between Machine Learning Ai vs Data Scientist?

AspectMachine Learning AiData Scientist
Required CredentialsDegree in Computer Science, AI, or related fields; experience with programming and algorithmsDegree in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentDeveloping algorithms, training models, deploying AI systemsAnalyzing data, creating reports, interpreting results
Employer & Industry UsageTech companies, AI startups, research institutionsFinance, healthcare, marketing, tech firms

Machine Learning Ai focuses on developing and deploying AI algorithms and models, while Data Scientists analyze and interpret data to inform business decisions. Both roles often collaborate but have distinct focuses within the data and AI ecosystem.

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What states have the most Machine Learning Ai jobs? States with the most job openings for Machine Learning Ai jobs include:
What job categories do people searching Machine Learning Ai jobs look for? The top searched job categories for Machine Learning Ai jobs are:
Machine Learning Ai jobs near you
ML Ops Engineer, Machine Learning & AI

ML Ops Engineer, Machine Learning & AI

The New York Times

New York, NY • Hybrid

Other

Posted 23 days ago

Job description

About the Role:

Machine Learning (ML) at the New York Times enhances the experience of our 150 million digital readers from around the globe and grows our subscriber base through content recommendations and personalizations.

The Machine Learning & AI team builds and maintains the infrastructure that hosts all of The New York Times real-time ML inference models, including both data and compute. Our partners are Data Scientists that build and deploy their ML models on the ML platform. On the other end, our partners are engineering systems that call these hosted models at scale with low-latency and Service Level Agreements guaranteed by our platform.

As an MLOps Engineer you will partner with product, data science and ML platform engineers to build and maintain the infrastructure that powers the machine learning lifecycle. You will automate and refine the training, deployment, monitoring, and management of our ML models.

This role reports to the Senior Engineering Manager of Data Management Infrastructure.

Responsibilities:

  • Build and Automate ML Pipelines: by owning robust CI/CD pipelines for automated model training, validation, deployment, and retraining.

  • Productionalize Models: Build the process for packaging, containerizing, and deploying ML models as scalable, low-latency, and highly-available services.

  • Monitoring and Operations: Implement and manage comprehensive monitoring for production models, tracking system health, data drift, and model performance degradation.

  • Tooling and Infrastructure: Manage and evolve our MLOps toolchain, including model registries, feature stores, experiment tracking systems, and model serving platforms.

  • Collaboration and Support: Partner with data scientists to understand model requirements and optimize them for production. Support software engineers in integrating with ML services.

  • Best Practices and Governance: Champion and enforce MLOps best practices for reproducibility, versioning (data, code, model), testing, and governance.

  • Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world.

Basic Qualifications:

  • 2+ years of software engineering or DevOps experience with a focus on MLOps, automation, and infrastructure

  • 2+ years of experience programming in Python or Go

  • Experience building and managing CI/CD pipelines (e.g., Github Actions, Jenkins, GitLab CI)

  • Hands-on experience with containerization and orchestration (e.g., Docker, Kubernetes)

  • Cloud platform experience (AWS, GCP) and familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation)

Preferred Qualifications:

  • Experience with MLOps tools (e.g., MLflow, Kubeflow)

  • Experience with the machine learning model lifecycle, from experimentation to production

  • Experience with data processing frameworks (e.g., Spark, Dask, or Ray)

  • Experience with low-latency no-sql datastores (BigTable, Dynamo, etc)

  • Familiarity with monitoring and observability stacks (e.g., Prometheus, Grafana, Datadog, or ELK)

  • Knowledge of data engineering pipelines and orchestration tools (e.g., Airflow, Prefect)

REQ-019522

#LI-hybrid

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