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Remote Machine Learning Postdoc Jobs in McLean, VA

Elder Research

Data Scientist (Remote)

Arlington, VA · On-site +1

Maintain current in emerging tools and techniques in machine learning, statistical modeling, and ... Washington DC Metro Area - Remote (candidates MUST BE located in the National Capital Region - DMV ...

Elder Research

Data Scientist (Remote)

Arlington, VA · On-site +1

Maintain current in emerging tools and techniques in machine learning, statistical modeling, and ... Washington DC Metro Area - Remote (candidates MUST BE located in the National Capital Region - DMV ...

Knexus

AI Software Engineer (Remote)

Vienna, VA · Remote

This is a fully remote position for candidates in the continental U.S., with work hours aligned to ... This role involves leveraging cutting-edge technologies, including GenAI and machine learning ...

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Remote Machine Learning Postdoc information

Is ML a high paying job?

Machine learning postdoctoral positions are generally well-paid compared to many academic roles, with salaries often ranging from $60,000 to over $100,000 annually depending on experience, location, and funding. These roles typically require strong programming skills in Python or R and knowledge of algorithms and data analysis, which can contribute to higher compensation levels.

Is a PhD in ML worth it?

A PhD in machine learning can enhance qualifications for a remote machine learning postdoc position, often leading to higher-level research opportunities and increased earning potential. However, it requires significant time investment and may not be necessary for industry roles that value practical skills and experience with tools like Python and TensorFlow. The decision depends on career goals and the specific requirements of the desired position.

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

A Remote Machine Learning Postdoc requires a PhD in computer science, statistics, or a related field, with expertise in machine learning algorithms, statistical modeling, and research methodologies. Proficiency in programming languages like Python or R, experience with machine learning frameworks such as TensorFlow or PyTorch, and familiarity with version control systems (e.g., Git) are typically necessary. Strong written and verbal communication, self-motivation, and collaboration skills are vital for remote research and effective teamwork. These capabilities enable impactful independent research, smooth collaboration across distributed teams, and the successful dissemination of findings to the wider scientific community.

Is a postdoc harder than a PhD?

A remote machine learning postdoc typically involves more specialized research, higher expectations for independence, and often requires advanced skills in programming and data analysis. While a PhD focuses on completing a dissertation and gaining foundational expertise, a postdoc emphasizes producing publishable research and may involve longer hours and greater responsibility, making it generally more demanding in terms of research output and expertise. However, the difficulty varies based on individual experience and research environment.

What is a Remote Machine Learning Postdoc?

A Remote Machine Learning Postdoc is a postdoctoral researcher specializing in machine learning who works predominantly or entirely from a location outside their host institution, often from home. Their work involves conducting advanced research, developing new algorithms, analyzing data, and publishing findings related to machine learning while collaborating virtually with faculty and research teams. This role is ideal for researchers seeking flexibility or those who cannot relocate but wish to contribute to academic or industrial research from a distance.

Do you need H-1B for postdoc?

A remote machine learning postdoctoral position typically does not require H-1B sponsorship if the candidate is already authorized to work in the country, such as through a visa or citizenship. However, international candidates may need H-1B or other work visas depending on the employer and local immigration laws. Employers often sponsor visas for postdocs to comply with legal requirements and facilitate employment.

What are some common challenges faced by remote machine learning postdocs when collaborating with research teams?

Remote machine learning postdocs often encounter challenges related to communication and coordination, especially when working across different time zones or with teams that have varying schedules. Effective collaboration usually requires proactive communication through virtual meetings, shared code repositories, and regular progress updates. Building rapport with colleagues and staying engaged with ongoing research discussions can take extra effort remotely, but leveraging collaborative tools and participating in virtual seminars or group chats can help bridge the gap. Being organized and self-motivated is key to ensuring productive contributions to the team’s research objectives.
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Remote Machine Learning Postdoc jobs near you
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Clearview AI, Inc.

Washington, DC • Remote

$118K - $162K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 22 days ago

Job 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:
  • 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)
  • 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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