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Remote Python Ai Jobs in Puerto Rico (NOW HIRING)

Linguist III

PR · Remote

US - NY - Remote Duration:8 months Job Title: Linguist lII (FAIR) Main duties: Perform linguistic ... Perform linguistic error analysis of AI model outputs, determining what the most frequent and ...

Remote Python Ai information

What are the key skills and qualifications needed to thrive as a Remote Python AI Developer, and why are they important?

To thrive as a Remote Python AI Developer, you need strong programming skills in Python, a solid understanding of machine learning concepts, and typically a degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with cloud platforms (e.g., AWS, Azure), and relevant certifications such as TensorFlow Developer are highly valued. Exceptional problem-solving abilities, self-motivation, and effective remote communication skills help set professionals apart in this distributed role. These capabilities are critical for developing robust AI solutions, collaborating across virtual teams, and delivering impactful results in a rapidly evolving field.

How is collaboration typically structured in a remote Python AI role, and what tools are commonly used to facilitate teamwork?

In a remote Python AI role, collaboration is often structured through regular virtual meetings, code reviews, and the use of collaborative platforms. Teams typically use version control systems like GitHub or GitLab for code sharing, and platforms such as Slack or Microsoft Teams for daily communication. Project management tools like Jira or Trello help organize tasks and track progress, while video calls via Zoom or Google Meet are used for team discussions and brainstorming sessions. This structure ensures that even in a distributed setting, team members can efficiently work together, share insights, and resolve challenges.

What are Remote Python AI jobs?

Remote Python AI jobs are positions that involve developing, implementing, or maintaining artificial intelligence solutions using the Python programming language, all while working from a remote location. These roles can include tasks such as building machine learning models, automating data analysis, and deploying AI-powered applications. Professionals in these jobs collaborate with teams online, use cloud-based tools, and contribute to a variety of industries such as tech, finance, healthcare, and more. Python is a popular choice for AI due to its simplicity and the availability of powerful libraries like TensorFlow, PyTorch, and scikit-learn.

What is the difference between Remote Python Ai vs Data Scientist?

AspectRemote Python AiData Scientist
Required CredentialsPython programming, AI/ML knowledge, possibly certifications in AI or data analysisStatistics, programming, data analysis, often a master's degree or higher
Work EnvironmentRemote, tech companies, AI-focused teamsRemote or on-site, diverse industries including tech, finance, healthcare
Employer & Industry UsageTech startups, AI firms, software companiesVarious sectors like finance, healthcare, marketing, tech
Search & Comparison IntentFocus on AI development using PythonData analysis, insights, statistical modeling

Remote Python Ai roles primarily focus on developing AI models and applications using Python, often within tech or AI companies. Data Scientists analyze data to extract insights, requiring broader statistical skills. While both roles may involve Python, Remote Python Ai emphasizes AI/ML development, whereas Data Scientists focus on data analysis and interpretation.

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Linguist III

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Posted yesterday


Job description

Job Title: Infrastructure Engineering - Linguist III
Location: US - NY - Remote
Duration:8 months
Job Title: Linguist lII (FAIR)
Main duties:
Perform linguistic analyses on large datasets.
Perform linguistic error analysis of AI model outputs, determining what the most frequent and severe error categories are.
Write and revise guidelines for human annotation and other AI projects, including but not limited to translation tasks.
Conduct typological and sociolinguistic research on a large number of languages, highlighting their similarities and differences.
Perform linguistic analyses for Responsible AI (toxic language, hate speech, gender bias and other cultural biases) in massively multilingual settings.
Conduct linguistic literature reviews on various NLP-adjacent topics, and summarize findings.
Compare the quality of deliveries between vendors, identify error patterns, and provide actionable feedback.
Provide information or guidance relative to any aspect of linguistic knowledge (typology, morpho-syntax, sociolinguistics, classification, phonetics/phonology, pragmatics, etc.).
Reach out to and collaborate with native speakers in various languages.
Communicate results of linguistic analyses to engineers and research scientists.
Skills:
Must have strong written and spoken communication skills, especially business and research communication.
Must be a native speaker of a non-English language (preferably Hindi) with a high level of proficiency in another Indo-Aryan or South Dravidian language, plus broad knowledge of other languages in either of those two groups.
Working knowledge in other languages is a plus. Proficiency in a low-resource language is valued.
Must be able to code in Python (must) and query databases using SQL, other coding languages used for data analysis are a plus.
Must be able to independently work through complex requests and perform under pressure.
Strong ability to work independently, prioritize, plan, and track work, as well as report progress
education or training in the basics of project management is a plus
self-motivation is a must
Working knowledge of international language-classification standards is valued.
Education:
Graduate degree in Linguistics or related field is a must; PhD is a plus
a background or specialization in corpus linguistics is a plus
experience with field work is a plus
a graduate degree in Literature or English is not an appropriate substitution
degree in Computer Science with a specialization in NLP is not an appropriate substitution
Must have a very firm grasp of the following linguistic fields: language typology, syntax, morphology, sociolinguistics (especially dialectology and discourse analysis), corpus linguistics, writing systems, pragmatics, phonology.
Must have some experience with applying basic Natural Language Processing techniques.
Experience
Years of experience: 0-3
Experience working cross-functionally
Experience collaborating with machine learning, NLP, or software engineers, or data scientists
Experience contributing to research papers
Important: Preferably no known conflicts of interest in the fields of machine translation, ASR, TTS, or LLM research (as FAIR Linguists need to be contributing to research papers)