Top AI & Machine Learning Jobs

Design, build, and operate AWS-based microservices and asynchronous workflows powering AI-driven laboratory protocol generation. Develop NLP pipelines, classifiers, embedding and RAG infrastructure, evaluation frameworks, guardrails, cost monitoring, and observability. Integrate AI systems into production SaaS architecture, ensure reliability and safe scaling, and support post-launch operations through debugging, incident response, and continuous improvement.

Build and deploy NLP and machine learning models that analyze student and educator text. Develop classical ML and LLM-based features, production inference systems, validation and monitoring pipelines, data-drift detection, retraining workflows, and responsible AI guardrails. Partner with product, engineering, and data science teams to improve real-time education technology features while ensuring accuracy, fairness, privacy, and reliability.

Develop full-stack web applications integrating AI, machine learning, and computer vision models. Build and test software using Java, Spring Boot, Python, React, MongoDB, and Docker; collaborate with data science teams, customers, and stakeholders; enhance the company website; and contribute to products throughout their lifecycles.

Develop machine-learning models and analytical pipelines for space domain awareness, including trajectory classification, anomaly detection, benchmarking, calibration, and evaluation under sparse and noisy observations. Integrate models with astrodynamics and embedded systems, optimize inference for constrained onboard hardware, and support model lifecycle, retraining, systems engineering, and operational documentation.

Build and improve AI agents that automate engineering, fleet operations, and business workflows. Responsibilities include integrating LLMs with internal systems, implementing planning and tool-using agent workflows, developing backend services and APIs, evaluating models and frameworks, and contributing to internal AI platforms. The role requires strong programming fundamentals, Python or comparable language skills, and familiarity with machine learning or LLMs. Preferred experience includes agent frameworks, RAG, vector databases, cloud platforms, and containerization.

Build and own Lightspark’s internal AI and data platform, including agent infrastructure, MCP servers, tool integrations, governed data, sandboxed execution, and production pipelines. Partner with Finance, Sales Ops, Compliance, and other teams to replace manual workflows with reliable systems. Extend dbt and cloud data warehouse infrastructure, support monitoring and data reliability, and improve engineering tooling. The role requires production experience with AI agents, LLM APIs, internal platforms, and TypeScript/Node.js or Python.

Leads complex data exploration, analytics, statistical modeling, and AI evaluation projects supporting strategic intelligence. Develops scalable testing libraries, validates AI and machine learning systems, performs TEVV and risk mitigation, and communicates complex findings and system risks to nontechnical leadership. The role requires advanced analytics experience with LLMs or agentic AI, CI/CD validation, classified DoD or Intelligence Community networks, and a TS/SCI clearance with current or attainable CI Polygraph.

Develops and supports data science and informatics solutions for DoD data, analytics, and AI initiatives. Responsibilities include building and testing machine learning models, analyzing large datasets, identifying patterns, creating data models, documenting requirements and findings, and presenting technical concepts to diverse audiences. The role requires experience with machine learning, programming, SQL, data visualization, terabyte-scale datasets, and GPUs, along with a Secret clearance and ability to obtain TS/SCI clearance.

Leads the strategy, architecture, governance, and lifecycle management of engineering analytics, automation tools, data workflows, and AI/ML initiatives. Builds and mentors a specialized technical team, identifies workflow optimization opportunities, manages intellectual property development, and coordinates with engineering, IT, legal, customers, and vendors. Tracks KPIs and presents executive reports on technical risks, innovation, tool adoption, commercial readiness, and IP progression.

Supports consulting teams by analyzing workflows, identifying AI and automation opportunities, building and testing prototypes, analyzing data, creating reports and user insights, documenting solutions, and presenting recommendations to stakeholders. The internship provides hands-on exposure to AI, automation, technology, and financial advisor business operations.