Machine Learning Jobs in Washington DC

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Looking for Machine Learning jobs in Washington DC? Browse our curated listings with transparent salary information to find the perfect Machine Learning position in the Washington DC area.

Test Automation Engineer-Mid

Company:

Location: Washington, DC

Posted Feb 03, 2025

Full Stack Developer- Sr.

Company:

Location: Washington, DC

Posted Feb 03, 2025

Manager, Product Management - Fraud (US Card)

Company: Capital One

Location: Washington DC

Posted Feb 01, 2025

Capital One's Fraud Product team is seeking a Product Manager for two key areas: Authentication & Verification and Payment Fraud Decisioning. The role involves driving the roadmap and delivery for real-time intelligent authentication experiences, determining strategy for pooling and reusing authentication tools, building a 101 Learning of the Authentication and Verification Space, outlining CX pain points, and managing a Senior Associate. The Product Manager will also lead a tech team, own feature and capability delivery, partner with data science teams, and collaborate with partner teams. Key qualifications include intellectual curiosity, communication and influencing skills, being a doer, passionate and customer-focused, a learner, and a team player. Basic qualifications include a Bachelor's Degree or military experience and at least 3 years of product management experience or experience in product design, agile delivery, business analysis, data science, or software engineering.

Apex Developer-Mid

Company:

Location: Washington, DC

Posted Feb 03, 2025

Associate General Counsel, IP Product

Company: Meta

Location: Washington, DC

Posted Feb 03, 2025

Project Manager (TS/SCI with Poly)

Company: Maxar Technologies

Location: Washington DC

Posted Feb 02, 2025

Maxar Technologies is seeking a Project Manager for a critical mission involving the development, optimization, and support of a geospatial data visualization web application. The role offers opportunities for professional growth, including dedicated development time, online learning, conference attendance, and education reimbursement. The ideal candidate should have a technical or business degree, 8+ years of relevant experience, and project management skills. Familiarity with system accreditation, Lean Agile Development, JIRA, Confluence, AWS cloud services, and geospatial tools is preferred. The base pay for this position in the Washington DC metropolitan area is $119,000 - $197,000 annually.

Accounting Associate

Company: Aprio

Location: Washington DC

Posted Feb 01, 2025

Aprio is a nationally ranked CPA and advisory firm with 22 US office locations and over 2100 team members. They offer a top-rated culture, vast growth opportunities, and competitive compensation. The Accounting Associate position involves verifying daily deposits, entering and coding invoices, performing bank reconciliations, managing vendor profiles, and assisting in the preparation of 1099 forms. Qualifications include a Bachelor's Degree in Accounting, previous accounts payable and bookkeeping experience, and proficiency with Microsoft Suite products. Aprio provides medical, dental, and vision insurance, a 401k with profit sharing, flexible spending accounts, parental leave, tuition assistance, and a top-rated wellness program. They encourage diversity and offer a flexible working environment.

Manager, Go-to-Market Strategy Consulting

Company: MarketBridge

Location: Washington DC

Posted Feb 02, 2025

Marketbridge is a growth consulting and marketing firm seeking a GotoMarket Strategy Manager with 5+ years of marketing experience and 3+ years in strategy consulting or market research. The role involves project management, research, storytelling, client relations, marketing expertise, and mentorship. The ideal candidate will have exceptional project management skills, be a persuasive communicator, and be proactive. Marketbridge fosters an entrepreneurial culture with a focus on professional growth, offering competitive compensation, flexible time off, and various benefits. The company is committed to supporting humanitarian and environmental nonprofit organizations.

Frequently Asked Questions

What are typical salary ranges by seniority for machine learning roles?
Entry‑level ML Engineer: $90k–$120k; Mid‑level ML Engineer or Data Scientist: $120k–$160k; Senior ML Engineer or Research Scientist: $160k–$220k; Lead ML Engineer or Principal Research Scientist: $200k–$280k; AI Product Manager: $130k–$180k depending on experience and market.
What skills and certifications are most valuable in machine learning today?
Core language: Python; Deep learning frameworks: TensorFlow, PyTorch; Scikit‑learn for classical models; SQL and NoSQL databases for data ingestion; Docker and Kubernetes for deployment; Cloud AI services such as AWS SageMaker, GCP Vertex AI, Azure ML. Certifications: TensorFlow Developer Certificate, AWS Certified Machine Learning – Specialty, GCP Professional Machine Learning Engineer.
How common is remote work for machine learning positions?
Over 70% of ML roles allow full remote or hybrid arrangements. Startups and fintech firms tend to offer 100% remote options, while larger enterprises often provide hybrid models with occasional on‑site data‑center visits. Remote work is especially prevalent for roles focused on model training and research.
What career progression paths exist in machine learning?
Typical paths: ML Engineer → Senior ML Engineer → Lead ML Engineer → ML Manager; Data Scientist → Senior Data Scientist → Lead Data Scientist → Head of Data; Research Scientist → Senior Research Scientist → Principal Scientist → Chief Data Scientist; ML Ops Engineer → Senior ML Ops Engineer → Lead ML Ops Engineer → Director of MLOps. Progression often involves moving from coding to architecture, then to leadership and strategy.
What are the current industry trends shaping machine learning hiring?
Key trends: reinforcement learning for autonomous systems; federated learning for privacy‑preserving models; edge AI for IoT devices; AutoML platforms speeding model deployment; MLOps practices for scalable pipelines; explainable AI and ethics compliance; and increased demand for AI governance roles.

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