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Startup Machine Learning Remote Jobs in Virginia

Halvik

Machine Learning Engineer - Remote

Vienna, VA · On-site +1

$140K - $150K/yr

Halvik is a highly successful WOB business with more than 50 prime contracts and 500+ professionals delivering Digital Services, Advanced Analytics, Artificial Intelligence/Machine Learning ...

Niyam IT, Inc.

AI Machine Learning Engineer

Ashburn, VA · On-site +1

Remote Work: Niyam understands the value of flexibility. We offer remote work. * Career Growth ... The ideal candidate brings a strong foundation in machine learning, data engineering, and MLOps ...

Elder Research

Data Scientist (Remote)

Arlington, VA · On-site +1

Maintain current in emerging tools and techniques in machine learning, statistical modeling, and ... Washington DC Metro Area - Remote (candidates MUST BE located in the National Capital Region - DMV ...

Elder Research

Data Scientist (Remote)

Arlington, VA · On-site +1

Maintain current in emerging tools and techniques in machine learning, statistical modeling, and ... Washington DC Metro Area - Remote (candidates MUST BE located in the National Capital Region - DMV ...

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Startup Machine Learning Remote information

What are the key skills and qualifications needed to thrive as a Startup Machine Learning Engineer in a remote setting, and why are they important?

To thrive as a Startup Machine Learning Engineer remotely, you need a solid background in computer science, statistics, and machine learning algorithms, typically supported by a relevant degree or equivalent experience. Familiarity with tools such as Python, TensorFlow or PyTorch, cloud platforms (AWS, GCP, or Azure), and version control systems like Git is essential. Strong self-motivation, communication skills, and the ability to collaborate effectively across time zones help set outstanding candidates apart. These skills and qualities are crucial for delivering impactful ML solutions independently while contributing to fast-paced, distributed startup teams.

What are remote startup machine learning jobs?

Remote startup machine learning jobs involve working for early-stage companies or startups to design, develop, and implement machine learning models and solutions, all while working from a remote location. These roles typically require strong programming skills, experience with data analysis, and familiarity with machine learning frameworks. Startups often offer dynamic environments where employees can work on diverse projects and contribute directly to the product's growth. Remote positions provide flexibility in work location and hours, but also require self-motivation and excellent communication skills to collaborate with distributed teams.

What are some unique challenges faced by machine learning professionals working remotely at a startup?

Machine learning professionals at startups often encounter fast-paced environments where priorities can shift quickly, and working remotely adds another layer of complexity. Collaboration with cross-functional teams, such as engineers and product managers, may require proactive communication to ensure alignment and clarity on project goals. Additionally, limited resources and data infrastructure at startups may mean you'll need to wear multiple hats and help shape processes from the ground up. However, this environment offers high autonomy, opportunities to have a direct impact, and rapid career growth potential as the startup scales.
What are the most commonly searched types of Startup Machine Learning jobs in Virginia? The most popular types of Startup Machine Learning jobs in Virginia are:
What are popular job titles related to Startup Machine Learning Remote jobs in Virginia? For Startup Machine Learning Remote jobs in Virginia, the most frequently searched job titles are:
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Startup Machine Learning Remote jobs near you
Machine Learning Engineer - Remote

Machine Learning Engineer - Remote

Halvik

Vienna, VA • On-site, Remote

$140K - $150K/yr

Full-time

Re-posted 19 days ago

Job description

Halvik Corp delivers a wide range of services to 13 executive agencies and 15 independent agencies. Halvik is a highly successful WOB business with more than 50 prime contracts and 500+ professionals delivering Digital Services, Advanced Analytics, Artificial Intelligence/Machine Learning, Cyber Security and Cutting Edge Technology across the US Government. Be a part of something special!
Role and Responsibilities
Model Development
  • Collaborate with data scientists and SMEs to develop ML models using curated datasets.
  • Conduct experiments, prototypes, and proof-of-concepts to validate model performance.
  • Create scalable and reusable training pipelines using Databricks notebooks and MLflow.

Implementation and Optimisation
  • LLMs (Large Language Models), RAGs, and AI agent systems for various business applications. Deployment & MLOps
  • Operationalize models with robust CI/CD workflows.
  • Deploy models usingMLflow, SageMaker, or custom APIs.
  • Monitor production models for accuracy, drift, and latency; manage retraining schedules.

Data Integration & Architecture Alignment
  • Work closely with Data Engineering to align ML pipelines with the Bronze, Silver, Gold layers of a Medallion Architecture.
  • Engineer high-quality features and maintain training/inference pipelines.

Cloud and Platform Engineering
  • Leverage AWS services including S3, EC2, Lambda, SageMaker, and Step Functions.

Collaboration & Documentation
  • Document ML artifacts, processes, and performance outcomes.
  • Contribute to agile project ceremonies and maintain a feedback loop with stakeholders.
  • Share knowledge and mentor junior team members.

Required Skills:
  • 5+ years of experience in ML Engineering or Applied Machine Learning.
  • Strong Python skills and hands-on experience with ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow).
  • Proficient with Databricks, MLflow, and PySpark.
  • Solid understanding of model lifecycle and MLOps practices.
  • Experience with AWS-based data infrastructure and related DevOps practices.
  • Demonstrated ability to productionize models and integrate with business system
  • Strong understanding of mathematics and statistics relevant to machine learning and AI.
  • Proven experience with machine learning models and algorithms (supervised, unsupervised, deep learning, etc.).
  • Solid background in software engineering principles and best practices.
  • Hands-on experience with model training frameworks (e.g., TensorFlow, PyTorch, Hugging Face).
  • Experience with MLOps tools and workflows, particularly on AWS (SageMaker, Lambda, S3, etc.).
  • Practical experience with LLMs, RAGs, and AI agent architectures.
  • Proficiency with the Databricks platform for data engineering and ML pipelines.
  • Advanced programming skills in Python.
  • Excellent communication and teamwork abilities.

Preferred Skills:
  • Experience building and deploying interactive UIs for AI models using Streamlit, Gradio, or similar frameworks for rapid prototyping and real-time model interactions
  • Business acumen and ability to align AI solutions with organizational goals.
  • Optimize compute and storage resources for performance and cost-efficiency.

Halvik's pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
Halvik Corp is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status.
Halvik's pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.

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