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Machine Learning Platform Engineer Jobs in New York, NY

exacare ai

Machine Learning Engineer

Manhattan, NY · On-site

Required : • Proven (3+ years) of experience in machine learning engineering, MLOps, ML infrastructure, data engineering, or backend/platform engineering in ML environments • Experience ...

ExaCare Inc

Machine Learning Engineer

Manhattan, NY · On-site

$100 - $130/hr

Machine Learning Engineer ExaCare Inc - New York, New York, United States About this position ... Our platform turns messy, unstructured referral packets into clear clinical insights and next steps ...

Hang

Machine Learning Engineer

New York, NY · On-site +1

Hang is the next generation brand loyalty & membership platform. By harnessing the power of ... About the Role We are seeking a skilled and innovative Machine Learning Engineer to join our team.

Baseten

Cloud Platform Engineer

Manhattan, NY · On-site

... a Cloud Platform Engineer to build scalable and reliable infrastructure. The role involves ... of machine learning models. • Establish standards and best practices for reliability and ...

Jane Street

Machine Learning Engineer

New York, NY · On-site

... Street as a Machine Learning Engineer while also providing a truly unparalleled educational ... Our ever-changing trading environment serves as a unique, rapid-feedback platform for ML ...

Jane Street

Machine Learning Engineer

New York, NY

... Street as a Machine Learning Engineer while also providing a truly unparalleled educational ... Our ever-changing trading environment serves as a unique, rapid-feedback platform for ML ...

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Machine Learning Platform Engineer information

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How much do machine learning platform engineer jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for machine learning platform engineer in New York, NY is $69.97, according to ZipRecruiter salary data. Most workers in this role earn between $55.24 and $80.72 per hour, depending on experience, location, and employer.

What skills and qualifications are needed to thrive as a machine learning platform engineer?

A Machine Learning Platform Engineer should have strong programming skills (especially in Python or Java), knowledge of machine learning frameworks (like TensorFlow or PyTorch), and experience with cloud platforms and scalable infrastructure. Familiarity with containerization tools (such as Docker and Kubernetes), CI/CD systems, and relevant certifications in cloud or machine learning technologies is highly valued. Effective problem-solving, teamwork, and clear communication are crucial soft skills for collaborating across data science and engineering teams. These capabilities enable seamless creation and maintenance of robust, high-performance machine learning platforms for scalable model development and deployment.

What does a machine learning platform engineer do?

A typical day for a Machine Learning Platform Engineer involves designing, building, and maintaining the infrastructure that supports data science and machine learning workflows. You might spend your time developing new features for the platform, optimizing data pipelines, deploying models, and troubleshooting technical issues alongside data scientists and engineers. Collaboration is key—you’ll often work closely with cross-functional teams to understand requirements, ensure scalability, and improve the overall machine learning lifecycle. This role offers a challenging mix of software engineering and system design, so adaptability and a proactive mindset are important for success.

What is a machine learning platform engineer?

A Machine Learning Platform Engineer designs, builds, and maintains the infrastructure that enables machine learning development and deployment at scale. They work on areas like data pipelines, model training workflows, monitoring, and cloud or on-premises platforms to ensure ML models run efficiently in production. Their role bridges software engineering and machine learning, focusing on automation, scalability, and reliability to support data scientists and ML engineers in delivering models faster and more effectively.

What are popular job titles related to Machine Learning Platform Engineer jobs in New York, NY? For Machine Learning Platform Engineer jobs in New York, NY, the most frequently searched job titles are:
What job categories do people searching Machine Learning Platform Engineer jobs in New York, NY look for? The top searched job categories for Machine Learning Platform Engineer jobs in New York, NY are:
Machine Learning Platform Engineer jobs near you
Infographic showing various Machine Learning Platform Engineer job openings in New York, NY as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $145,535 per year, or $70 per hour.

Senior Machine Learning Platform Engineer

Charlie Health

Manhattan, NY • On-site

$115K - $158K/yr

Full-time

Re-posted 21 days ago

Charlie Health rating

8.5

Company rating: 8.5 out of 10

Based on 12 frontline employees who took The Breakroom Quiz

Job description

Job Summary:
Charlie Health is a rapidly growing organization focused on providing personalized, virtual behavioral health treatment. They are seeking a Senior Machine Learning Platform Engineer to define the technical direction and build foundational systems for their ML and AI capabilities, ensuring scalable and reliable delivery of AI-powered features.
Responsibilities:
• Define technical direction for ML/AI infrastructure and make build-vs-buy decisions as the founding platform engineer
• Design and operate multi-vendor AI infrastructure supporting client-facing and clinician-facing LLM applications across multiple LLM providers
• Design, build, and operate production model serving systems; maintain infrastructure as code for reproducible ML environments, training pipelines, and deployment workflows
• Develop high-performance GPU inference pipelines with low latency and high availability
• Own the multimodal data pipeline layer—manage ingestion, processing, and serving of text, audio, and structured clinical data for ML and AI systems
• Create reliable infrastructure for agentic AI systems, including orchestration, monitoring, evaluation, and observability tooling
• Build developer tooling that accelerates data science and ML engineering workflows across the organization
• Own AI observability—build monitoring, alerting, and debugging capabilities for production ML systems
• Partner with ML engineers, data scientists, and product teams to understand infrastructure needs and translate them into scalable platform capabilities
• Foster a culture of collaboration and learning across engineering, product, and design through mentoring, documentation, presentations, and knowledge sharing
• Participate in our on-call rotation to ensure model serving uptime, pipeline reliability, and infrastructure health
Qualifications:
Required:
• 4+ years of professional experience in software engineering, with at least 2 years focused on ML infrastructure, ML platform, or AI systems engineering
• Strong software engineering fundamentals in Python and deep infrastructure expertise
• Familiarity with cloud ML services (AWS SageMaker, GCP Vertex AI, or similar) and CI/CD for ML pipelines
• Experience with infrastructure as code (Terraform, Pulumi, or similar) and container orchestration (Kubernetes, ECS)
• Excellent at managing ambiguity—able to break down big, messy problems into smaller parts with tractable solutions and clear iterations
• Growth mindset and sense of humor; you welcome feedback, adapt quickly in a fast-paced environment, and foster a culture of learning and fun
Preferred:
• Experience building evaluation and observability systems for LLM-based or agentic AI applications is a plus
• Experience with a systems language (Go, Rust, or C++) is a plus
Company:
Virtual behavioral health clinic for high acuity youth Founded in 2020, the company is headquartered in Bozeman, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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