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Director Of Machine Learning Jobs in Washington (NOW HIRING)

Multiyear Contract We are looking for candidates with 5-10 years of professional experience with a combination of data science and machine learning expertise with knowledge of AI governance, MLOps ...

Multiyear Contract We are looking for candidates with 5-10 years of professional experience with a combination of data science and machine learning expertise with knowledge of AI governance, MLOps ...

Multiyear Contract We are looking for candidates with 5-10 years of professional experience with a combination of data science and machine learning expertise with knowledge of AI governance, MLOps ...

Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ... With direct access to company leadership, a laid-back and inclusive atmosphere, and exceptional ...

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Director Of Machine Learning information

What does a director of machine learning do?

A Director of Machine Learning leads and oversees machine learning teams and projects within an organization. They are responsible for setting the strategic direction for AI and ML initiatives, managing research and development, and ensuring successful deployment of machine learning solutions. Their role often includes collaborating with other departments, mentoring staff, and staying updated with the latest advancements in the field. Ultimately, they ensure that machine learning technologies are effectively leveraged to meet business goals.

What are the typical challenges a director of machine learning faces when leading cross-functional teams?

As a Director of Machine Learning, one common challenge is aligning the goals and expectations of data scientists, engineers, and business stakeholders. Balancing innovative research with practical deployment requires strong communication and prioritization skills. Additionally, ensuring that machine learning solutions are scalable, ethical, and meet organizational needs often involves navigating resource constraints and evolving technologies. Effective directors foster collaboration, provide technical guidance, and help teams stay focused on deliverable outcomes.

What are the key skills and qualifications needed to thrive as a director of machine learning, and why are they important?

To thrive as a Director Of Machine Learning, you need advanced expertise in machine learning algorithms, statistical analysis, and a strong background in computer science or a related field, typically supported by a master's or PhD degree. Familiarity with technical tools such as Python, TensorFlow, PyTorch, and cloud platforms, along with project management experience, is essential. Leadership, strategic vision, and excellent communication skills are crucial soft skills for guiding teams and aligning ML initiatives with business goals. These skills ensure that complex ML projects are successfully designed, implemented, and deliver tangible value to the organization.

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

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

What cities in Washington are hiring for Director Of Machine Learning jobs?

Cities in Washington with the most Director Of Machine Learning job openings:

Director of Machine Learning (Washington)

Washington, DC • On-site

Virtualitics
Software Development • 51 - 200 employees

Full-time

This job post has expired today. Applications are no longer accepted.


Key responsibilities

  • Lead the Machine Learning Engineering team and oversee the development of AI-native readiness intelligence applications.

  • Provide guidance on architecting robust, scalable applications and managing the complete data lifecycle from acquisition to model inference and postprocessing.

  • Collaborate with cross-functional teams to align engineering deliverables with strategic customer commitments and communicate technical progress, risks, and ROI.


Job description

Director of Machine Learning Engineering (Secret Cleared, DMV)About Virtualitics:

Virtualitics is a fast-growing, Caltech-born defense tech company (:120 people) dedicated to reversing the decline in military readiness. Virtualitics builds AI-native readiness intelligence for the US and allied nations’ defense sector. Virtualitics Iris transforms how defense organizations act on maintenance, logistics, personnel, and global force management data. Our team is actively refining agentic workflows, composable AI agents, and generative interfaces to replace static dashboards with dynamic, conversational intelligence.

What You Will Do:
  • Lead the Machine Learning Engineering team
  • Provide guidance on how to architect robust, scalable applications using sound engineering principles, managing the complete data lifecycle from acquisition to model inference and postprocessing
  • Tackle runtime performance and optimize data access patterns for highly responsive applications
  • Collaborate across the delivery team (e.g. Product, Customer Success, DevOps, and QA) to align engineering deliverables with strategic customer commitments
  • Tackle key issues across Delivery and Platform teams and flag pain points to help influence the roadmap
  • Set the technical hiring bar and mentor engineers, ensuring teams are well-staffed and capable
  • Clearly communicate technical progress, risks, and ROI, directly linking AI team output to revenue, mission impact both up and down as well as internally and externally
Core Requirements:
  • Clearance & Location: Must hold at least a U.S. Secret security clearance and be willing to upgrade to a TS/SCI if needed. Must be willing to travel to customer locations as needed
  • Engineering Fundamentals: A degree in Computer Science or related field and 8+ years of software engineering experience. We target candidates with a strong background in software engineering and production deployment, rather than strictly research-oriented backgrounds
  • AI & Systems: A proven track record of deploying software into production environments. Has shipped production-grade AI / agentic systems
  • GPU Fluency: Has experience with offloading compute for AI systems to GPUs and is comfortable with designing training and inference pipelines
  • Full Stack & DevSecOps: Understands full stack software development, DevSecOps, and AI systems holistically. Familiarity with Docker, Kubernetes, and Git
  • Data Ecosystem: Proficiency in Python with a solid understanding of the Python Data Stack (pandas, NumPy, scikit-learn, PyTorch, Matplotlib, etc.). Experience working with a wide variety of data (both structured and unstructured) from different sources
  • Culture & Values: Embody Virtualitics core values by bringing a positive attitude, fostering a highly collaborative environment, and always being ready to lean in to tackle complex challenges alongside the team
Preferred Qualifications (Pluses):
  • Has built and cultivated a high functioning Machine Learning Engineering team before
  • Has contributed to building engineering excellence and has top-tier engineering experience
  • Experience with big data technologies and frameworks (Spark, Databricks, Snowflake, etc.)

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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