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Aws Sagemaker Machine Learning Remote Jobs (NOW HIRING)

Lead Machine Learning Engineer - REMOTE

Lynn, MA · Remote

$105K - $139K/yr

Join a Company that Empowers you to Build your Future Lennar is seeking a Machine Learning Engineer ... They are hands-on with AWS SageMaker (including SageMaker Unified Studio), MLflow, Weights & Biases ...

Lead Machine Learning Engineer - REMOTE

Boston, MA · Remote

$107K - $142K/yr

Join a Company that Empowers you to Build your Future Lennar is seeking a Machine Learning Engineer ... They are hands-on with AWS SageMaker (including SageMaker Unified Studio), MLflow, Weights & Biases ...

Summary We're looking for a Machine Learning Engineer to design, deploy, and operate production ML ... Build and maintain reproducible model training workflows on AWS (SageMaker, S3, Glue, etc.), making ...

Summary We're looking for a Machine Learning Engineer to design, deploy, and operate production ML ... Build and maintain reproducible model training workflows on AWS (SageMaker, S3, Glue, etc.), making ...

$228K - $253K/yr

... Databricks, AWS, Sagemaker, etc. As a Principal Machine Learning Engineer, you will act as a ... Remote options are available for the following states - AZ, AR, CA, FL, GA, IL, IN, IA, KS, MD, MA ...

$206K - $230K/yr

... AWS, Sagemaker, etc. As a Staff Machine Learning Engineer, you will act as a technical leader and ... Remote options are available for the following states - AZ, AR, CA, FL, GA, IL, IN, IA, KS, MD, MA ...

Lead Machine Learning Engineer - REMOTE

Boston, MA · On-site +1

$111K - $146K/yr

Join a Company that Empowers you to Build your Future Lennar is seeking a Machine Learning Engineer ... They are hands-on with AWS SageMaker (including SageMaker Unified Studio), MLflow, Weights & Biases ...

Remote - United States Employment type: Contract (6-12+ months) Indicative rate: $85-$115/hr Role ... Experience deploying models to cloud (AWS, Azure, or GCP) * Familiarity with LLMs, embeddings, and ...

Machine Learning Engineer 3-7881

Philadelphia, PA · On-site +1

$115K - $138K/yr

Position is eligible for 100% remote work. REQUIREMENTS:Master's degree, or foreign equivalent, in ... TensorFlow, PyTorch, Scikit-learn, or XGBoost; using AWS services including SageMaker; modeling ...

PyTorch, Python, GitHub, Snowflake, Huggingface Transformers, AWS Sagemaker, Microsoft DeepSpeed ... Remote-First Team - Work from anywhere in the U.S. * Unlimited PTO & 10 Holidays - So you can relax ...

Role We are looking for a Staff Machine Learning Engineer to help us build a best in class ML ... AWS, MLflow, Sagemaker, Terraform, K8s Compensation $230-260k base salary #LI-Remote Benefits

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Aws Sagemaker Machine Learning Remote information

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$25.5K

$42.6K

$88K

How much do aws sagemaker machine learning remote jobs pay per year?

As of Jun 8, 2026, the average yearly pay for aws sagemaker machine learning remote in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What is the difference between Aws Sagemaker Machine Learning Remote vs Data Scientist?

AspectAws Sagemaker Machine Learning RemoteData Scientist
Required CredentialsAWS certifications, machine learning knowledgeStatistics, data analysis, programming skills
Work EnvironmentRemote, cloud-based platformsOffice or remote, research and analysis focus
Industry UsageTech, cloud services, AI projectsFinance, healthcare, marketing, research
Common Search/ComparisonYesYes

While Aws Sagemaker Machine Learning Remote specialists focus on deploying and managing ML models on AWS cloud, Data Scientists analyze data to generate insights and develop models. Both roles require technical skills, but differ in their primary focus and work environment.

What is an AWS SageMaker Machine Learning Remote job?

An AWS SageMaker Machine Learning Remote job involves working with Amazon SageMaker, a cloud-based machine learning platform, to build, train, and deploy machine learning models, all while working remotely. Professionals in this role use SageMaker’s tools and services to handle data preprocessing, model development, and model deployment at scale. They collaborate with data scientists, engineers, and stakeholders, all through remote communication tools, making it possible to work from anywhere. This job typically requires knowledge of Python, machine learning frameworks, and experience with AWS cloud services.

What are some common challenges faced by AWS SageMaker Machine Learning engineers working remotely, and how can they be addressed?

One common challenge for AWS SageMaker Machine Learning engineers working remotely is ensuring seamless collaboration with cross-functional teams, such as data engineers, DevOps, and stakeholders, due to the distributed work environment. Effective communication tools, regular virtual meetings, and clear documentation can help bridge gaps and prevent misunderstandings. Additionally, managing cloud resources and costs remotely requires diligent monitoring and adherence to best practices. Staying updated on AWS SageMaker releases and participating in online knowledge-sharing sessions also helps maintain productivity and innovation.

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

To thrive as an AWS SageMaker Machine Learning Engineer, you need a strong background in machine learning algorithms, Python programming, and experience with cloud-based deployment, typically supported by a degree in computer science or related field. Familiarity with AWS SageMaker, cloud infrastructure (like EC2, S3), and relevant certifications such as AWS Certified Machine Learning – Specialty are highly valuable. Strong problem-solving, communication, and self-motivation are crucial soft skills for effective remote collaboration and project delivery. These competencies ensure the ability to build, deploy, and maintain scalable machine learning solutions while working efficiently in distributed teams.
Infographic showing various Aws Sagemaker Machine Learning Remote job openings in the United States as of May 2026, with employment types broken down into 9% Locum Tenens, 5% Internship, 58% As Needed, 14% Full Time, 9% Temporary, and 5% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Lead Machine Learning Engineer - REMOTE

Lead Machine Learning Engineer - REMOTE

Lennar Homes

Lynn, MA • Remote

$105K - $139K/yr

Full-time

Medical, Dental, Vision, Retirement

Posted 4 days ago


Lennar rating

7.8

Company rating: 7.8 out of 10

Based on 44 frontline employees who took The Breakroom Quiz

25th of 78 rated construction


Job description

Lead ML Engineer - REMOTE We are Lennar Lennar is one of the nation's leading homebuilders, dedicated to making an impact and creating an extraordinary experience for their Homeowners, Communities, and Associates by building quality homes and providing exceptional customer service, giving back to the communities in which we work and live in, and fostering a culture of opportunity and growth for our Associates throughout their career. Lennar has been recognized as a Fortune 500® company and consistently ranked among the top homebuilders in the United States. Join a Company that Empowers you to Build your Future Lennar is seeking a Machine Learning Engineer to own and evolve the infrastructure and surface mechanisms that take our data science and ML models from notebook to production.

This is a key role on the Applied AI & Data Science team, sitting at the intersection of software engineering, ML platform, and applied data science. The ideal candidate is a software engineer with deep MLOps expertise. They know how to design model serving for both batch and real-time inference, build durable model registries and versioning practices, and stand up retraining pipelines that data scientists actually use.

They are hands-on with AWS SageMaker (including SageMaker Unified Studio), MLflow, Weights & Biases, and the surrounding tooling that makes ML systems reliable in production. You’ll partner closely with data scientists, AI engineers, and platform teams—building and setting the foundation that lets ML models ship faster, retrain on schedule, and operate with the same engineering rigor as any other production service across 40+ divisions of one of the nation’s largest homebuilders. A career with purpose.

A career built on making dreams come true. A career built on building zero defect homes, cost management, and adherence to schedules. Your Responsibilities on the Team Design, build, and set the ML platform surface used by our data science team—covering model packaging, deployment, batch and real-time inference, and observability.

Establish and evangelize ML platform standards, patterns, and reusable components—raising the engineering bar for how ML models are built, deployed, and operated across the organization. Mentor data scientists and engineers on production ML practices, code review their platform-adjacent work, and serve as the technical authority on MLOps decisions. Own model serving infrastructure on AWS SageMaker (including SageMaker Unified Studio)—building patterns for batch inference jobs, real-time endpoints, and serverless inference depending on workload requirements.

Build and maintain the model registry, version control, and promotion workflows that move models cleanly from development to staging to production with full lineage and auditability. Stand up and operate retraining pipelines using MLflow, Weights & Biases, and orchestration tools—automating retraining triggers, experiment tracking, model evaluation, and approval gates. Build monitoring and alerting for production models including drift detection, performance degradation, data quality issues, and latency or cost anomalies.

Write clean, modular Python and infrastructure-as-code (Terraform) for ML platform components, applying software engineering best practices including testing, versioning, and code review. Partner closely with data scientists to make their workflow faster and more reliable—reducing time-to-production for new models and increasing confidence in models already in production. Collaborate with Data / Platform Engineering and AI Engineering counterparts to ensure feature pipelines, model artifacts, and inference services are integrated cleanly with the broader data and AI platform.

Requirements Bachelor’s degree or higher in Computer Science, Engineering, or a related technical field. 7+ years of software engineering experience, including meaningful production ownership of services or platforms in a cloud environment. 5+ years of hands-on MLOps or ML platform experience—deploying, monitoring, and retraining production models at scale.

Strong hands-on experience with AWS SageMaker (Unified Studio strongly preferred), including model training jobs, endpoints, batch transform, and pipelines. Deep experience with experiment tracking, model registries, and retraining workflows using MLflow, Weights & Biases, or comparable tooling. Strong Python skills with a track record of writing modular, well-tested, production-ready code; experience with infrastructure-as-code (Terraform preferred).

Solid understanding of both batch and real-time inference patterns, including the tradeoffs between latency, throughput, cost, and operational complexity. Proven ability to partner with data scientists—understanding their workflow, lowering friction, and translating modeling needs into reliable platform capabilities. Comfortable operating with autonomy in ambiguous environments—scoping work, setting realistic timelines, and raising blockers proactively without waiting to be asked.

Bonus: Experience with feature stores, model gateways, GPU workloads, distributed training, model drift monitoring tools, or supporting both classical ML and LLM-based models on the same platform. What we offer: The opportunity to deliver impact across one of the largest homebuilders in the United States. A corporate culture focused on growth and development.

Freedom to try new impactful ideas. Ability to deploy your work to teams across 40+ divisions and interact directly with those teams. End-to-end project ownership.

Occasional travel for team activities and meetings. Remote work schedule, with a preference for candidates based in Miami, FL; Bentonville, AR; or Dallas, TX. Healthcare (medical, dental, vision) and 401k matching This information is intended to be a general overview and may be modified by the company due to factors affecting the business.

General Overview of Compensation & Benefits: We reasonably expect the base compensation offered for this position to range from an annual salary of $152,600.00 - $190,700, subject to adjustment based on business-related factors such as employee qualifications, geographic pay differentials (e.g., cost of labor/living, etc.), and operational considerations. This position may be eligible for bonuses. This position may be eligible for commissions.

This position will be eligible for the described benefits listed in the above section in accordance with Company Policy. This information is intended to be a general overview and may be modified by the Company due to factors affecting the business. Life at Lennar At Lennar, we are committed to fostering a supportive and enriching environment for our Associates, offering a comprehensive array of benefits designed to enhance their well-being and professional growth.

Our Associates have access to robust health insurance plans, including Medical, Dental, and Vision coverage, ensuring their health needs are well taken care of. Our 401(k) Retirement Plan, complete with a $1 for $1 Company Match up to 5%, helps secure their financial future, while Paid Parental Leave and an Associate Assistance Plan provide essential support during life's critical moments. To further support our Associates, we provide an Education Assistance Program and up to $30,000 in Adoption Assistance, underscoring our commitment to their diverse needs and aspirations.

From the moment of hire, they can enjoy up to three weeks of vacation annually, alongside generous Holiday, Sick Leave, and Personal Day policies. Additionally, we offer a New Hire Referral Bonus Program, significant Home Purchase Discounts, and unique opportunities such as the Everyone’s Included Day. At Lennar, we believe in investing in our Associates, empowering them to thrive both personally and professionally.

Lennar Associates will have access to these benefits as outlined by Lennar’s policies and applicable plan terms. Visit Lennartotalrewards.com to view our suite of benefits. Join the fun and follow us on social media to see what's happening at our company, and don't forget to connect with us on Lennar: Overview | LinkedIn for the latest job opportunities.

Lennar is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws.


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About Lennar

Sourced by ZipRecruiter

Since 1954, Lennar has built over one million new homes for families across America. We build in some of the nation’s most popular cities, and our communities cater to all lifestyles and family dynamics, whether you are a first-time or move-up buyer, multigenerational family, or Active Adult.

Industry

Construction

Company size

5,001 - 10,000 Employees

Headquarters location

Miami, FL, US

Year founded

1954

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