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Remote Machine Learning Postdoc Jobs in Ohio (NOW HIRING)

Digital - Principal SRE

Columbus, OH · On-site +1

$53.50 - $71.25/hr

Collaborate with cross-functional teams to integrate machine learning models into production ... Remote roles will also have the opportunity to come together in our offices for moments that matter.

Monday - Friday 8am - 5pm (Onsite 4 days a week) (Possible remote for the right candidate) Position ... machine learning, or generative AI can improve productivity, reduce cost, or unlock new ...

Monday - Friday 8am - 5pm (Onsite 4 days a week) (Possible remote for the right candidate) Position ... machine learning, or generative AI can improve productivity, reduce cost, or unlock new ...

Monday - Friday 8am - 5pm (Onsite 4 days a week) (Possible remote for the right candidate)   ... machine learning, or generative AI can improve productivity, reduce cost, or unlock new ...

Position is currently remote but may work at or visit a facility based on business need. This ... Machine Learning), Informatica PowerCenter and IDR, and legacy on-premise platforms such as ...

May telecommute 100% of the time from their home office, consistent with dunnhumby's remote work ... Applying advanced statistical models and machine learning algorithms to develop and implement ...

Director of Software Engineering

Cleveland, OH · On-site +1

$245.40K/yr

Partner with data and product teams to operationalise machine learning and real-time decisioning ... that allow remote work or office attendance An attractive compensation and benefits package ...

Experience with Databricks workspace administration, machine learning operations (MLOps), or ... remote client service delivery. Recruiting for this role ends on 06/30/2026. Work you'll do As a ...

Experience with Databricks workspace administration, machine learning operations (MLOps), or ... remote client service delivery. Recruiting for this role ends on 06/30/2026. Work you'll do As a ...

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Remote Machine Learning Postdoc information

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.

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 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 most commonly searched types of Machine Learning Postdoc jobs in Ohio? The most popular types of Machine Learning Postdoc jobs in Ohio are:
What job categories do people searching Remote Machine Learning Postdoc jobs in Ohio look for? The top searched job categories for Remote Machine Learning Postdoc jobs in Ohio are:
What cities in Ohio are hiring for Remote Machine Learning Postdoc jobs? Cities in Ohio with the most Remote Machine Learning Postdoc job openings:

Digital - Principal SRE

Huntington

Columbus, OH • On-site, Remote

$53.50 - $71.25/hr

Other

Posted 7 days ago


Job description

Description The Digital - Principal SRE (AI Engineer) role is a position that blends expertise in artificial intelligence, machine learning, and reliability engineering. This professional is responsible for designing, deploying, and maintaining AI-driven solutions while ensuring the reliability, scalability, and performance of digital platforms and services. The ideal candidate will work closely with Digital SRE engineers, data scientists, DevOps, and operations teams to deliver robust, efficient, and automated systems that support business goals.

Job Description Summary: The IS Technical Specialist provides technical and consultative support on the most complex technical matters. This role typically reports to the Head of Digital SRE and may involve on-call responsibilities. The position provides opportunities to work on cutting-edge AI solutions, collaborate with cross segment teams, and drive reliability for mission-critical digital services Duties and Responsibilities: Design, develop, and implement AI-driven systems and automation tools to enhance the reliability and efficiency of digital platforms.

Monitor the health, availability, and performance of AI-enabled applications and infrastructure using SRE best practices. Collaborate with cross-functional teams to integrate machine learning models into production environments, ensuring seamless deployment and operation. Establish and enforce service-level objectives (SLOs), error budgets, and incident response procedures for AI-driven services.

Identify, troubleshoot, and resolve complex incidents related to AI systems, leveraging observability and monitoring tools. Drive continuous improvement by analyzing post-incident reviews, automating manual tasks, and optimizing system performance. Stay up to date with advancements in AI, SRE, and cloud technologies, recommending innovative solutions to enhance digital reliability.

Document processes and runbooks for operational transparency and knowledge sharing. AI Platform Integration: Develop abstraction layers across AI providers (Google, OpenAI, etc. ) to enable seamless integration and enablement.

Conduct design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders, fostering trust and adoption. Measure & Improve: Define and use key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness. Knowledge Sharing: Craft reusable patterns, documentation, and best practices to influence internal assets and client roadmaps.

Basic Qualifications: Bachelor's or master's degree in computer science, Engineering, Data Science, or a related field. Minimum 5 YOE Proven experience in AI/ML engineering, SRE, DevOps, or related roles. Strong programming skills in Python, Java, or similar languages, with experience in developing and deploying machine learning models.

Hands-on experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). Familiarity with observability tools (Prometheus, Grafana, ELK stack) and Service Now incident management platforms. Solid understanding of SRE principles: monitoring, alerting, SLOs, error budgets, and automation

Experience with infrastructure-as-code (Terraform, Ansible) and CI/CD pipelines. Excellent problem-solving skills, attention to detail, and ability to work in a fast-paced, collaborative environment. Preferred Qualifications: Experience operationalizing large language models (LLMs) or generative AI systems in production settings.

Background in MLOps, data engineering, and/or cloud-native AI deployment. Strong communication and documentation abilities Knowledge of security best practices for AI and cloud infrastructure. Contributions to open source AI/SRE projects or relevant technical communities Exempt Status: (Yes = not eligible for overtime pay) (No = eligible for overtime pay) Yes Workplace Type: Office Our Approach to Office Workplace Type Certain positions outside our branch network may be eligible for a flexible work arrangement.

We're combining the best of both worlds: in-office and work from home. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. Remote roles will also have the opportunity to come together in our offices for moments that matter.

Specific work arrangements will be provided by the hiring team. Huntington is an Equal Opportunity Employer. Tobacco-Free Hiring Practice: Visit Huntington's Career Web Site for more details.

Note to Agency Recruiters: Huntington Bank will not pay a fee for any placement resulting from the receipt of an unsolicited resume. All unsolicited resumes sent to any Huntington Bank colleagues, directly or indirectly, will be considered Huntington Bank property. Recruiting agencies must have a valid, written and fully executed Master Service Agreement and Statement of Work for consideration.

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