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Temporary Meta Machine Learning Jobs in Washington

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Temporary Meta Machine Learning information

What is a temporary Meta machine learning job?

Temporary Meta Machine Learning jobs are short-term positions at Meta (formerly Facebook) that focus on developing, deploying, or researching machine learning models and technologies. These roles may support ongoing projects, fill gaps during employee leave, or address spikes in workload. Responsibilities can include data preprocessing, model training, evaluation, and collaborating with cross-functional teams. Temporary roles often give candidates exposure to Meta's cutting-edge AI tools and processes, and may sometimes lead to permanent opportunities.

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

To thrive as a Temporary Meta Machine Learning Engineer, you need a strong background in computer science, statistics, and machine learning, typically with experience in Python and relevant ML frameworks. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms, and version control systems is often required, along with a proven ability to rapidly learn new technologies. Strong problem-solving skills, adaptability, and effective communication are essential for collaborating within dynamic teams and meeting project goals on tight timelines. These skills ensure that you can quickly contribute to impactful ML projects, deliver results efficiently, and integrate well into fast-paced, innovative environments.

What are some common challenges faced by professionals in temporary machine learning roles at Meta, and how can they be addressed?

Professionals in temporary machine learning roles at Meta often encounter challenges such as quickly acclimating to complex codebases, integrating with established teams, and delivering impactful results within a limited timeframe. Success in these roles typically requires strong technical skills, adaptability, and effective communication. Proactively seeking guidance, leveraging available documentation, and collaborating closely with permanent team members can help overcome these hurdles and maximize contributions during the temporary assignment.

What is the difference between Temporary Meta Machine Learning vs Data Scientist?

AspectTemporary Meta Machine LearningData Scientist
CredentialsTypically requires a background in computer science, statistics, or related fields; certifications in machine learning or data analysis are commonRequires a degree in computer science, statistics, or related fields; certifications like Certified Data Scientist are advantageous
Work EnvironmentProject-based, often contract roles within tech companies, startups, or consulting firmsFull-time or contract roles in various industries including finance, healthcare, and tech
Industry UsagePrimarily in tech, AI, and machine learning-focused companiesWidely used across multiple industries including finance, healthcare, marketing, and tech

Temporary Meta Machine Learning roles focus on short-term projects involving machine learning model development and deployment, often requiring specialized technical skills. Data Scientist roles are broader, encompassing data analysis, statistical modeling, and insights generation across diverse industries. While both roles require strong analytical skills and technical knowledge, Temporary Meta Machine Learning positions are more specialized in AI and machine learning applications.

What are the most commonly searched types of Meta Machine Learning jobs in Washington?

The most popular types of Meta Machine Learning jobs in Washington are:

What are popular job titles related to Temporary Meta Machine Learning jobs in Washington?

For Temporary Meta Machine Learning jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Temporary Meta Machine Learning jobs in Washington look for?

The top searched job categories for Temporary Meta Machine Learning jobs in Washington are:

What cities in Washington are hiring for Temporary Meta Machine Learning jobs?

Cities in Washington with the most Temporary Meta Machine Learning job openings:

Temporary Meta Machine Learning jobs near you

AI/ML engineering

Meta Force Technology Staffing LLC

Reston, VA • On-site

$119K - $143K/yr

Contractor

Re-posted 18 days ago

Job description

Developer IV :::::::: Position will be located  Reston, V

Onsite Interview required

Halting and SL'ing will take place Tuesday the 22nd at 10am EST

Description: 

Experience:
• 8+ years overall in Software Engineering disciplines, preferably in the financial services industry
• 2-3 years of experience in AI/ML engineering roles
• Strong programming skills in Python, SQL and experience with AWS.
Key Responsibilities:
• Design, test, and refine prompts for large language models (LLMs) to support financial reporting, summarization, and client communication tools.
• Analyze structured and unstructured financial data using Python and SQL, delivering insights through dashboards and reports.
• Develop and maintain data pipelines and ETL workflows to support GenAI model training and evaluation.
• Use AWS SageMaker to build, train, and deploy machine learning and GenAI models.
• Collaborate with data scientists, analysts, and business stakeholders to align AI solutions with financial objectives.
• Monitor model performance and iterate on prompt and model design to improve accuracy and relevance.
• Document workflows, models, and prompt strategies for internal knowledge sharing and compliance.
Required Qualifications:
• 2–3 years of experience in data analysis or machine learning roles.
• Proficiency in Python and SQL for data manipulation and analysis.
• Hands-on experience with major AWS services, particularly SageMaker, S3, Redshift, and Lambda.
• Experience working with LLMs (Anthropic Claude, Sonnet) and prompt engineering techniques.
• Strong understanding of financial data, KPIs, and reporting standards.
• Excellent communication and collaboration skills.
Preferred Qualifications:
• Experience in the finance or fintech industry.
• Familiarity with vector databases (e.g., FAISS, Pinecone) and retrieval-augmented generation (RAG).
• Exposure to data visualization tools (e.g., Power BI, Tableau).
• Understanding of MLOps practices and model lifecycle management.
Education
• B Bachelor’s degree in Computer Science, Data Science, Finance, or a related field.

Enable Skills-Based Hiring

No

Do the resources need to be convertible talent?

 

(No Value)

Recruiting Strategy Type

 

Competitive Source

Is this position open to subcontractors?

 

Yes