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Machine Learning Ops Engineer Jobs in New York (NOW HIRING)

Machine Learning Engineers build production grade machine learning algorithms that operate in real time or at scale. They have a very deep understanding of machine learning algorithms and cloud ...

We are seeking a Machine Learning Engineer to join the High Frequency Trading Technology team. This role will apply the latest AI technologies to solve various real-world problems and streamline day ...

As a Machine Learning Engineer, you will play a critical role in shaping the future of cooking. Working on a small, high-impact team, you will have significant ownership over the strategy, research ...

We are seeking a Machine Learning Engineer to join the High Frequency Trading Technology team. This role will apply the latest AI technologies to solve various real-world problems and streamline day ...

Machine Learning Engineer

New York, NY · On-site +1

$170K - $212K/yr

We're looking for a Machine Learning Engineer to help us build systems that more accurately understand the performance that promotion can have, giving customers actionable insights for building their ...

Machine Learning Engineer

New York, NY · On-site +1

$170K - $212K/yr

We're looking for a Machine Learning Engineer to help us build systems that more accurately understand the performance that promotion can have, giving customers actionable insights for building their ...

Sr. Lead Machine Learning Engineer

New York, NY · On-site +1

$112K - $147K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be ... The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this ...

The Machine Learning Engineer will play a central role in building the platform for training, evaluating, and deploying interpretable AI systems at scale. Responsibilities : • Turn cutting edge ...

We are looking for an engineer with robust experience in machine learning and strong mathematical foundations to join our growing ML team and to help drive the direction of our ML platform. Machine ...

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Showing results 1-20

Machine Learning Ops Engineer information

See New York salary details

$34.5K

$140.9K

$211.7K

How much do machine learning ops engineer jobs pay per year?

As of Jul 4, 2026, the average yearly pay for machine learning ops engineer in New York is $140,878.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,000.00 and $169,600.00 per year, depending on experience, location, and employer.

What is a Machine Learning Ops Engineer job?

A Machine Learning Ops Engineer (MLOps Engineer) focuses on deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and software engineering, ensuring models run efficiently, reliably, and at scale. Their responsibilities include automating workflows, managing infrastructure, and ensuring CI/CD pipelines for ML models. They work with tools like Kubernetes, Docker, and cloud platforms to streamline model deployment. Ultimately, an MLOps Engineer ensures that machine learning models are operationalized and continuously improved in a real-world environment.

What does a typical day look like for a Machine Learning Ops Engineer?

A typical day for a Machine Learning Ops Engineer involves collaborating with data scientists to streamline the deployment of models, building and maintaining scalable infrastructure on cloud services, and automating workflows with CI/CD tools. You may troubleshoot issues in production environments, monitor model performance, and implement solutions for model versioning and retraining. Often, you’ll work closely with software engineers, DevOps teams, and data analysts to ensure seamless integration of machine learning solutions into products. This cross-functional role keeps you engaged with cutting-edge technology and provides opportunities to influence both technical and business outcomes.

What are the key skills and qualifications needed to thrive in the Machine Learning Ops Engineer position, and why are they important?

To thrive as a Machine Learning Ops Engineer, you need a solid grasp of machine learning concepts, cloud platforms, software engineering, and DevOps practices, typically supported by a degree in computer science or a related field. Experience with tools like Docker, Kubernetes, TensorFlow, CI/CD pipelines, and certifications such as AWS Certified Machine Learning – Specialty are highly valuable. Strong problem-solving skills, communication, and the ability to work collaboratively across data science and engineering teams set top candidates apart. These skills ensure reliable deployment, scalability, and optimization of machine learning models in production environments.

What are the most commonly searched types of Machine Learning Ops Engineer jobs in New York? The most popular types of Machine Learning Ops Engineer jobs in New York are:
What job categories do people searching Machine Learning Ops Engineer jobs in New York look for? The top searched job categories for Machine Learning Ops Engineer jobs in New York are:
Infographic showing various Machine Learning Ops Engineer job openings in New York as of June 2026, with employment types broken down into 87% Full Time, 3% Part Time, and 10% Contract. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution, with an average salary of $140,878 per year, or $67.7 per hour.

Sr. Machine Learning Engineer

Canoe Intelligence

Manhattan, NY • On-site, Remote

$180K - $220K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 26 days ago


Job description

COMPANY: Canoe Intelligence

WEBSITE: https://canoeintelligence.com/

TITLE: Sr. Machine Learning Engineer

LOCATION: New York City or London (hybrid) / Fully Remote in the United States or United Kingdom

SALARY: $180,000 - $220,000 (based on NYC, will be adjusted for geo)

The Role:

We are looking for a Senior Machine Learning Engineer to design and deploy models that make sense of highly complex, unstructured financial documents, enabling us to deliver data with unprecedented accuracy, speed, and trust. You’ll work hands-on with LLM and other ML Models, helping scale Canoe’s platform while shaping how alternative investment firms interact with their data.

What You’ll Do:

  • Design, train, and evaluate ML models for document classification, entity extraction, summarization, and information retrieval.

  • Fine-tune and optimize large language models for domain specific use cases, optimizing their performance for accuracy, efficiency, and scalability.

  • Work closely with data engineering teams to preprocess and engineer features from large datasets to enhance the performance of machine learning models.

  • Build scalable, production-ready ML services with strong observability, monitoring, and retraining capabilities.

  • Contribute to Canoe’s MLOps stack, including CI/CD for models, feature stores, evaluation frameworks, and data versioning.

  • Collaborate with product managers, software engineers, and other stakeholders to integrate machine learning models into end-to-end solutions.

  • Stay current with advancements in LLMs, Agentic AI, and ML, and translate new research into practical improvements to Canoe’s technology stack.

  • Conduct code reviews to ensure code quality and provide mentorship to junior members of the machine learning team.

What We’re Looking For:

  • Minimum of 5 years of experience in applied ML engineering, with a focus on NLP, information extraction, or LLMs.

  • Proficiency in Python and relevant machine learning libraries (e.g., TensorFlow, PyTorch).

  • Strong understanding of MLOps (Docker, Kubernetes, CI/CD for ML, experiment tracking).

  • Proficiency with AI-assisted development tools (e.g., GitHub Copilot, Claude Code agent) to accelerate software development, prototyping, testing, and deployment of ML solutions.

  • Problem-solver with a product mindset and bias toward outcomes.

  • Excellent communication skills; able to partner across engineering, product, and business teams.

  • Comfortable in fast-paced, agile startup environments.

  • Bachelor’s degree in computer science or related field.

Preferred

  • Master Degree or PhD in computer science or related field

  • Experience in training and deploying large language models.

  • Familiarity with cloud computing platforms and distributed computing.

  • Familiarity with modern ML Ops tools such as Modal, Weights and Biases, Sagemaker, etc.

  • Experience with LLM fine-tuning techniques such as LoRA, QLoRA, or parameter-efficient training frameworks (e.g., Unsloth).

What You’ll Get:

  • Medical, dental, vision benefits

  • Flexible PTO

  • 401(k)

  • Flexible work from home policy

  • Home office stipend

  • Employee Assistance Program

  • Gym/Wifi reimbursement

  • Education assistance

  • Parental Leave

Our Values:

  • Client First —> Listen, and deliver client-centric solutions

  • Be An Owner —> Take initiative, improve situations, drive positive outcomes

  • Excellence —> Always set the highest standard for yourself and others

  • Win Together —> 1 + 1 = 3

Who We Are:

Canoe is reimagining alternative investment data processes for hundreds of leading institutional investors, capital allocators, asset servicing firms and wealth managers. By combining industry expertise with the most sophisticated data capture technologies, Canoe’s technology automates the highly-frustrating, time-consuming, and costly manual workflows related to alternative investment document and data management, extraction and delivery. With Canoe, clients can refocus capital and human resources on business performance and growth, increase efficiency, and gain deeper access to their data. Canoe’s AI-driven platform was developed in 2013 for Portage Partners LLC, a private investment firm.

Canoe is an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.