Top Entry Level Machine Learning Jobs in Washington, DC

Supports CrowdStrike Falcon Intel Recon customers by analyzing and triaging cyber threat intelligence, explaining dark web findings, managing customer relationships, escalating noteworthy intelligence, and communicating feedback to Intelligence and Engineering teams. The role requires knowledge of threat intelligence lifecycles, dark web ecosystems, enterprise security, customer engagement, AI-enabled workflows, and presenting technical information to varied audiences. Travel is minimal to light, up to 25%.

Develop and test C++ and Python software, embedded firmware, autonomy features, simulation tools, and testing infrastructure for UAV systems. Work with sensors, perception, planning, mission systems, and real or simulated data. Investigate failures using logs and telemetry, support hardware- and software-in-the-loop integration, collaborate with multidisciplinary engineering teams, document workflows, and participate in code reviews and design discussions.

Design and modernize enterprise network platforms across data center, campus, WAN, cloud, and edge environments. Build reusable automation services, APIs, data pipelines, observability integrations, cloud-native platforms, and AI-enabled workflows for NetOps and NOC operations. Advise clients on network architecture, automation, AIOps, responsible AI, resilience, and operational transformation while creating reusable frameworks, code components, reference architectures, and delivery accelerators.

Develop advanced analytical models, predictive forecasts, and AI/ML capabilities supporting Navy Reserve readiness and mission assurance. Build analytics products, dashboards, and visualizations; apply statistical analysis, machine learning, data mining, and predictive modeling to identify risks and optimize resources. Collaborate with engineers, analysts, and government stakeholders, evaluate emerging technologies, communicate recommendations, and potentially lead analytic workstreams.

Own research and implementation of post-training methods for legal language models, including supervised fine-tuning, reinforcement learning, reward design, verifiers, preference modeling, evaluations, and distributed training. Lead experiments from research direction through production deployment, while debugging training instability, reward hacking, and divergence. Build rigorous evaluation systems and production-quality Python code for long-horizon legal reasoning and agentic workflows.

Build synthetic data pipelines, evaluation systems, benchmarks, verifiers, rubrics, and quality metrics for legal AI models and agentic workflows. Partner with researchers, legal experts, and customers to investigate model failures, validate metrics, maintain data quality and provenance, and convert expert feedback and product usage into training signals and improved evaluations.

The Director, AI Sales will grow Databricks’ enterprise AI business by driving strategic account adoption, production usage, bookings, and consumption. Responsibilities include developing AI sales plays, enabling field teams, securing executive and technical sponsorship, advancing complex opportunities, coordinating sales and partner stakeholders, and translating customer feedback into product and go-to-market recommendations.

Conduct empirical AI safety research with mentor guidance, owning projects from hypotheses and controlled experiments through analysis and technical writing. Build and debug evaluation harnesses, agent environments, datasets, monitoring methods, and post-training experiments. Investigate model failures, alignment, deception, misuse, multi-agent risks, and evaluation reliability. Produce reproducible code, experiment logs, research analyses, and papers for potential submission to leading machine learning conferences.

Develop and support software, automation tools, dashboards, and alarms for cloud infrastructure, cost attribution, and cloud efficiency. Collaborate with cross-functional teams and senior engineers to design reliable systems, APIs, databases, tests, and AI agents. Contribute to architecture and automation strategies while maintaining strong security, communication, and delivery practices.

Leads enterprise software architecture, development, testing, modernization, and secure delivery. Establishes development standards and frameworks while building scalable applications across air-gapped, hybrid, and on-premises environments. Designs AI and GenAI solutions, data architectures, ETL pipelines, cross-domain integrations, and distributed systems using modern application technologies. Provides technical leadership for secure, interoperable, high-performing solutions involving AI orchestration, databases, containers, and enterprise infrastructure.

Conduct independent AI research spanning large language models, reasoning, knowledge acquisition, factuality evaluation, and trustworthy AI. Translate research ideas into scalable prototypes, implement and extend state-of-the-art methods, design experiments, develop algorithms and evaluation methods, use AI-assisted development tools, and communicate findings through technical reports and research papers.

Develop and refine machine learning models using Python, analyze datasets with MongoDB, and integrate preprocessing and data pipelines. Evaluate models, tune hyperparameters, benchmark performance, document experiments, and deliver data-driven recommendations. Collaborate remotely with cross-functional contributors to improve and operationalize machine learning solutions in cloud or enterprise environments.

Conduct coding-heavy machine learning research involving model development, training pipelines, inference systems, numerical methods, performance optimization, and validation. Build reproducible workflows in Python and command-line tools, implement model and data pipeline components, debug system failures, and verify correctness and performance. The contract is fully remote, flexible, and approximately 15 hours per week, with output-based compensation.

Build production-ready agentic AI capabilities focused on monitoring, guardrails, evaluation, security, quality, and human-agent collaboration. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and explore emerging agentic technologies.

Leads AI governance and assurance engagements, designing policies, controls, operating models, risk assessments, and compliance programs. Evaluates AI security, model governance, third-party solutions, and control effectiveness against frameworks including NIST AI RMF, ISO 42001, and the EU AI Act. Collaborates with security and engineering teams to test threats such as prompt injection, model manipulation, data poisoning, and leakage. Advises executives and clients, develops methodologies and proposals, researches emerging AI risks and regulations, and supports business development.

Review and annotate robotic-system video footage by identifying objects, movements, events, visual features, and anomalies. Provide feedback to improve video datasets and annotation consistency, collaborate with AI teams on annotation protocols, document observations, participate in quality reviews, and recommend improvements to video capture and labeling strategies.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Paid, part-time Creative Advisor collaborating with AI research and machine learning teams. The role involves sharing creative expertise, defining quality standards, developing work, reviewing model outputs, identifying limitations, and helping fellow creators engage with the program. Applicants should be leading creators with strong portfolios and interest in shaping AI’s impact on creative practice; no AI experience is required.

Applies statistics, experimentation, data analysis, and machine learning to complex datasets and AI model evaluation. Designs analyses, develops and validates models, performs Python and Pandas data transformations, creates expert prompts and datasets, identifies quantitative errors, and provides structured feedback. Produces reproducible code, analytical reports, and clear documentation while collaborating with stakeholders and reviewing peer contributions.

Own product outcomes for an AI-native cloud infrastructure platform. Conduct customer discovery, frame problems, prioritize opportunities, prototype solutions, analyze product and business data, and collaborate with Design, Engineering, Sales, Customer Success, and Marketing. The role requires enterprise B2B product judgment, technical fluency across cloud infrastructure and FinOps, strong commercial awareness, AI-assisted workflows, outcome measurement, and end-to-end ownership from strategy through iteration.

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

Leads Utility’s AI practice by defining its roadmap, overseeing AI research and deployment, identifying client opportunities, mentoring AI teams, and establishing responsible AI practices. The role combines technical leadership, product strategy, client advisory, and operational execution across machine learning, generative AI, and intelligent automation initiatives.

Develop and deploy mineral exploration instruments, sensor systems, data acquisition methods, and proprietary software. Integrate geospatial, geophysical, geochemical, geological, and geographic data; build statistically rigorous and physics-based models; identify ore-forming anomalies; and generate 2D and 3D exploration predictions. Collaborate with data scientists, engineers, geoscientists, vendors, and external partners to design field programs, deploy sensors, reduce uncertainty, and guide exploration decisions.