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Mlops Machine Learning Engineer Jobs in Utah (NOW HIRING)

Leash Bio

Machine Learning Engineer

Salt Lake City, UT

$150K - $200K/yr

Machine Learning Engineer At Leash Biosciences, we are at the cutting edge of integrating machine ... Familiarity with common MLops tooling (e.g., Dagster, Prefect, Airflow, Docker, MLflow, Kubeflow, W ...

BILL

Senior Machine Learning Engineer

Draper, UT

$97K - $134K/yr

As a Senior Machine Learning Engineer , you'll play a pivotal role in designing, building, and deploying machine learning solutions that power BILL's next-generation products. This is an opportunity ...

BILL

Senior Machine Learning Engineer

Draper, UT · On-site

$114K - $151K/yr

As a Senior Machine Learning Engineer, you will design, build, and deploy machine learning solutions that enhance BILL's products and directly impact user experiences. Responsibilities : • Design ...

Bespoke Labs

Machine Learning Engineer

Ogden, UT

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Orem, UT

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Logan, UT

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

BILL

Senior Machine Learning Engineer

Draper, UT · On-site

$145K - $174K/yr

As a Senior Machine Learning Engineer , you'll play a pivotal role in designing, building, and deploying machine learning solutions that power BILL's next-generation products. This is an opportunity ...

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All JobsMlops Machine Learning Engineer JobsMlops Machine Learning Engineer Jobs in Utah

Mlops Machine Learning Engineer information

Is MLOps harder than DevOps?

MLOps, as a specialized subset of DevOps focused on deploying and maintaining machine learning models, often involves additional challenges such as data management, model versioning, and monitoring. While both require skills in automation, scripting, and cloud environments, MLOps typically demands expertise in machine learning workflows and tools like TensorFlow or PyTorch, making it more complex in certain aspects compared to traditional DevOps.

What does an MLOps Machine Learning Engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

How does an MLOps Machine Learning Engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

Are MLOps engineers in demand?

MLOps engineers are in high demand due to the increasing adoption of machine learning models in various industries. Their skills in deploying, managing, and scaling machine learning systems, along with knowledge of tools like Docker, Kubernetes, and cloud platforms, make them valuable in the job market.

What engineers make $500,000?

Senior machine learning engineers, including those specializing in MLOps, often reach or exceed $500,000 annually with experience, advanced skills, and in high-demand industries like tech or finance. Compensation can include base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

How much do MLOps engineers make?

MLOps engineers typically earn between $100,000 and $150,000 annually, with salaries increasing based on experience, location, and expertise in tools like Kubernetes, Docker, and cloud platforms. Senior roles or those with specialized skills can exceed $180,000 per year.

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

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.
What are popular job titles related to Mlops Machine Learning Engineer jobs in Utah? For Mlops Machine Learning Engineer jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Mlops Machine Learning Engineer jobs in Utah look for? The top searched job categories for Mlops Machine Learning Engineer jobs in Utah are:
What cities in Utah are hiring for Mlops Machine Learning Engineer jobs? Cities in Utah with the most Mlops Machine Learning Engineer job openings:
Mlops Machine Learning Engineer jobs near you
Infographic showing various Mlops Machine Learning Engineer job openings in Utah as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Machine Learning Engineer

Leash Bio

Salt Lake City, UT

$150K - $200K/yr

Other

Medical, Retirement

Posted 14 days ago

Job description

Machine Learning Engineer

At Leash Biosciences, we are at the cutting edge of integrating machine learning with drug discovery. Our unique approach focuses on predicting molecular and protein interactions, aiming to revolutionize the field of medicinal chemistry. Our team prides itself on its ability to generate and analyze vast datasets, directly contributing to groundbreaking advancements in drug development.

We offer a supportive and inclusive environment, encouraging personal agency, collaboration, and sharing of knowledge. We're driven by an ambitious goal, and we aim to inspire each other to achieve groundbreaking results. We take big bets and are okay when only some of them pay off.

Benefits include healthcare, 401K match, stock options, free lunches, and access to some of the best outdoor locations in the country.

The Role:

We are seeking a highly skilled and self-driven Machine Learning Engineer to join our team. In this role, you'll be instrumental in handling enormous datasets, orchestrating cloud-based computing resources, and training a multitude of advanced machine-learning models. Your work will directly contribute to our mission of creating foundational models for medicinal chemistry. While you will be dealing with massive amounts of chemical and biological information, biology and chemistry experience is not required. Our dataset can be thought of as billions of labeled sentences so experience with language models is highly relevant.

Key Responsibilities:
  • Manage and optimize data processing workflows for large-scale datasets, with an approach akin to language data handling.
  • Scale and maintain machine learning model training processes, with a focus on cloud environments (primarily Google Cloud, with flexibility to other platforms).
  • Collaborate closely with ML researchers, data scientists, and lab automation teams to ensure seamless integration of lab data and ML model training.
  • Innovate and iterate on our existing technology stack, taking the initiative to solve problems and improve our ML operations.
  • Act as a self-sufficient project manager, overseeing your projects from conception to completion.
About You:
  • Strong experience in machine learning engineering, including data handling, model training, and scaling in cloud environments.
  • Comfortable building ML infrastructure
  • Experience working with large amounts of text data, NLP, or training LLMs
  • Demonstrated capability to make informed decisions, take ownership of solutions, and drive projects forward in a startup environment.
  • Excellent collaboration skills, with the ability to work effectively with cross-functional teams.
Preferred Qualifications:
  • Familiarity with common MLops tooling (e.g., Dagster, Prefect, Airflow, Docker, MLflow, Kubeflow, W&B, Ray, etc.)
  • Ability to manage own compute cluster
  • Ability to maximize GPU utilization and keep cluster busy 24/7
  • Ability to analyze model results and kick off new experiments in response
  • Experience with BERT or similar language models in PyTorch.
  • Experience or interest in biology, chemistry, or related fields is a plus.

Salary: $150,000 - $200,000 per year

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