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Remote Machine Learning Engineer Biotech Jobs in Puerto Rico

Infotech Sourcing

Linguist III

PR · Remote

Infrastructure Engineering - Linguist III Location: US - NY - Remote Duration:8 months Job Title ... machine learning, NLP, or software engineers, or data scientists Experience contributing to ...

Cognizant Technology Solutions

$100K - $132K/yr

... Remote Serve as a Senior Engineer - Execution Systems in a hybrid work environment, designing ... learning and staying current with AVEVA PI and industrial data trends. Job Details Job Title:

Huntington

Lead ETL Developer

Florida, PR · On-site +1

If you consider data as a strategic asset and have a passion for learning and continuous ... Remote roles will also have the opportunity to come together in our offices for moments that matter.

ABB

Service Sales Specialist

Aguadilla, PR · Remote

... learning, and creative problem-solvingthat allows the right candidate to handle multiple work ... Remote {#LI-Remote}, this role provides support for the East Coast and will require East Coast ...

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Remote Machine Learning Engineer Biotech information

What are some common challenges faced by remote machine learning engineers in the biotech industry, and how can they be addressed?

Remote machine learning engineers in biotech often face challenges such as managing large datasets securely, collaborating effectively across multidisciplinary teams, and staying updated with the latest scientific and technical developments. Communication is key—regular video meetings and clear documentation help bridge gaps with colleagues in research, data science, and regulatory domains. Additionally, leveraging secure cloud platforms and adhering to data privacy regulations are essential for handling sensitive biological information. Staying proactive with self-learning and participating in online forums or company-sponsored training can also help address these challenges.

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

To thrive as a Remote Machine Learning Engineer in Biotech, you need a strong background in computer science, statistical modeling, and biology, typically supported by a relevant degree and experience in data-driven research. Proficiency with programming languages like Python or R, machine learning frameworks (such as TensorFlow or PyTorch), and bioinformatics tools is essential, and certifications in data science or machine learning are advantageous. Strong problem-solving, communication, and collaboration skills are crucial for working effectively in remote, interdisciplinary teams and explaining complex results to stakeholders. These skills ensure accurate model development, effective knowledge transfer, and impactful contributions to biotech innovations.

What does a Remote Machine Learning Engineer do in the biotech industry?

A Remote Machine Learning Engineer in the biotech industry develops and implements machine learning models to analyze biological data, such as genomics, proteomics, or medical imaging. They collaborate with scientists and researchers to interpret complex datasets, automate data-driven processes, and drive innovation in drug discovery, diagnostics, or personalized medicine. Working remotely, they use programming, data science, and domain knowledge to create solutions that improve research efficiency and outcomes in biotechnology.
What job categories do people searching Remote Machine Learning Engineer Biotech jobs in Puerto Rico look for? The top searched job categories for Remote Machine Learning Engineer Biotech jobs in Puerto Rico are:
What cities in Puerto Rico are hiring for Remote Machine Learning Engineer Biotech jobs? Cities in Puerto Rico with the most Remote Machine Learning Engineer Biotech job openings:
Remote Machine Learning Engineer Biotech jobs near you
Linguist III

Linguist III

Infotech Sourcing

PR • Remote

Other

Posted 25 days ago

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)

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