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Evening Machine Learning Engineer Biotech Jobs (NOW HIRING)

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 ...

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift: N/A Location: San Mateo, CA (Peninsula) - Onsite Preferred Schedule: Full time, Permanent Role Visa ...

Machine Learning Engineer

Manhattan, NY · On-site

$170.17 - $255.26/hr

Job Overview Machine Learning Engineer w/ Spotify USA Inc. in NY, NY. Bld productn systms that enrich & improve Spotify listeners' exp on Spotify usg machine learng techniques. Bach deg (U.S. or for ...

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 ...

Machine Learning Engineer We're looking for a talented and motivated Machine Learning Engineer to join our team and help develop cutting-edge AI solutions. In this role, you'll have the opportunity ...

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

The Role We are looking for a Machine Learning Engineer to bridge the gap between AI research and production-grade flight systems. You will optimize, deploy, and scale machine learning models that ...

Machine Learning Engineer

Seattle, WA · On-site

$95 - $135/hr

The Role We are looking for a Machine Learning Engineer to bridge the gap between AI research and production-grade flight systems. You will optimize, deploy, and scale machine learning models that ...

Machine Learning Engineer

Atlanta, GA · On-site

$130 - $185/hr

Openings › Software › Machine Learning Engineer Software Machine Learning Engineer Atlanta, US Remote Full-time $130,000 - $185,000 About Winixx Winixx Inc. is a New York-based technology holding ...

Showing results 21-40

Evening Machine Learning Engineer Biotech information

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$31.5K

$128.8K

$193.5K

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

As of Aug 15, 2026, the average yearly pay for evening 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 Evening Machine Learning Engineer Biotech vs Evening Data Scientist Biotech?

AspectEvening Machine Learning Engineer BiotechEvening Data Scientist Biotech
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related fields; experience with ML frameworksBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in data analysis tools
Work EnvironmentDeveloping ML models, coding, deploying algorithms in biotech settingsAnalyzing datasets, creating reports, interpreting data in biotech companies
Employer & Industry UsageBiotech firms, research labs, pharmaceutical companiesBiotech firms, research institutions, healthcare organizations

While both roles involve working with data in biotech, the Evening Machine Learning Engineer Biotech focuses on developing and deploying machine learning models, whereas the Evening Data Scientist Biotech emphasizes data analysis and interpretation. Both roles require strong technical skills and are integral to biotech innovation, but they differ in daily tasks and technical focus.

What cities are hiring for Evening Machine Learning Engineer Biotech jobs?

Cities with the most Evening Machine Learning Engineer Biotech job openings:

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

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

What states have the most Evening Machine Learning Engineer Biotech jobs?

States with the most job openings for Evening Machine Learning Engineer Biotech jobs include:

Machine Learning Engineer

AI Squared

Washington, DC • On-site

Full-time

Re-posted 25 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.