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Remote Spacex Machine Learning Jobs in New York (NOW HIRING)

Career Renew

Senior Machine Learning Engineer

New York, NY · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ...

Etsy

Senior Staff Machine Learning Engineer

New York, NY · On-site +1

$245K - $319K/yr

This position will be in Brooklyn, NY or for remote candidates based in the United States. Etsy is ... A foundational and practical understanding of machine learning principles and the critical steps ...

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Remote Spacex Machine Learning information

What does a remote SpaceX machine learning engineer do?

A Remote SpaceX Machine Learning Engineer uses data-driven algorithms and models to solve complex problems for SpaceX, often focusing on areas such as rocket manufacturing, satellite communications, and mission planning. Working remotely, these engineers collaborate with cross-functional teams to design, develop, and implement machine learning solutions that improve efficiency, safety, and performance. They may analyze large datasets, build predictive models, and deploy AI systems to support SpaceX's ambitious goals in space exploration.

What are some unique challenges of working remotely as a machine learning engineer at SpaceX, and how can candidates prepare for them?

Working remotely as a Machine Learning Engineer at SpaceX presents unique challenges such as collaborating across distributed teams, managing time zones, and maintaining effective communication with colleagues involved in hardware and aerospace projects. To succeed, candidates should be proactive in seeking regular updates, use collaborative tools efficiently, and be comfortable working independently while still aligning with team objectives. Familiarity with remote development environments and a strong ability to document and present complex models are also key to thriving in this role.

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

AspectRemote Spacex Machine LearningRemote Spacex Data Scientist
Required CredentialsAdvanced degree in Computer Science, AI, or related field; experience in ML frameworksDegree in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentDeveloping ML models, algorithms, and AI systems for space applicationsAnalyzing data, creating insights, and supporting decision-making processes
Employer & Industry UsageUsed in AI-driven space missions, autonomous systems, and roboticsApplied in data analysis, reporting, and predictive modeling for space projects

Remote Spacex Machine Learning specialists focus on developing AI models for space technology, while Data Scientists analyze data to inform decisions. Both roles require strong technical skills and often collaborate but serve different core functions within the industry.

What are the key skills and qualifications needed to thrive as a remote SpaceX machine learning engineer?

To excel as a Remote SpaceX Machine Learning Engineer, you need strong expertise in machine learning, data analysis, and programming languages like Python, along with a relevant degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, cloud computing platforms, and version control systems is typically necessary, and certifications in machine learning or data science can be advantageous. Excellent problem-solving skills, strong communication, and the ability to collaborate remotely are key soft skills that help you stand out. These skills ensure you can develop robust ML models that support SpaceX’s technical goals while effectively working within distributed teams.
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Remote Spacex Machine Learning jobs near you

Sr. Lead Machine Learning Engineer/Remote

Apetan Consulting llc

Paterson, NJ • Remote

$80 - $150/hr

Contractor

Re-posted 21 days ago

Job description

Sr. Lead Machine Learning EngineerLocation-RemoteJob Summary

The Sr. Lead Machine Learning Engineer is responsible for leading the design, development, deployment, and optimization of machine learning solutions that drive business value. This role combines technical expertise, strategic leadership, and cross-functional collaboration to build scalable AI/ML systems, mentor engineering teams, and guide the organization's machine learning initiatives.

Key Responsibilities
  • Lead the development and deployment of machine learning models and AI-driven solutions.
  • Design scalable ML architectures, pipelines, and production-ready systems.
  • Collaborate with data scientists, software engineers, product managers, and business stakeholders to define and deliver ML solutions.
  • Oversee data preparation, feature engineering, model training, evaluation, and monitoring processes.
  • Optimize model performance, scalability, reliability, and operational efficiency.
  • Establish best practices for MLOps, model governance, testing, and deployment.
  • Conduct code reviews and provide technical leadership and mentorship to engineering teams.
  • Evaluate emerging AI/ML technologies and recommend innovative solutions.
  • Ensure compliance with security, privacy, and responsible AI standards.
  • Support production systems by troubleshooting and resolving complex ML-related issues.
  • Drive technical roadmaps and contribute to strategic AI initiatives.
Required Qualifications
  • Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.
  • 8+ years of software engineering experience, including 5+ years in machine learning engineering.
  • Strong proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Experience building and deploying machine learning models in production environments.
  • Strong knowledge of data structures, algorithms, statistics, and machine learning techniques.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud.
  • Knowledge of MLOps tools, CI/CD pipelines, and model monitoring practices.
  • Excellent leadership, communication, and problem-solving skills.
Preferred Qualifications
  • Master’s degree or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
  • Experience with large-scale distributed systems and big data technologies.
  • Knowledge of Generative AI, Large Language Models (LLMs), NLP, computer vision, or recommendation systems.
  • Experience with Kubernetes, Docker, and cloud-native architectures.
  • Prior experience leading technical teams and enterprise AI initiatives.