ZipRecruiter

Log In

2

Remote Director Machine Learning Jobs in Washington

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ... Fully remote, U.S.-based * Health Benefits : Comprehensive health, dental, and vision coverage

Spear AI

Machine Learning Engineer

Washington, DC · On-site +1

We're seeking a skilled Machine Learning Engineer to build and deploy production ML systems for the ... Onsite / Remote / Flexible work arrangements or hybrid options (position dependent) * Relocation ...

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 ...

next page

Showing results 1-20

All JobsMachine Learning JobsRemote Director Machine Learning JobsRemote Director Machine Learning Jobs in Washington

Remote Director Machine Learning information

What is the difference between Remote Director Machine Learning vs Remote Data Science Manager?

AspectRemote Director Machine LearningRemote Data Science Manager
Required CredentialsMaster's or PhD in Computer Science, Data Science, or related field; experience in ML algorithmsMaster's in Data Science, Statistics, or related; strong analytical background
Work EnvironmentLeads ML teams, develops models, and oversees deployment in tech-focused companiesManages data science teams, focuses on insights and analytics for business decisions
Employer & Industry UsageTech firms, AI startups, large enterprises with AI initiativesFinancial, healthcare, retail, and other industries leveraging data insights

While both roles require advanced education and involve data-driven work, the Remote Director Machine Learning primarily focuses on leading ML model development and deployment, whereas the Remote Data Science Manager emphasizes managing data analysis teams and deriving business insights.

What does a Remote Director of Machine Learning do?

A Remote Director of Machine Learning leads teams of data scientists and engineers to develop, implement, and oversee machine learning solutions for an organization, all while working remotely. They are responsible for setting the strategic direction for ML projects, collaborating with stakeholders, and ensuring that models align with business objectives. This role typically involves both technical leadership—such as reviewing algorithms and architectures—and managerial duties, such as mentoring staff and managing budgets. Working remotely, they use digital collaboration tools to communicate, monitor progress, and deliver results effectively.

What are the key skills and qualifications needed to thrive as a Remote Director of Machine Learning, and why are they important?

To thrive as a Remote Director of Machine Learning, you need advanced expertise in machine learning algorithms, data science, and leadership, typically supported by a graduate degree in a related field and extensive experience in deploying ML solutions. Familiarity with tools like Python, TensorFlow, PyTorch, cloud platforms, and experience with project management systems is essential, and certifications such as AWS Certified Machine Learning can be advantageous. Outstanding communication, strategic thinking, and the ability to mentor and manage distributed teams are crucial soft skills in this role. These skills and qualities are vital to successfully lead innovative ML projects, align technical teams with business goals, and drive impactful outcomes in a remote environment.

How does a Remote Director of Machine Learning typically coordinate and lead distributed teams across different time zones?

As a Remote Director of Machine Learning, effective coordination of distributed teams requires strong communication strategies, including regular video meetings, clear documentation, and use of collaborative project management tools. Leaders in this role often establish overlapping core hours and leverage asynchronous communication to accommodate various time zones. They focus on aligning goals, fostering a culture of transparency, and ensuring continuous progress through well-defined milestones. Building trust and maintaining team engagement remotely are common challenges, but successful directors prioritize mentorship, feedback, and virtual team-building activities to create a cohesive work environment.
What are popular job titles related to Remote Director Machine Learning jobs in Washington? For Remote Director Machine Learning jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Remote Director Machine Learning jobs in Washington look for? The top searched job categories for Remote Director Machine Learning jobs in Washington are:
What cities in Washington are hiring for Remote Director Machine Learning jobs? Cities in Washington with the most Remote Director Machine Learning job openings:
Remote Director Machine Learning jobs near you

Machine Learning Engineer

10a Labs

Washington, DC • On-site, Remote

$130K - $200K/yr

Other

Medical, Dental, Vision, PTO

Re-posted 13 hours ago

Job description

About the Role:

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and intelligence applications.

This role spans the full ML lifecycle, from dataset development and experimentation to model training, evaluation, deployment, and monitoring. You will work both independently and collaboratively across projects involving multimodal classification systems, frontier model evaluations, model distillation research, and agentic workflows. The ideal candidate combines strong engineering fundamentals with a research mindset and enjoys tackling ambiguous, high-impact problems at the frontier of AI.

You will collaborate closely with researchers, software engineers, red teamers, and subject-matter experts to develop production-ready systems that support leading AI organizations and technology companies.

Responsibilities may include:

  • Design, train, evaluate, and deploy machine learning models across text, image, audio, and multimodal domains.
  • Develop and improve classification systems for safety, security, abuse detection, and intelligence applications.
  • Conduct experiments to benchmark, evaluate, and compare AI models, including large language models and multimodal systems.
  • Contribute to model distillation, optimization, and fine-tuning efforts to improve performance, efficiency, and deployability.
  • Design evaluation pipelines, metrics, and testing frameworks to measure model capabilities, reliability, and safety.
  • Build agentic systems and automated workflows for evaluation, red teaming, research, and large-scale experimentation.
  • Own ML projects from initial research and prototyping through production deployment and monitoring.
  • Partner with software engineers to productionize ML systems and support ongoing improvements.
  • Provide technical expertise and guidance across client engagements and internal research initiatives.

We're looking for someone who: 

  • Brings curiosity, creativity, and rigor to ambiguous research and engineering problems, with a bias toward experimentation and rapid iteration; 
  • Thrives in collaborative, interdisciplinary environments while also being comfortable independently driving projects to completion;
  • Communicates technical concepts clearly to both technical and non-technical audiences;
  • Is resourceful, proactive, and comfortable operating in a fast-moving startup environment.
  • Is excited about developing novel approaches that advance the state of AI safety, evaluation, and security.

Requirements:

  • 3-5+ years of professional experience building and deploying machine learning systems.
  • Strong proficiency in Python and modern machine learning frameworks such as PyTorch and/or TensorFlow
  • Experience working across multiple modalities, with expertise in one or more of:
    • Computer Vision: image classification, object detection, OCR, segmentation, deepfake detection, multimodal vision-language systems, or related areas.
    • Natural Language Processing: LLMs, text classification, information extraction, retrieval systems, speech-to-text, agentic applications, or related areas.
  • Experience training, fine-tuning, evaluating, and deploying machine learning models in production environments.
  • Experience designing evaluation methodologies, benchmarking systems, and model performance metrics.
  • Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.)
  • Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ML infrastructure, workflow orchestration, storage, and database services.
  • Familiarity or experience with model distillation, synthetic data generation, reinforcement learning, or AI evaluation research is strongly preferred.

Preferred:

  • Experience working with frontier language models, multimodal foundation models, or AI safety evaluations.
  • Prior experience in cybersecurity, trust and safety, abuse prevention, threat intelligence, or related domains. 
  • Experience with retrieval-augmented generation (RAG), AI agent frameworks, and context orchestration systems such as LangChain, LlamaIndex, OpenAI Agents, or AutoGen.

Compensation:

  • Salary Range: $130K-$200K, depending on experience and location
  • Bonus: Performance-based annual bonus
  • Professional Development: Support for conferences, continuing education, or leadership training
  • Work Environment: Fully remote, U.S.-based
  • Health Benefits: Comprehensive health, dental, and vision coverage
  • Time Off: Generous PTO and paid holiday schedule

Apply