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Remote Google Cloud Machine Learning Engineer Jobs in Washington

NewGen

Data Engineer

Herndon, VA · On-site +1

$117K - $141K/yr

Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer, Azure Data ...

Platinum Technologies

AI Engineer

Washington, DC · On-site +1

$160K - $180K/yr

... machine learning, cloud-native architecture, and Generative AI technologies to help drive ... This role is remote/hybrid in the VA/MD/DC area. There may be occasional travel to client site in ...

Knexus

AI Software Engineer (Remote)

Vienna, VA · Remote

This role involves leveraging cutting-edge technologies, including GenAI and machine learning ... Google Cloud Platform : Experience using and deploying technologies onto GCP, existing GCP ...

Autodesk

Senior Machine Learning Test Engineer

Washington, DC · On-site +1

$125K - $162K/yr

Job Requisition ID # 26WD98377 Senior Machine Learning Test Engineer Location: United States East ... Experience with cloud providers (e.g., AWS, Azure, Google Cloud Platform) * Experience testing ML ...

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Remote Google Cloud Machine Learning Engineer information

What is the difference between Remote Google Cloud Machine Learning Engineer vs Remote AWS Machine Learning Engineer?

AspectRemote Google Cloud Machine Learning EngineerRemote AWS Machine Learning Engineer
Required CredentialsGoogle Cloud certifications, Python, ML frameworksAWS certifications, Python, ML frameworks
Work EnvironmentGoogle Cloud Platform, GCP toolsAWS Cloud, AWS tools
Industry UsageTech, finance, healthcare using GCPTech, retail, finance using AWS
Search & Comparison IntentHigh overlap in cloud-based ML rolesSimilar roles in cloud ML, different platform

Both roles involve developing machine learning models in cloud environments, requiring cloud platform certifications and expertise in Python and ML frameworks. The main difference lies in the cloud platform used: Google Cloud vs AWS. Candidates should choose based on their platform familiarity and employer requirements.

How does a Remote Google Cloud Machine Learning Engineer typically collaborate with cross-functional teams?

As a Remote Google Cloud Machine Learning Engineer, collaboration often happens through virtual meetings, shared documentation, and cloud-based development environments. You'll regularly interact with data scientists, software developers, and product managers to align machine learning solutions with business objectives. Clear communication and proactive updates are essential, as you may work across time zones and need to coordinate on project requirements, data pipelines, and model deployment strategies. Tools such as Google Meet, Slack, and shared code repositories like Git are commonly used to facilitate seamless teamwork.

What does a Remote Google Cloud Machine Learning Engineer do?

A Remote Google Cloud Machine Learning Engineer designs, develops, and deploys machine learning models on Google Cloud Platform (GCP) from a remote location. They work with cloud-based tools and services such as TensorFlow, Vertex AI, BigQuery, and Dataflow to build scalable, production-ready ML solutions. Their responsibilities also include data preprocessing, model training and evaluation, and integrating ML solutions with other cloud services. Collaboration with data scientists, software engineers, and stakeholders is a key part of the role, ensuring that ML solutions meet business goals while leveraging the full capabilities of Google Cloud.

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

To thrive as a Remote Google Cloud Machine Learning Engineer, you need expertise in machine learning algorithms, data analysis, and proficiency in programming languages like Python, along with a degree in computer science or a related field. Familiarity with Google Cloud Platform (GCP) services such as Vertex AI, BigQuery, and TensorFlow, as well as relevant certifications like Google Professional Machine Learning Engineer, is highly valued. Strong problem-solving skills, self-motivation, and effective remote communication set top performers apart in this role. These competencies are critical for building scalable ML solutions, collaborating remotely, and delivering impactful results using cloud technologies.
What are the most commonly searched types of Google Cloud Machine Learning Engineer jobs in Washington? The most popular types of Google Cloud Machine Learning Engineer jobs in Washington are:
What cities in Washington are hiring for Remote Google Cloud Machine Learning Engineer jobs? Cities in Washington with the most Remote Google Cloud Machine Learning Engineer job openings:
Remote Google Cloud Machine Learning Engineer jobs near you
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Clearview AI

Washington, DC • On-site, Remote

$180K - $250K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 17 days ago

Job description

Senior Machine Learning Engineer
Department: Engineering
Employment Type: Full Time
Location: Remote USA
Compensation: $180,000 - $250,000 / year
Description
Clearview AI is the leading provider of facial recognition technologies to US law enforcement, state, and federal agencies. Our mission is to help our users solve crimes and prevent financial fraud with the responsible use of our facial recognition software. Our company is a high-octane, fast growing startup looking to hire enthusiastic and intelligent team members to join our team. To learn more about us, and our revolutionary facial recognition technology, please visit www.clearview.ai.
Senior Machine Learning Engineer
Position Summary: We are hiring a highly technical individual contributor to push the limits of our computer vision and machine learning capabilities. This is a high-impact, hands-on role for a research-minded engineer who wants to build and ship models, not manage a team. Much of the work involves large-scale visual understanding, extracting structured signals from imagery and reasoning about the real-world context behind a photograph, but we care more about deep ML/CV ability than any one problem area and welcome strong generalists.
Responsibilities:
  • Build, train, evaluate, and deploy computer vision and multimodal models, taking them from early prototype through to production
  • Design systems that infer structured attributes and spatial context from imagery, combining learned models with geometric and heuristic reasoning
  • Train and fine-tune models on large, diverse real-world image datasets, and build the pipelines to curate and label that data at scale
  • Work with vision-language models (VLMs) and build rigorous evaluation frameworks to measure their accuracy on our tasks
  • Develop and benchmark high-performance image retrieval capabilities with embedding models and vector indexing strategies
  • Optimize models for inference latency and throughput using techniques like distillation, quantization, and GPU acceleration
  • Read current research, prototype novel algorithms from academic literature, and turn promising ideas into reliable production code
  • Implement efficient, scalable data pipelines and inference infrastructure
  • Develop high-performance tooling in ML and data engineering
  • Additional duties and responsibilities as reasonably required by the employee's supervisor or CEO

Requirements and Experience
Requirements:
  • Experience building, training, evaluating, and deploying ML models in production
  • Strong experience using PyTorch, JAX, or other deep learning frameworks to develop and optimize models
  • Strong software engineering ability to build and maintain complex systems and work with large-scale datasets
  • Ability to solve open-ended problems and quickly learn new domains
  • Comfort operating with significant ownership and autonomy, making pragmatic trade-offs between model sophistication, velocity, inference and business constraints
  • BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience

Nice to have:
  • Experience inferring structured, real-world attributes from images
  • Experience training models on large-scale, real-world image datasets
  • Familiarity with vision-language models (VLMs)
  • Ability to digest academic literature, prototype novel algorithms, and bridge the gap between research and production code
  • Experience building LLM or VLM pipelines and the evaluation frameworks to measure their performance
  • Experience in an ML role at a growth-stage startup
  • Publications in major ML or computer vision conferences (e.g., CVPR, ICML, ICCV, WACV)

Benefits
  • Medical, Dental, Vision, STD and LTD Plans
  • FSA - Medical and Dependent Care
  • EAP and wellness programs
  • 13 Paid Holidays
  • Unlimited PTO
  • Flexible work environment - 100% remote
  • 401(k) plan

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