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Flexible Machine Learning Engineer Biotech Jobs (NOW HIRING)

TwinThread

Machine Learning Engineer

Charlottesville, VA · On-site

$90 - $120/hr

... data scientist, machine learning engineer, or similar role * Solid understanding of the ... Flexible schedule * Regular team lunches * Latest Macbook Pro, any other productivity-boosting ...

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying ...

Advatix Inc.

Machine Learning Engineer

San Mateo, CA · On-site

$110 - $165/hr

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift ... Unlimited/flexible paid time off * High‑impact role within a YC‑backed startup * Direct ...

Unissant

Machine Learning Engineer

Ashburn, VA · On-site

$110 - $170/hr

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA ... Flexible in working extended hours. The above statements are intended to describe the general ...

Speria

Machine Learning Engineer

Atlanta, GA · On-site

$120 - $160/hr

... flexible and rewarding atmosphere. We pride ourselves for having a working atmosphere that ... Job Summary We are seeking a highly skilled and motivated Machine Learning Engineer to join our ...

Interwell Health

Machine Learning Engineer

In this role, you will be flexible, eager to learn new skills, and willing to contribute wherever the team needs support. This Machine Learning Engineer is comfortable working with both traditional ...

UNISSANT

Machine Learning Engineer

Ashburn, VA · On-site

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA ... Flexible in working extended hours. The above statements are intended to describe the general ...

Cymertek Corporation

Machine Learning Engineer

Aurora, CO · On-site

$120 - $180/hr

S. Citizenship) KEY SUMMARY We are seeking a talented and innovative Machine Learning Engineer to ... Flexible Work Schedule * Cafeteria Style Benefits * 10% - 401k Matching (Vested Immediately)

CoVar

Machine Learning Engineer

Durham, NC

Machine Learning Engineer About CoVar CoVar is a small AI/ML R&D software company in Durham, NC ... Flexible work schedule * Tuition support * PTO and paid holidays Visit us: www.covar.com

innovitusa

Machine Learning Engineer

Atlanta, GA · On-site

W2 Candidates Only We are seeking a Machine Learning Engineer to develop, deploy, and optimize machine learning models and AI solutions. The ideal candidate will have strong experience with Python ...

1 point system

Machine Learning Engineer

Fort Mill, SC · Remote

$48/hr

Job Title Machine Learning Engineer Location Remote Rate $48/hr on W2 Must Haves: Neaural networks NLP Python AZURE Pytorch or tensorflow Machine Learning Engineer / AI Engineer Role Role Overview ...

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Flexible Machine Learning Engineer Biotech information

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$31.5K

$128.8K

$193.5K

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

As of Sep 4, 2026, the average yearly pay for flexible machine learning engineer biotech in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

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

AspectFlexible Machine Learning Engineer BiotechData Scientist Biotech
Required CredentialsDegree in Computer Science, Data Science, or related fields; experience with ML frameworksDegree in Statistics, Mathematics, or related fields; proficiency in data analysis
Work EnvironmentDevelops and deploys ML models in biotech R&D and production settingsAnalyzes biological data to extract insights, often in research labs or biotech companies
Employer & Industry UsageUsed by biotech firms focusing on AI-driven drug discovery and diagnosticsCommon in biotech research, clinical data analysis, and bioinformatics

The main difference is that a Flexible Machine Learning Engineer Biotech primarily develops and implements machine learning models tailored for biotech applications, while a Data Scientist Biotech focuses on analyzing biological data to generate insights. Both roles require strong technical skills, but the engineer emphasizes model deployment and integration, whereas the scientist emphasizes data interpretation and statistical analysis.

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Flexible Machine Learning Engineer Biotech jobs near you
Infographic showing various Flexible Machine Learning Engineer Biotech job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 67% Full Time, 29% Part Time, and 3% Contract. Highlights an 88% Physical, 1% Hybrid, and 11% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer

AI Squared

Washington, DC

Full-time

Re-posted 15 days ago

Job description

Machine Learning Engineer
Washington, DC (Hybrid)

About the Role:

We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying, maintaining, and monitoring the AI/ML systems that power our platform. You will work closely with data scientists, data engineers, and product teams to ensure scalable, reliable, and production-grade AI solutions. You'll play a critical role in operationalizing large language models (LLMs) and other ML systems, ensuring they run efficiently, securely, and with robust monitoring in place.

Key Responsibilities:
  • Design, implement, and maintain ML deployment pipelines for scalable production systems.
  • Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
  • Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
  • Partner with data scientists to transition models from research/prototype into production-ready deployments.
  • Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
  • Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
  • Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
  • Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.
Qualifications:
  • 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
  • Proven experience deploying and maintaining machine learning models in production at scale.
  • Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
  • Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
  • Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
  • Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
  • Strong understanding of MLOps best practices, monitoring, and automation.
  • Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
  • Strong communication and collaboration skills across technical and non-technical teams.