Machine Learning Engineer Location: Fort Meade, MD Required Clearance : TS/SCI w/ Full-Scope Poly Salary: Competitive We are seeking a highly skilled and motivated Machine Learning Engineer to join ...
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Quick apply
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CA · On-site
$75 - $89/hr
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They are seeking a Machine Learning Engineer to build systems that analyze the performance of music promotions, providing actionable insights for creators and partners. Responsibilities : • ...
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Contract Machine Learning Engineer Biotech information
See salary details
$31.5K - $46.2K
1% of jobs
$46.2K - $61K
1% of jobs
$61K - $75.7K
5% of jobs
$75.7K - $90.4K
6% of jobs
$102.6K is the 25th percentile. Wages below this are outliers.
$90.4K - $105.1K
14% of jobs
$105.1K - $119.9K
14% of jobs
The median wage is $127.2K / yr.
$119.9K - $134.6K
18% of jobs
$134.6K - $149.3K
14% of jobs
$152.3K is the 75th percentile. Wages above this are outliers.
$149.3K - $164K
12% of jobs
$164K - $178.8K
11% of jobs
$178.8K - $193.5K
5% of jobs
$31.5K
$128.8K
$193.5K
How much do contract machine learning engineer biotech jobs pay per year?
What is the difference between Contract Machine Learning Engineer Biotech vs Contract Data Scientist Biotech?
| Aspect | Contract Machine Learning Engineer Biotech | Contract Data Scientist Biotech |
|---|---|---|
| Credentials | Degree in Computer Science, Data Science, or related field; experience with ML frameworks | Degree in Statistics, Data Science, or related; strong analytical skills |
| Work Environment | Develops ML models, algorithms, and pipelines for biotech applications | Analyzes data, builds statistical models, and interprets biological data |
| Industry Usage | Used in biotech R&D, drug discovery, and personalized medicine | Applied in clinical data analysis, biomarker discovery, and research |
Contract Machine Learning Engineers focus on developing and deploying ML models specific to biotech challenges, while Contract Data Scientists analyze biological data to extract insights. Both roles require strong technical skills but differ in their primary focus—model development versus data analysis.
Other
Posted 28 days ago
Job description
Location:Fort Meade, MD
Required Clearance: TS/SCI w/ Full-Scope Poly
Salary:Competitive
We are seeking a highly skilled and motivated Machine Learning Engineer to join our dynamic team. The ideal candidate will have a strong background in machine learning, data science, and software engineering. You will work closely with data scientists, engineers, and product managers to design, develop, and deploy machine learning models and solutions that drive business value.
Key Responsibilities:
- Design, develop, and implement machine learning models and algorithms to solve real-world problems.
- Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
- Conduct data analysis and preprocessing to ensure high-quality data for model training.
- Optimize and fine-tune models for performance, accuracy, and scalability.
- Deploy machine learning models into production and monitor their performance.
- Develop and maintain machine learning pipelines and infrastructure.
- Stay current with the latest research and advancements in machine learning and AI.
- Participate in code reviews, team meetings, and contribute to a collaborative development environment.
- Document processes, models, and findings comprehensively.
- Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field. Ph.D. is a plus.
- Proven experience as a Machine Learning Engineer or in a similar role.
- Strong proficiency in programming languages such as Python, R, or Java.
- Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
- Solid understanding of machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning.
- Experience with data processing tools like Pandas, NumPy, and data visualization tools such as Matplotlib or Seaborn.
- Familiarity with cloud platforms like AWS, Google Cloud, or Azure for model deployment and scaling.
- Strong problem-solving skills and the ability to think critically and analytically.
- Excellent communication and teamwork skills.
- Experience with natural language processing (NLP) and computer vision.
- Familiarity with big data technologies such as Hadoop, Spark, or Kafka.
- Knowledge of software development best practices and version control systems like Git.
- Experience with containerization tools like Docker and orchestration tools like Kubernetes.
- Previous experience in a fast-paced, startup environment.