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Senior Machine Learning Ops Engineer Jobs (NOW HIRING)

As the Machine Learning Ops Engineer for the AI Team you will: * Work closely with the Data Science ... Liaise with senior stakeholders across the Data function and the wider business * Use industry best ...

$77K - $105K/yr

Senior Machine Learning Engineer Position Type: Full-Time/Onsite in Richmond, VA - NO REMOTE Level: Senior Department: Engineering About the Role We are seeking an exceptional Senior Machine Learning ...

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Senior Machine Learning Engineer * Location: Remote * Employment Type: Full-time Compensation: Competitive salary commensurate with experience, qualifications, and location. Indicative range: $150 ...

$150K - $250K/yr

Senior Machine Learning Engineer * Location: Remote * Employment Type: Full-time Compensation: Competitive salary commensurate with experience, qualifications, and location. Indicative range: $150 ...

$84K - $115K/yr

En Estados Unidos, un Senior Machine Learning Engineer Senior gana en el mercado entre US$ 12,500 y US$ 17,500 por mes. La remuneración depende de tu experiencia demostrable, la profundidad de tu ...

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Senior Machine Learning Engineer

Manhattan, NY · On-site

$115K - $158K/yr

About the Role We're looking for a Senior Machine Learning Engineer to join our AI team and help build the next generation of predictive trading models. You'll work on cutting-edge problems involving ...

Senior Machine Learning Engineer

Palo Alto, CA · On-site

$144K - $189K/yr

Sr. Machine Learning Engineer Palo Alto, CA (Onsite 5x a week) At Klaviyo, we believe the future of software lies not in productivity tools for human users but in software that can run and optimize ...

Senior Machine Learning Engineer AgentPlatform - Adobe Experience Platform THE OPPORTUNITY Build ... ML-Ops or Agent-Ops experience .You'vebuilt eval frameworks, execution tracing, drift detection ...

Machine Learning Engineer

Phoenix, AZ · On-site

$130K - $150K/yr

Sr. Machine Learning Engineer Salary Range: $130k to $150k Our client is seeking a Sr. Machine Learning Engineering for a direct hire role to sit in North Phoenix, AZ or Hillsboro, OR. This role will ...

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Senior Machine Learning Ops Engineer information

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$59.5K

$126.6K

$183.5K

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

As of Sep 10, 2026, the average yearly pay for senior machine learning ops engineer in the United States is $126,557.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,500.00 and $143,500.00 per year, depending on experience, location, and employer.

What is a senior machine learning ops engineer?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.

What are the key skills and qualifications needed to thrive as a senior machine learning ops engineer?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by senior machine learning ops engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

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Infographic showing various Senior Machine Learning Ops Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $126,557 per year, or $60.8 per hour.

Sr. Machine Learning Engineer

Manhattan, NY • On-site, Remote

$180K - $220K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 4 days ago


Key responsibilities

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

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

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


Job description

COMPANY: Canoe Intelligence

WEBSITE: https://canoeintelligence.com/

TITLE: Sr. Machine Learning Engineer

LOCATION: Hybrid in New York City or London

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.