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Remote Director Machine Learning Jobs in Washington

Lateral Insights LLC

Senior AI/ML Engineer

Great Falls, VA · Remote

$105K - $145K/yr

... machine learning platforms, and practical experience operationalizing AI solutions from concept to production. Location: Vienna VA (We will consider Remote candidates within US Mainland on EST ...

System One

Be Seen First

Data Scientist (AI)

Washington, DC · Remote

$125K - $190K/yr

AI Data Scientist REMOTE US Citizen What You Will Need: * Bachelor's or Master's degree in Data ... Solid experience in data science, machine learning, or applied analytics roles * Experience ...

NT Concepts

Data Scientist

Herndon, VA · On-site +1

... the machine learning development lifecycle, from data curation and synthetic data generation to ... Herndon, VA with remote flexibility. Must be local to the DC Metro area. Responsibilities * Curate ...

Maximus

Senior Director, AI Systems Engineering

VA · On-site +1

... Machine Learning operations. - 12+ years experience with Software Engineering. - 5+ years ... Remote EEO Statement Maximus is an equal opportunity employer. We evaluate qualified applicants ...

Knexus

AI Software Engineer (Remote)

Vienna, VA · Remote

This role involves leveraging cutting-edge technologies, including GenAI and machine learning ... Our work has a direct, mission-driven impact, and we believe that our innovative ideas help us stay ...

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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:
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Remote Director Machine Learning jobs near you
Director, AI Engineering (Data Science)

Director, AI Engineering (Data Science)

Blend360

Columbia, MD • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 6 days ago

Job description

Company Description
Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com.
Job Description
We are seeking a visionary and execution-oriented Director of AI Engineering to join our team. In this senior, client facing role, you will own the full lifecycle of AI model development, setting technical strategy and ensuring that cutting-edge machine learning solutions move from concept to production with business impact at their core.
With roots in data science and hands-on expertise in custom transformer architecture, you will bring both the credibility to lead technical teams. You will operate at the intersection of stakeholder management and deep technical execution and you should be equally comfortable presenting to a senior audience and reviewing model architecture with your team.
This is a high-impact, high-autonomyy role with significant organizational influence. You will define the AI roadmap, establish engineering best practices, and champion a culture of rigorous, reproducible, and responsible machine learning.
Strategic Leadership & Team Management
  • Define technical investments with business objectives
  • Mentor, and manage AI/ML engineers, senior data scientists, and MLOps engineers-setting performance expectations and a high-performance culture.
  • Partner with cross-functional leaders to prioritize initiatives, allocate resources, and measure organizational impact.
  • Establish engineering standards, code review practices, and model governance frameworks across the AI org.

Custom Transformer Architecture & Model Development
  • Serve as the technical authority on deep learning architecture-personally leading the design and development of custom transformer models for sequence modeling, customer propensity scoring, audience segmentation, and churn prediction.
  • Drive innovation in attention mechanisms, positional encodings, and tokenization strategies specifically suited to tabular, time-series, and event-stream data common in marketing and telecom.
  • Oversee adaptation and fine-tuning of foundation models (BERT, T5, TabTransformer, LLMs) for proprietary client datasets, ensuring domain-specific performance.
  • Champion reproducible experimentation and architectural decision documentation across the team.

Data Science & Applied Analytics
  • Oversee end-to-end data science workflows: problem framing, feature engineering, model development, validation, and production deployment.
  • Ensure statistical rigor in experimental design, causal inference, A/B testing, and offline/online evaluation frameworks.
  • Guide the team in building robust data pipelines for large-scale structured and unstructured datasets, including clickstream, CRM, ad telemetry, CDRs, and network KPIs.

Client & Executive Engagement
  • Lead technical discovery and solutioning with enterprise clients translating ambiguous business problems into well-scoped AI initiatives.
  • Present AI strategy, model results, and roadmap updates to C-suite and senior client stakeholders with clarity and executive presence.
  • Contribute to business development: support RFP responses, lead technical portions of client proposals, and help grow the AI engineering practice.

MLOps, Infrastructure & Governance
  • Establish production standards for model deployment, monitoring, drift detection, and automated retraining across cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
  • Drive adoption of MLOps best practices including CI/CD for ML, containerization (Docker/Kubernetes), and experiment tracking (MLflow, W&B, DVC).
  • Implement model governance, explainability, and responsible AI standards in compliance with client and regulatory requirements.

Qualifications
  • Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a closely related quantitative field; Ph.D. strongly preferred.
  • 10+ years of progressive experience in data science and machine learning, with at least 3-5 years in a people management or technical leadership role (Director, Sr. Manager, or Principal Engineer level).
  • Proven track record of leading high-performing AI/ML engineering teams in a fast-paced, client-facing or product environment.
  • Deep, hands-on expertise designing and training custom transformer architectures from scratch-not only fine-tuning pre-built checkpoints, but architecting novel attention mechanisms, embedding strategies, and model topologies.
  • Strong applied data science foundation: feature engineering, statistical modeling, causal inference, and experimental design across large-scale datasets.
  • Proficiency in Python and core ML/DL libraries: PyTorch (preferred), TensorFlow, HuggingFace Transformers, scikit-learn, XGBoost/LightGBM.
  • Direct experience with industry datasets in marketing & media (DSP/DMP logs, ad impression data, attribution pipelines, MMM) OR telecommunications (CDRs, network KPIs, subscriber behavior, churn datasets).
  • Command of SQL and large-scale data platforms: Spark, BigQuery, Snowflake, or Databricks.
  • Experience owning end-to-end MLOps: cloud deployment (SageMaker, Vertex AI, or Azure ML), monitoring, CI/CD for ML, and model governance.
  • Exceptional executive communication skills-able to translate complex model behavior into business language for C-suite and client audiences.

PREFERRED QUALIFICATIONS
  • Professional services experience across multiple client engagements or business units
  • Background in privacy-preserving ML: federated learning, differential privacy, or synthetic data generation-especially relevant in post-cookie marketing environments.
  • Knowledge of graph neural networks (GNNs) for social graph or network topology analysis in telecom contexts.
  • Published research or conference contributions (NeurIPS, ICML, KDD, RecSys, or industry equivalents) related to applied transformers, tabular deep learning, or domain-specific AI.
  • Experience with real-time inference and streaming ML pipelines (Kafka, Flink, or similar).
  • Demonstrated ability to build strategic partnerships with external clients, contributing to revenue growth or account expansion through technical leadership.
  • Deep experience with openai focused on embeddings
  • Experience building custom transformer models

Additional Information
The starting pay range for this role is $180,000 - $240,000. Actual compensation within the range will be dependent on several factors including but not limited to relevant experience, skills, certifications, training, and location. It is not typical for an individual to be hired at or near the top of the range and determining factors for compensation are considered for each individual circumstance. BLEND360 also offers a competitive benefits program to meet the health and financial well-being of our team and their families. You can look forward to a range of benefits including medical, dental, vision, 401K, PTO, paid holidays, commuter benefits, spending accounts, life insurance, disability coverage, and EAPs.

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