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Mle Jobs (NOW HIRING)

Ancestry

Machine Learning Engineer, Co-op

The MLE team is responsible for developing, deploying, fine-tuning and optimizing machine learning models and LLMs to enhance customer experiences, improve internal workflows, and drive business ...

Ancestry

Machine Learning Engineer, Co-op

Lehi, UT · Remote

The MLE team is responsible for developing, deploying, fine-tuning and optimizing machine learning models and LLMs to enhance customer experiences, improve internal workflows, and drive business ...

Plaid Inc

Staff Product Manager, Risk Platform

New York, NY · On-site

$207K - $306K/yr

Define and prioritize the DS/MLE roadmap for network-health detection - articulate model investment needs, own precision/recall tradeoff decisions, and translate model outputs into real-time product ...

Plaid Inc

Staff Product Manager, Risk Modeling

New York, NY · On-site

$207K - $306K/yr

Define and prioritize the DS/MLE roadmap for network-health detection - articulate model investment needs, own precision/recall tradeoff decisions, and translate model outputs into real-time product ...

NOUS RESEARCH

Machine Learning Engineer

New York, NY · On-site

We're looking for an MLE to scale the training and deployment of large transformer-based models. You'll work across training infrastructure, inference optimization, and reinforcement learning ...

Nvidia

Principal Software Engineer - GT Data

Santa Clara, CA · On-site

$158K - $212K/yr

Serving as a key technical hub for GT in the US, you will work across hardware, system, infrastructure, MLE and operations teams to resolve the upstream blockers that affect GT production. The role ...

Intuit

Senior Staff AI/MLE Scientist

San Diego, CA · On-site

$210K - $284K/yr

We're scaling our machine learning capabilities, and we're looking for a Senior Staff Data Scientist - Machine Learning to take the lead. This role owns the end-to-end ML stack that powers production ...

Intuit

Senior Staff AI/MLE Scientist

San Diego, CA · On-site

$210K - $284K/yr

Overview We're scaling our machine learning capabilities, and we're looking for a Senior Staff Data Scientist - Machine Learning to take the lead. This role owns the end-to-end ML stack that powers ...

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How much do mle jobs pay per hour?

As of Jul 15, 2026, the average hourly pay for mle in the United States is $26.34, according to ZipRecruiter salary data. Most workers in this role earn between $15.14 and $30.77 per hour, depending on experience, location, and employer.

What is the difference between Mle vs Mechanical Engineer?

AspectMleMechanical Engineer
Required CredentialsTypically requires a degree in data science, computer science, or related fields; certifications in machine learning or AI are commonRequires a degree in mechanical engineering; professional engineering (PE) license may be preferred
Work EnvironmentPrimarily in tech companies, research labs, or AI-focused firms; involves programming and data analysisManufacturing, design firms, or industrial settings; involves design, testing, and manufacturing processes
Employer & Industry UsageUsed in tech, AI, and data-driven industriesUsed in manufacturing, automotive, aerospace, and industrial sectors

While Mle (Machine Learning Engineer) focuses on developing algorithms and models in data science and AI, Mechanical Engineers work on designing and building physical systems and machinery. Both roles require technical skills but differ significantly in their work environment, credentials, and industry applications.

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

To thrive as a Machine Learning Engineer, you need a solid background in computer science, mathematics, and statistics, often supported by a relevant degree or equivalent experience. Proficiency with programming languages like Python or R, frameworks such as TensorFlow or PyTorch, and experience with version control and cloud platforms are commonly required. Strong problem-solving abilities, effective communication, and a collaborative mindset are standout soft skills in this role. These skills are vital for building robust machine learning models, translating complex data into actionable insights, and working effectively within multidisciplinary teams.

What are the most common challenges machine learning engineers face when deploying models to production?

Machine learning engineers (MLEs) often encounter challenges such as ensuring model scalability, maintaining performance in real-world data environments, and managing model monitoring post-deployment. Integrating models with existing systems and overcoming data drift or changes in user behavior can also be complex. Collaboration with software engineers and data scientists is crucial to address these issues, as is adopting robust MLOps practices to streamline deployment and monitoring processes.

What are MLEs (Machine Learning Engineers)?

Machine Learning Engineers (MLEs) are professionals who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready applications. MLEs collaborate with data scientists to implement algorithms, manage data pipelines, and optimize the performance of machine learning models. Their role is essential in bringing AI solutions from research to real-world use cases.
More about Mle jobs
What job categories do people searching Mle jobs look for? The top searched job categories for Mle jobs are:
Mle jobs near you
Infographic showing various Mle job openings in the United States as of July 2026, with employment types broken down into 97% Full Time, 1% Temporary, and 2% Contract. Highlights an 75% Physical, 1% Hybrid, and 24% Remote job distribution, with an average salary of $54,791 per year, or $26.3 per hour.

Software Engineer II - Python

Rocket Close, LLC

Detroit, MI • On-site

Full-time

Posted 18 days ago

Job description

As a Python Engineer on our Data Science team, you will be at the forefront of our most ambitious technical initiatives. Your role is dual-purposed: you will build and orchestrate next-generation Agentic AI systems using AgentCore and LangGraph, and you will act as a Machine Learning Engineer (MLE) to productionize the sophisticated models developed by our Data Scientists.
We don't just follow industry trends; we aim to set them. You will be expected to be a perpetual student of the field, constantly researching and implementing the newest technologies to ensure our platform remains world-class.
Minimum Qualifications
  • Master's degree in Computer Science, Software Development, Machine Learning, or a related field, OR equivalent professional experience (3-5+ years in production-level engineering).
  • Expert-level proficiency in Python with a focus on building distributed, scalable cloud-native services.
  • Proven experience in a Data Science or Machine Learning environment, specifically in bridging the gap between research code and production software.

Preferred Qualifications
Agentic AI & Orchestration:
  • Hands-on experience with AgentCore runtime for building and managing autonomous agents.
  • Extensive experience using LangGraph to create complex, stateful multi-agent orchestrations with high visibility.
  • Deep familiarity with Amazon Bedrock, OpenAI, or Anthropic APIs and the latest advancements in LLM reasoning.
  • Experience building and optimizing RAG (Retrieval-Augmented Generation) pipelines.

Machine Learning Engineering (MLE):
  • Proven track record of productionizing Data Science models, transforming research-grade code into high-performance, scalable APIs (e.g., using FastAPI).
  • Experience with the full MLOps lifecycle: model deployment, versioning, and performance monitoring.
  • Familiarity with Amazon SageMaker or other cloud-based ML platforms.

Cloud & Infrastructure (AWS):
  • Expertise in the AWS ecosystem: Lambda (Serverless), Step Functions, ECS/EKS (Containers), EventBridge, and S3.
  • Strong proficiency in Infrastructure as Code (IaC) using Terraform.
  • Experience building asynchronous, event-driven architectures.

Observability & Engineering Excellence:
  • Proficiency in Splunk and CloudWatch for production monitoring and alerting.
  • Strong knowledge of software development life cycle (SDLC) processes, including unit testing, regression testing, and Agile concepts.
  • Ability to work with broad, loosely developed concepts and translate them into precise technical specifications.

Key Responsibilities
  • Agentic AI Innovation: Design and implement autonomous agents using AgentCore and orchestrate them via LangGraph to ensure complex workflows are visible and manageable.
  • MLE Productionization: Partner with Data Scientists to take ML models from research notebooks into scalable, production-ready AWS environments.
  • Constant Research: Proactively research, test, and present the newest technologies, frameworks, and AI research papers to the team. You are expected to be an early adopter of tools that can improve our velocity or service quality.
  • System Architecture: Build and maintain the cloud-native infrastructure (AWS) required for AI/ML inference and agentic execution, ensuring high availability and cost-efficiency.
  • Observability: Implement deep monitoring and alerting for all services, using LangGraph for agent-specific visibility and Splunk for broader system health.
  • Code Quality: Participate in rigorous code reviews and help define the engineering standards for the Data Science team.
  • Collaboration: Work without complete specifications to help derive technology solutions that meet the evolving needs of the business.

What you'll get
Our team members fuel our strategy, innovation and growth, so we ensure the health and well-being of not just you, but your family, too! We go above and beyond to give you the support you need on an individual level and offer all sorts of ways to help you live your best life. We are proud to offer eligible team members perks and health benefits that will help you have peace of mind. Simply put: We've got your back. Check out our full list of Benefits and Perks.
On-Call Expectations
This role may include participation in an on-call rotation to support production systems and ensure service reliability. On-call responsibilities may include coverage during nights and weekends. If applicable, frequency and scheduling will be determined by team needs and communicated accordingly.
About us
Rocket Close is a leading national provider of title insurance, property valuations and settlement services. Here, you'll be given all the resources and support needed to deliver innovative solutions and in turn, your hard work will be rewarded with a competitive compensation package and an array of other amazing benefits. Apply today to join a team that offers career growth, amazing benefits and the chance to work with leading industry professionals.
This job description is an outline of the primary responsibilities of this position and may be modified at the discretion of the company at any time. Decisions related to employment are not based on race, color, religion, national origin, sex, physical or mental disability, sexual orientation, gender identity or expression, age, military or veteran status or any other characteristic protected by state or federal law. The company provides reasonable accommodations to qualified individuals with disabilities in accordance with applicable state and federal laws. Applicants requiring reasonable accommodations in completing the application and/or participating in the application process should contact a member of the Human Resources team, at Careers@Rocket.com.

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