Top Entry Level Machine Learning Jobs in Richmond, VA

Designs, deploys, and optimizes GenAI data solutions for enterprise clients. Responsibilities include translating client needs into scalable workflows, creating annotation guidelines, delivering high-quality datasets, building validation and automation pipelines, and ensuring data accuracy, reliability, observability, and readiness for model development.

Run Discovered Labs’ founder-led content operation across LinkedIn and YouTube, from topic selection and editorial development through publication and performance analysis. Responsibilities include shaping founder content, coordinating external partners, repurposing long-form material, improving AI-supported content systems, and using engagement and pipeline data to guide editorial decisions. The role requires strong editorial judgment, hands-on execution, social content expertise, and the ability to work autonomously in an AI-forward, remote environment.

Annotate and label data accurately according to project guidelines, conduct quality checks, provide feedback on annotation tools and processes, collaborate with project teams, maintain efficiency, and identify issues for resolution. The role requires strong attention to detail, critical thinking, adaptability, communication skills, and comfort with repetitive computer-based work.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Develops advanced analytics for national security using large, disparate datasets. Responsibilities include designing data exploitation methods, researching sponsor inquiries, developing custom searches, applying classification, regression, clustering, and anomaly detection algorithms, and supporting threat hunting and malicious-activity detection. The role also involves statistical analysis, data visualization for nontechnical audiences, and prototyping data engineering pipelines.

Annotates, labels, categorizes, validates, and quality-checks structured and unstructured data for AI model training. Maintains dataset accuracy, consistency, documentation, and version control while collaborating with AI engineers, data scientists, and quality teams. The role also supports annotation workflow management, guideline development, calibration exercises, error identification, and continuous process improvement.

Designs, implements, optimizes, and administers generative and agentic AI solutions. Translates business requirements into AI workflows, oversees model deployment and performance, analyzes implementation data using Power BI, and coordinates responsible AI governance. Collaborates with engineering, security, data science, sales, and reporting teams while supporting users and maintaining AI platforms, cloud services, and low-code tools.

Lead technical project success for enterprise AI infrastructure engagements. Collaborate with engineers, clients, and account leaders to capture requirements, decisions, dependencies, risks, and tradeoffs. Track constraints involving security, compliance, data sovereignty, procurement, and restricted environments. Maintain project visibility, align deliverables with business objectives, resolve issues, support implementation-to-operations handoffs, and identify expansion and renewal opportunities. Translate complex technical concepts for engineering, business, and executive stakeholders.

Instructor-led AI and machine learning training covering theory, data preprocessing, algorithms, model building, analytics, real-time projects, case studies, and practical business and IT applications. Participants use Python, TensorFlow, and Scikit-learn and receive certification upon successful completion.

Create end-to-end computer-use tasks for AI agents in Linux desktop environments. Design realistic multi-application scenarios, prepare files and instructions, write Python setup and automated scoring scripts, test tasks, and document them. The contract requires daily check-ins, rapid feedback, and availability throughout a three- to four-week engagement. Experience with AI tools, AI/ML projects, Linux VMs, Git, reinforcement learning environments, or computer-use benchmarks is relevant.

Build and maintain Quora’s machine learning platform and distributed model-serving infrastructure. Develop scalable systems for recommendation and language models, optimize GPU serving latency, throughput, and cost, improve ML tooling and feature stores, and contribute to platform modernization. The role includes production deployment, collaboration with senior engineers, mentoring, and participation in an on-call rotation.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, RAG, model routing, tool calling, guardrails, evaluation, monitoring, and cost optimization. Ship reliable, safe, measurable AI features; develop evaluation frameworks and quality controls; monitor production behavior; and collaborate with client-facing product, engineering, platform, backend, and security teams to deliver practical AI solutions.

Develop and refine public-facing technical documentation for AI and model releases. Consolidate engineering input, verify technical accuracy, establish repeatable documentation standards, and translate complex engineering concepts for technical audiences. The role requires deep engineering and AI/ML knowledge, exceptional writing and editing skills, independent judgment, and the ability to work quickly around release timelines in a fractional, ongoing engagement.

Build and deploy generative AI solutions for creative enterprise workflows across image, video, 3D, vector graphics, and design. Partner with customers to identify opportunities, prototype integrations, adapt multimodal models, integrate AI into production systems, diagnose technical issues, evaluate solution quality, and develop reusable deployment patterns. Collaborate with research, product, engineering, and design teams to translate customer needs into scalable technical solutions.

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.

Provides clinical expertise, customer support, product demonstrations, training, onboarding, and certification for Transpara Breast AI across the USA and Canada. Supports research initiatives, clinical adoption, customer engagement, NPS tracking, workshops, webinars, educational content, case curation, and customer feedback resolution. Collaborates with Sales, Customer Success, Marketing, Engineering, Innovation, and Product Management teams. Requires medical imaging experience, preferably in mammography or breast imaging AI, strong communication and training skills, and approximately 20% travel.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

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.

Owns the end-to-end product strategy and execution for AI-powered systems. Defines requirements, evaluation frameworks, quality standards, and system behavior while balancing quality, latency, cost, reliability, safety, and user experience. Partners closely with ML, backend, and client engineering teams to identify failure modes, improve model performance, prioritize roadmaps, and deliver predictable, useful, trusted AI experiences.

Own the end-to-end strategy, requirements, quality, and execution of an AI-powered email product. Partner closely with ML, backend, and client engineers to define system behavior, evaluate model performance, manage trade-offs across quality, latency, cost, reliability, safety, and UX, and establish feedback loops. Drive prioritization, specifications, experimentation, failure handling, and product quality in a technical zero-to-one environment.

Design, build, and deploy production-ready intelligent solutions using machine learning, deep learning, generative AI, and large language models. Develop RAG pipelines, integrate AI services, manage model deployment and MLOps workflows, and apply strong software engineering practices. Collaborate with engineering, product, and data teams in a distributed environment to create scalable, reliable, responsible AI-powered products.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

Clean, standardize, enrich, and import product data into the PIM while maintaining data integrity and governance standards. Oversee bulk ingestion processes, leverage AI and automation, support data model improvements, resolve product listing issues, and contribute to data hygiene and workflow modernization. The role also involves collaborating with client-facing teams, documenting processes, proofreading product data, communicating progress, and adapting to changing priorities.

Develop, train, evaluate, and deploy machine learning models and AI-powered applications. Responsibilities include data preprocessing, feature engineering, deep learning development, cloud-based model deployment, and collaboration with data engineers, MLOps teams, and business stakeholders. The role also requires applying strong analytical and research skills while staying current with advances in artificial intelligence.

Define and lead enterprise-wide AI/ML architecture strategy, standards, governance, reference architectures, and adoption roadmaps. Design scalable architectures for generative AI, LLM applications, RAG, AI agents, MLOps, data platforms, cloud infrastructure, security, and enterprise applications. Lead technology evaluations, architecture reviews, modernization strategies, responsible AI controls, vendor decisions, and transformation programs while advising executives and technical teams.