Top AI & Machine Learning Jobs

The AVP, Business Analytics & AI leads the execution of data, analytics, and AI strategies, driving measurable business outcomes and ensuring governance of data products and AI operations while partnering with business and technology leaders.

Productionize and optimize ML systems for AI reasoning over structured data. Responsibilities include improving training and inference performance, porting workloads from CPU to GPU, building training and deployment infrastructure, developing experimentation and observability tools, and implementing new model architectures and fine-tuning methods using Python and PyTorch. The role requires strong ML research, systems engineering, software architecture, optimization, and experimental design skills.

Leads multidisciplinary teams across data engineering, AI/ML, scientific computing, and modeling to build reliable biomedical research systems. Sets technical strategy, guides production data platforms, advances responsible AI, and establishes standards for reproducibility, governance, security, observability, and delivery. The role moves research prototypes into dependable production capabilities, develops technical leaders, supports federal proposals, and partners with scientists, engineers, security teams, and government stakeholders.

Develop and optimize advanced AI models, training pipelines, evaluation frameworks, and debugging systems for robotics applications. Lead rapid experimentation, model architecture development, fine-tuning, performance optimization, and interpretability efforts. Collaborate with engineering teams on training infrastructure and deployment. The role emphasizes ML planning for autonomous vehicles, diffusion and autoregressive models, reinforcement learning, and translating research into production systems.

Own the full lifecycle of clinical text extraction models, from problem framing and annotation strategy through production deployment, evaluation, monitoring, and retraining. Build clinical NLP pipelines, establish defensible quality and error-analysis systems, and apply LLMs using rigorous evaluation, observability, and cost controls. Partner with clinical data and backend engineering teams while addressing PHI protection, de-identification, auditability, and access controls. As the first ML hire, set technical direction and potentially build and lead the future ML team.

Analyze complex datasets to identify trends, build predictive models, and apply machine learning to support business decisions. Collaborate with cross-functional teams, communicate insights to technical and non-technical stakeholders, ensure security and confidentiality compliance, and mentor junior data scientists and analysts. The role requires working with statistical methods, data visualization, SQL, machine learning frameworks, and classified government data in a fully onsite, defense-related environment.

Own the maintenance, security hardening, multi-tenant provisioning, deployment, and operation of an AWS-hosted AI SaaS platform. The role will also help commercialize the product through packaging, positioning, and go-to-market efforts. Applicants should have independently shipped and operated a real SaaS product and be comfortable working as the sole or near-sole engineer. Compensation is currently revenue share, equity, or another negotiated arrangement without guaranteed cash pay.

Design, build, optimize, and deploy systematic trading models and algorithms. Analyze large datasets to identify market patterns and trading signals, collaborate with traders and technologists to productionize research, and monitor and refine models based on live performance. The role requires advanced quantitative expertise, strong programming skills in Python or C++, and the ability to solve open-ended problems independently.

Hands-on technical role implementing, supporting, and optimizing Microsoft 365 Copilot and Copilot Studio agents. Design and build APIs/connectors, develop Copilot prompts and AI workflows, extend solutions via Power Platform, ensure tenant configuration, security, and compliance, provide operational support and incident response, run workshops and adoption activities, and mentor teams while driving scalable, monitored, production-ready AI integrations.

Conduct applied research in generative video and audio synthesis, voice and roomtone cloning, multimodal understanding, computer vision, and speech modeling. Design, implement, test, and evaluate deep learning algorithms, develop new research directions, and help move prototypes into production features. The role requires end-to-end ownership, strong experimental judgment, and the ability to generate and rapidly evaluate machine learning ideas.