Top AI & Machine Learning Jobs

Support the development and testing of AI-enabled engineering workflow tools. Responsibilities include building AI and machine learning solutions, integrating AI into existing engineering tools, implementing information retrieval, documentation review, and test automation solutions, and collaborating with engineers to analyze requirements and design effective AI-assisted workflows.

Leads the architecture and development of Java-based enterprise applications, microservices, REST APIs, and cloud solutions on AWS. Develops applied generative AI solutions using LLMs, RAG, fine-tuning, prompt engineering, and managed platforms such as AWS Bedrock. Guides application design, deployment, security, monitoring, performance, and technical implementation while collaborating with project teams. The role is fully onsite in McLean, Virginia and requires extensive experience with modern Java, cloud-native technologies, containers, UI frameworks, and AI development.

Coordinates a small team of research scientists, balancing rapidly changing research priorities, strategic direction, external deadlines, deliverables, and stakeholder tradeoffs. The role requires high-stakes judgment, negotiation, communication, empathy, and execution under uncertainty. It partners closely with executive leadership to influence frontier AI research strategy and ensure ambitious work is delivered with quality and timeliness.

Annotates, categorizes, validates, and quality-checks structured and unstructured datasets for AI platforms supporting government operations. Maintains labeling records, metadata, change histories, and quality documentation; identifies discrepancies and coordinates resolutions with technical teams, subject matter experts, and government stakeholders. Applies approved taxonomies, annotation standards, sampling plans, and agreement measures while supporting repeatable data-quality workflows in secure environments.

Improve production conversational AI agents through data analysis, experimentation, evaluation, and software engineering. Responsibilities include diagnosing failures in customer conversations, building trustworthy metrics and datasets, improving voice interactions, testing models and prompts, optimizing latency and reliability, and shipping measurable production improvements. The role requires hands-on ownership across the full agent improvement cycle, with Staff-level engineers also shaping evaluation strategy and mentoring team practices.

Leads global AI integration across software engineering platforms and manages distributed engineering leaders across three countries. Drives adoption of AI-assisted development tools, modernizes software delivery workflows, oversees security and compliance, and integrates AI into React, .NET, Java, Salesforce, and MuleSoft platforms. Requires extensive software development and senior engineering leadership experience, plus expertise in cloud, SaaS architecture, privacy, tenant isolation, and enterprise AI transformations.

Analyzes large datasets using data science, machine learning, predictive modeling, and big data technologies. Designs approaches for data handling, search, retention, storage, and processing; develops software to improve data search; validates data quality and processes; and reports actionable findings. The role emphasizes scientific analysis over visualization and requires an active Top Secret/SCI-eligible clearance, with potential Counterintelligence Polygraph requirements.

Build, train, evaluate, deploy, and monitor dermatology computer vision and multimodal models. Responsibilities include lesion detection and tracking, dataset and labeling workflows, inference services, uncertainty estimation, bias evaluation, production monitoring, and integration with TypeScript healthcare products. The role partners closely with physicians and engineers in a regulated environment, emphasizing patient safety, reliability, observability, and iterative production delivery.

Designs and governs secure, responsible AI/ML integrations for government contracting and service-management workflows. Translates mission needs into measurable use cases, defines API and workflow integration patterns, establishes evaluation and oversight controls, protects CUI and classified information, coordinates cybersecurity and authorization requirements, guides pilots, and monitors deployed model performance. Requires six or more years of technology delivery experience, AI/ML integration expertise, a bachelor’s degree or equivalent experience, U.S. citizenship, and Secret clearance eligibility.

Build and maintain AI-powered healthcare products for patient engagement, care coordination, and claims management. Develop full-stack software, APIs, backend services, automation pipelines, and LLM integrations using Claude Code. Apply software engineering, DevOps, and MLOps practices for testing, deployment, monitoring, security, and performance. Collaborate with product, design, engineering, and clinical stakeholders in a remote Agile environment.