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Machine Learning Engineer Biotech Jobs in South Carolina

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Showing results 41-60

Machine Learning Engineer Biotech information

What does a machine learning engineer do in biotech?

A Machine Learning Engineer in biotech applies advanced algorithms and data analysis techniques to solve biological and medical problems. They work with large datasets such as genomic sequences, medical images, or clinical records to develop predictive models, automate data analysis, and uncover insights that can accelerate drug discovery, diagnostics, and personalized medicine. Their work often involves close collaboration with biologists, data scientists, and software engineers to create tools and solutions that improve healthcare outcomes. Machine Learning Engineers in this field need a strong background in both computational methods and biological sciences.

How do machine learning engineers in biotech typically collaborate with research scientists and domain experts?

Machine Learning Engineers in biotech often work closely with research scientists and domain experts to translate complex biological problems into data-driven solutions. This collaboration involves regular meetings to understand experimental data, refine project goals, and iterate on model development based on domain feedback. Engineers are expected to communicate technical concepts clearly, adapt models to fit scientific needs, and help validate results alongside laboratory teams. This interdisciplinary environment fosters innovation but also requires flexibility and strong communication skills.

What are the key skills and qualifications needed to thrive as a machine learning engineer in biotech?

To thrive as a Machine Learning Engineer in Biotech, you need a solid background in computer science, statistics, and biology, often with an advanced degree in a related field. Experience with programming languages such as Python or R, machine learning frameworks like TensorFlow or PyTorch, and familiarity with bioinformatics tools are typically required. Strong problem-solving, communication, and interdisciplinary collaboration skills set standout candidates apart. These capabilities are crucial for developing effective models that drive scientific innovation and advance biotechnological research.

What is the difference between Machine Learning Engineer Biotech vs Data Scientist Biotech?

AspectMachine Learning Engineer BiotechData Scientist Biotech
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related; knowledge of ML frameworksBachelor's or Master's in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentDevelops ML models, coding, deploying algorithms in biotech R&DAnalyzes biological data, interprets results, creates reports
Employer & Industry UsageBiotech firms, pharma companies, research labsBiotech companies, healthcare, research institutions

While both roles work with biological data, Machine Learning Engineers focus on developing and deploying ML algorithms, whereas Data Scientists analyze and interpret biological datasets to inform research and decision-making in biotech settings.

What are the most commonly searched types of Machine Learning Engineer Biotech jobs in South Carolina?

The most popular types of Machine Learning Engineer Biotech jobs in South Carolina are:

What are popular job titles related to Machine Learning Engineer Biotech jobs in South Carolina?

For Machine Learning Engineer Biotech jobs in South Carolina, the most frequently searched job titles are:

What cities in South Carolina are hiring for Machine Learning Engineer Biotech jobs?

Cities in South Carolina with the most Machine Learning Engineer Biotech job openings:

Machine Learning Engineer - Special Projects

Apple

Fort Mill, SC • On-site

Full-time

Re-posted 28 days ago


Apple rating

8.1

Company rating: 8.1 out of 10

Based on 681 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers


Job description

We're seeking research engineers to build infrastructure for breakthrough innovations in AI agents, reinforcement learning, and simulation environments. You will design and implement high-quality data pipelines, simulation systems, and tooling that enable cutting-edge agent research. You will work in an organization of world-class machine learning researchers and engineers. Our work powers technologies across the Apple ecosystem and is published in the most selective scientific journals and conferences.
Description
We are a team of best-in-the-world research scientists and engineers building the foundations for autonomous AI systems. We work on exciting new technologies that bring joy to millions of people. In our daily work, the team stays innovative, productive, and fun by sharing some key values:
Minimum Qualifications
5+ years of ML engineering experience building and maintaining data-intensive systems - including feature pipelines, training infrastructure, model serving, or evaluation frameworks.
Solid software engineering skills in complex systems - Fluency in Python. You deliver clean, well-tested code.
Hands-on experience with distributed ML systems - CI/CD at scale, distributed testing, or ML evaluation pipelines.
Strong quantitative and data skills - comfortable with SQL, statistical reasoning, and translating ambiguous signals into clear, actionable findings.
Proven track record shipping ML systems end-to-end - from problem framing and data curation through training, evaluation, deployment, and monitoring in production.
Preferred Qualifications
Bachelors or Masters Degree in Computer Science, Engineering, Math, or Physics from a strong program.
2+ years at a company building AI products or agent systems.
Experience with job orchestration frameworks (Airflow, Prefect, Ray, etc.).
Familiarity with macOS/iOS development ecosystems.
Active personal interest in AI agents-you're already experimenting on your own time.

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Apple logo

About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976