16 days ago
Washington, DC, USAMid Level
Base Salary
$150k - $200k/yr
Responsibilities
- Design, develop, and deploy machine learning and AI-powered features into production systems.
- Apply supervised, unsupervised, and deep learning techniques to structured and unstructured data.
- Build and evaluate models for classification, ranking, prediction, NLP, and anomaly detection.
- Develop and integrate GenAI solutions including LLM-based workflows, retrieval-augmented generation, and agents.
- Translate business and user needs into ML problem statements, metrics, and experiments.
- Implement data pipelines and feature engineering workflows for model training and inference.
- Evaluate model performance, bias, drift, and reliability, and iterate based on results.
- Integrate models into APIs, services, and user-facing applications with software engineers.
- Contribute to architecture decisions involving model serving, scalability, and cost optimization.
- Document technical approaches, assumptions, and tradeoffs.
Requirements
- Strong foundation in machine learning concepts including model selection, training, validation, and evaluation.
- Experience building and deploying ML models in real-world applications.
- Proficiency in Python and common ML libraries such as PyTorch, TensorFlow, and scikit-learn.
- Experience with large language models, embeddings, and prompt-driven systems.
- Familiarity with data-processing tools and workflows such as Pandas, SQL, and Spark.
- Understanding of software engineering best practices including version control, testing, and code reviews.
- Ability to evaluate tradeoffs among accuracy, latency, cost, and maintainability.
- Strong communication skills and comfort working with cross-functional teams.
- Applicants must be authorized to work in the United States.
- Candidates must be located in the DMV area and able to participate in in-office collaboration.
- Bonus qualifications include experience in innovation, R&D, labs, exploratory engineering, cloud model deployment, inference at scale, MLOps, model monitoring, ML CI/CD, experiment tracking, architecture discussions, or technical strategy.
- Certain roles may require U.S. citizenship and the ability to obtain and maintain a federal background investigation or security clearance.
Benefits
- Hybrid/remote work with fully remote benefits stated; candidates must be located in the DMV area and participate in in-office collaboration.
- Annual stipend.
- Comprehensive benefits package.
- Company Match 401(k) plan.
- Flexible PTO and paid holidays.
Tech Stack
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