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Machine Learning Engineer Biotech Jobs in Atlanta, GA

Iterable

Senior Machine Learning Engineer (Nova)

Atlanta, GA

$100K - $138K/yr

We are looking for a Senior Machine Learning Engineer to build the core Machine Learning foundations that power Nova's agentic experiences. This role focuses on applied Machine Learning in production ...

Iterable

Senior Machine Learning Engineer (Nova)

Atlanta, GA · On-site

$100K - $138K/yr

We are looking for a Senior Machine Learning Engineer to build the core Machine Learning foundations that power Nova's agentic experiences. This role focuses on applied Machine Learning in production ...

Cox Automotive

Machine Learning Lead Engineer

Redan, GA

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Morrow, GA

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

Cox Automotive

Machine Learning Lead Engineer

Smyrna, GA

$134K - $224K/yr

We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting ...

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Showing results 1-20

All JobsMachine Learning Engineer Biotech JobsMachine Learning Engineer Biotech Jobs in Atlanta, GA

Machine Learning Engineer Biotech information

See Atlanta, GA salary details

$30.3K

$123.8K

$186.1K

How much do machine learning engineer biotech jobs pay per year?

As of Jun 14, 2026, the average yearly pay for machine learning engineer biotech in Atlanta, GA is $123,832.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,600.00 and $149,100.00 per year, depending on experience, location, and employer.

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

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, and why are they important?

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 Atlanta, GA? The most popular types of Machine Learning Engineer Biotech jobs in Atlanta, GA are:
What are popular job titles related to Machine Learning Engineer Biotech jobs in Atlanta, GA? For Machine Learning Engineer Biotech jobs in Atlanta, GA, the most frequently searched job titles are:
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What cities near Atlanta, GA are hiring for Machine Learning Engineer Biotech jobs? Cities near Atlanta, GA with the most Machine Learning Engineer Biotech job openings:
Machine Learning Engineer Biotech jobs near you

Machine Learning Engineer III / AI-ML Engineer

4pconsultinginc

Atlanta, GA • On-site

Contractor

Posted 9 days ago

Job description

Position:           Machine Learning Engineer III – AI/ML Engineer

Location:          Atlanta, GA

Duration:          6 Months

Client:             Southern Company services

Job Summary

We are seeking an experienced Machine Learning Engineer III / AI-ML Engineer to support the development of reusable, scalable AI products that can be deployed across multiple operating companies and business units.

This role will focus on building production-grade AI solutions, including Retrieval-Augmented Generation (RAG), multi-agent orchestration, NLP pipelines, transcription solutions, model deployment, and reusable AI components for internal operational workflows.

The ideal candidate will have strong hands-on experience with GCP or Azure AI services, modern ML frameworks, strong software engineering skills, and a product-focused mindset.

Key Responsibilities

  • Design and build modular, reusable AI components that can scale across business units.
  • Lead development of scalable RAG-based solutions for document comparison and analysis.
  • Work with structured and unstructured data to support AI-driven business solutions.
  • Engineer multi-agent systems for intelligent task coordination and decision support.
  • Develop transcription and NLP pipelines for customer interaction analysis.
  • Build, fine-tune, and deploy models using tools and frameworks such as PyTorch, Transformers, and LangChain.
  • Package models for deployment in GCP, Azure ML, and/or Databricks.
  • Integrate with Databricks for data ingestion, feature engineering, experimentation, and model development.
  • Work closely with MLOps, DevOps, and Data Engineering teams to align infrastructure and deployment patterns.
  • Contribute to shared libraries, APIs, templates, and reusable frameworks that accelerate AI product delivery.
  • Provide technical guidance to teams adopting reusable AI components.
  • Ensure AI products meet enterprise-grade security, compliance, scalability, and maintainability standards.
  • Implement monitoring for model performance, data drift, usage metrics, and production reliability.

Required Qualifications

  • Experience as a Machine Learning Engineer, AI Engineer, Data Scientist, or similar technical role.
  • Strong experience building production-grade AI/ML solutions.
  • Hands-on experience with cloud-based AI services, preferably GCP or Azure.
  • Experience developing RAG-based applications using structured and unstructured data.
  • Strong knowledge of machine learning, NLP, LLMs, and modern AI application patterns.
  • Experience with frameworks and tools such as:
    • PyTorch
    • Transformers
    • LangChain
  • Experience deploying models in cloud or enterprise environments.
  • Strong programming and software engineering skills.
  • Ability to work with APIs, reusable components, and scalable architectures.
  • Experience collaborating with MLOps, DevOps, and data engineering teams.
  • Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

  • Experience with Azure ML, GCP Vertex AI, Databricks, or similar platforms.
  • Experience designing multi-agent systems or AI orchestration workflows.
  • Experience developing transcription, NLP, or customer interaction analytics pipelines.
  • Experience with model monitoring, data drift detection, observability, and usage metrics.
  • Experience building shared AI libraries, reusable templates, or internal AI platforms.
  • Understanding of enterprise security, compliance, and governance requirements for AI products.
  • Product mindset with the ability to design AI solutions that are reusable, scalable, and business-focused.