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Remote Machine Learning Postdoc Jobs in New York

We have a flexible work environment and allow remote work depending on one's personal choice. Responsibilities: As the Machine Learning Ops Engineer for the AI Team you will: * Work closely with the ...

... Hybrid/Remote - NYCNo visa sponsorship available at this time.Our client an up and coming ... Design and implement machine learning algorithms and models for tasks such as classification ...

New

... Hybrid/Remote - NYCNo visa sponsorship available at this time.Our client an up and coming ... Design and implement machine learning algorithms and models for tasks such as classification ...

New

Showing results 41-60

Remote Machine Learning Postdoc information

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.

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

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.

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.

What are the most commonly searched types of Machine Learning Postdoc jobs in New York?

The most popular types of Machine Learning Postdoc jobs in New York are:

What cities in New York are hiring for Remote Machine Learning Postdoc jobs?

Cities in New York with the most Remote Machine Learning Postdoc job openings:

Infographic showing various Remote Machine Learning Postdoc job openings in New York as of August 2026, with employment types broken down into 5% Internship, 60% Full Time, 9% Part Time, and 26% Contract. Highlights an 100% Remote job distribution.

Data Scientist Manager - Eso (Remote)

Quest Diagnostics

Secaucus, NJ • On-site, Remote

Full-time

Re-posted 20 days ago


ExamOne rating

6.6

Company rating: 6.6 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

96th of 120 rated laboratories


Job description


Healthcare Analytics Solutions (HAS) is an innovative team within Quest Diagnostics that leverages Quest data to develop products and services to improve outcomes in healthcare across many different markets (Pharma, Clinical Trials, Health Plans/Payers, Hospitals/Health Systems, and Public Health agencies).
Join HAS to build, productionize, and operationalize clinical ML products using billions of results from Quest laboratory data. You will partner with clinicians, engineers, and product teams to deliver robust, compliant, and well-documented models that impact patient care and downstream products. In this role, you will be responsible for understanding and implementing the latest advances in the field of machine learning applied to healthcare use cases. Fully remote, minimal travel required; strong emphasis on hands-on production experience and pragmatic problem solving.
Responsibilities
• Machine learning model garden used to create predictive analytics-based data products in healthcare.
• Monitoring and surveillance of state of art research in machine learning in relation to healthcare and clinical AI. Incorporating innovation as appropriate into our ML garden and solutions.
• Thought leaders support for ML Ops, including containerization, model serving, performance tuning, rollout strategies, and observability (drift, performance, alerts).
• Model governance: reproducibility, versioning, bias/fairness checks, and audit-ready documentation.
• Integration of advanced analytics and machine learning models into business products and services including business intelligence dashboards and real-time analytics.
• Curation of data sets from Quest and non-Quest data sources in support of deriving business insights driven by advanced analytics.
• Cross-functional partnership with clinicians and product owners to define outcomes, acceptance criteria, and validation plans.
• Mentor and raise engineering standards across the team: coding best practices, testing, and deployment patterns.
• Translate technical results into clear explanations and recommendations for technical and executive stakeholders.
Qualifications
• 2+ years evidence gaining deep knowledge about advanced machine learning concepts, new model architectures as well as research-level evaluation of promising model designs and architectures.
• 5+ years relevant experience with Python and SQL; production-grade code and testing practices.
• Practical experience with model serving and monitoring, CI/CD for ML, and feature pipeline orchestration.
• Experience working with healthcare data (labs, EHR, claims) and familiarity with PHI handling/HIPAA considerations.
• Excellent statistics, model evaluation, and pragmatic approach to validation.
• Excellent communication and problem-solving skills; comfortable leading technical discussions with clinicians and engineers and presenting to senior executives
• Excellent scientific writing skills; we may publish studies based on novel models or methods
• A Master's degree from an accredited college or university in a related area of Data Science, Statistics, Computer Science, Mathematics, Economics, or Information Technology. PhD preferred.
Preferred
• Familiarity with major commercial data platforms, including Google cloud AI solutions.
• Prior experience in regulated environments or deploying clinical decision support tools.
• Demonstrated ability to leverage data visualization tools and software to present advanced analytics that are easy to interpret and spot patterns, trends, and correlations
• Aptitude in other programing languages like R, SAS, JavaScript
Why join this team?
• High-impact work across products and markets; you have the opportunity to meaningfully improve patient outcomes and healthcare delivery in the United States in this role
• Fully remote, collaborative team.
• Opportunity to define production ML standards.
• Clear ownership of end-to-end model lifecycle and opportunity to mentor others.
About the Team
Quest Diagnostics honors our service members and encourages veterans to apply.
While we appreciate and value our staffing partners, we do not accept unsolicited resumes from agencies. Quest will not be responsible for paying agency fees for any individual as to whom an agency has sent an unsolicited resume.
Equal Opportunity Employer: Race/Color/Sex/Sexual Orientation/Gender Identity/Religion/National Origin/Disability/Vets or any other legally protected status.

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