Machine Learning Engineer
Remote · USFull-timeSeen todaySeen in employer's feed today
Most applications go out cold — see where you stand first. No sign-up to start.
You apply on the employer's site, with no Olive account needed.
Watch jobs like this. New remote roles like this one, by email.
Don't just apply. Show up ready.
Olive works from this exact posting.
At a glance
Olive lists jobs from US employers, including remote roles you can work from the United States.
Job overview
The Machine Learning Engineer designs, builds, and deploys production-grade machine learning models and AI systems within ADT’s Data & AI organization. The role operationalizes research models, automates retraining, and supports secure, scalable, highly available intelligence systems across cloud architectures and edge devices. The engineer collaborates with data science, data engineering, software engineering, and product teams to integrate ML capabilities into ADT applications and services.
Skills & qualifications
Skills
Qualifications
Full job description
Summary:
The Machine Learning Engineer will be responsible for designing, building, and deploying production-grade machine learning models and AI systems within ADT's Data & AI organization. This role bridges the gap between data science research and software engineering, taking theoretical models and scaling them to run efficiently across ADT’s vast ecosystem—from high-throughput cloud architectures to edge devices and smart home IoT telemetry.
The ideal candidate is a software-focused engineer with deep expertise in machine learning frameworks, data pipelines, and MLOps practices. You will work closely with Data Scientists, Data Engineers, and Product teams to operationalize AI algorithms, automate model retraining cycles, and ensure our intelligence systems are secure, scalable, and highly available.
Duties and Responsibilities:
-
Production Deployment & Scaling: Operationalize and scale machine learning models into production systems via GCP Vertex AI, ensuring low-latency execution and high availability.
-
MLOps & Pipeline Automation: Design, implement, and maintain robust end-to-end ML pipelines for data preprocessing, continuous model training, and evaluation using Vertex AI Pipelines (Kubeflow/TFX) and GCP CI/CD toolchains.
-
Model Optimization: Optimize ML algorithms, large language models (LLMs), and deep learning models for computational efficiency using GCP infrastructure (GPUs/TPUs) and edge-computing constraints.
-
Monitoring & Drift Detection: Build tracking and observability systems to monitor production model performance, logging, and data/concept drift leveraging Vertex AI Model Monitoring and Google Cloud Storage solutions.
-
Infrastructure Collaboration: Partner with data engineering and platform teams to design, manage, and optimize scalable feature stores (Vertex AI Feature Store) and high-dimensional analytical data warehouses (BigQuery).
-
Cross-Functional Integration: Collaborate with software engineering and product development teams to embed ML capabilities into customer-facing ADT applications and core services via microservices/APIs.
-
Code Quality & Best Practices: Champion rigorous software engineering principles within the data science organization, including modular code design, comprehensive testing (unit/integration), and technical documentation.
-
Continuous Innovation: Stay current with the rapidly evolving ML engineering landscape, adopting cutting-edge tools, framework upgrades, and hardware accelerations to keep ADT competitive.
Minimum Qualifications:
-
Education: Bachelor’s degree (or equivalent experience) in Computer Science, Software Engineering, Data Science, Mathematics, or a highly quantitative field. (Master's degree preferred).
-
Experience: 3+ years of professional software engineering or machine learning engineering experience, with a proven track record of deploying models to production environments.
-
Technical Proficiency:
-
Advanced, production-grade proficiency in programming languages like Python, with strong object-oriented design skills.
-
Deep experience with core ML frameworks and libraries (e.g., PyTorch, TensorFlow, Scikit-Learn, XGBoost).
-
Strong familiarity with Google Cloud Platform (GCP), specifically Vertex AI (Pipelines, Feature Store, Model Registry), BigQuery, and Google Cloud Storage (GCS).
-
Solid experience constructing high-dimensional data pipelines using SQL, Spark on GCP, or Google Cloud Dataflow.
-
Software Practices: Strong understanding of Docker, Kubernetes (Google Kubernetes Engine - GKE), MLOps, CI/CD toolchains, and Bitbucket.
-
Ambiguity Management: Versatility to manage multiple technical priorities, apply structure to complex engineering challenges, and thrive in a fast-paced environment.
Required Licensing or Certifications:
- Highly Preferred: Google Cloud Certified Professional Machine Learning Engineer or Professional Cloud Architect
Communication Skills:
- Writing, Talking/Hearing on the phone (Continually=67-100% of workday)
Environment Requirements:
- Remote/Home office (Continually=67-100% of the workday)
Travel:
- Occasionally, less than 25%
Job Identification: 3021789
Job Schedule: Full time
How this job compares
Set against the 6,060 open Machine Learning Engineer jobs on Olive Jobs, counted November 5, 2026.
- Pay
- This postingNot listed
- Texas median$126–224K/yr193 salary ranges
- US median$31–77/hr137 hourly ranges
- Openings
- Olive Jobs lists 279 open Machine Learning Engineer jobs in Texas and 6,060 across the US. 8% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 3 years of experience; Machine Learning Engineer postings that state a minimum ask for 5 years at the median (4,865 postings).
- Education
- 2,211 of the 6,060 open Machine Learning Engineer jobs (36%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 5, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
Prepare for this role
You've read the whole posting — now see how you match it.
