Top AI & Machine Learning Jobs

Design, implement, and deploy production-scale machine learning models to evaluate disputes, chargebacks, and fraud likelihood. Build and manage orchestration layers for multiple models, collaborate with stakeholders to integrate partner/customer data, and improve automated and supervised decision-making for a product-focused ML environment.

Build verification and evaluation systems that allow AI agents to determine when work is complete. Develop testing environments, convert failures and human feedback into evaluation signals, improve agent context and memory delivery, and operate the agent fleet as a production system. The role spans application code, infrastructure, testing, measurement, and ambiguous problem-solving. Prior voice, model training, or product-domain experience is not required.

Develop computer-vision methods for detecting and tracking insects in behavioral-assay videos. Build labeled datasets, evaluation metrics, video-processing pipelines, confidence estimates, visual overlays, and quality-control tools. Derive interpretable behavioral measurements and collaborate with entomologists, laboratory teams, and software engineers to improve imaging standards, annotations, and scientific reliability.

Build and deploy AI agents, intelligent applications, scalable backend services, APIs, and full-stack solutions. Integrate large language models, AI services, data platforms, and external APIs into customer-focused products. Collaborate with engineering, product, and cross-functional teams while contributing to cloud-native applications, data pipelines, distributed systems, testing, deployment, and production operations.

Build and scale AI-powered GTM tools, automations, agents, and workflows for Sales, Marketing, Customer Success, and RevOps. Own automation products from discovery through adoption while measuring efficiency and pipeline impact. Develop and optimize automated outbound programs for North America, testing targeting, messaging, and GTM strategies with sales teams. Translate manual processes into scalable systems and bridge technical and non-technical stakeholders.

Develop spatial AI and perception systems for autonomous mobile robots operating in dynamic warehouses and yards. Train and evaluate models using real multimodal fleet data, improve 3D scene representations and state estimation, and develop evaluation methods. Take research concepts from literature through on-vehicle validation and productionization, collaborating with software and test engineering on data, annotation, calibration, monitoring, and simulation infrastructure.

Leads AI presales strategy, customer advisory, technical solution architecture, and team development. Translates business needs into AI use cases, prototypes, business cases, roadmaps, and repeatable offerings. Partners with sales, engineering, consultants, and technology vendors to support complex pursuits, executive briefings, demonstrations, proofs of concept, and AI business growth. Promotes responsible AI governance, tracks pipeline performance, develops thought leadership, and remains hands-on in high-impact customer engagements.

Leads firmwide adoption of AI tools in partnership with attorneys and paralegals. Translates legal workflows into scalable AI solutions, manages platform configuration, supports AI governance and risk practices, and develops communications and training programs. The role requires hands-on AI platform expertise, strong project management, legal workflow knowledge, stakeholder collaboration, and the ability to identify and address risks in AI-enabled legal work.

Develops real-time computer vision, image-processing, tracking, and state-estimation algorithms for directed-energy defense systems. Responsibilities include integrating vision pipelines with sensors, gimbals, and beam-steering hardware; optimizing CUDA and TensorRT workloads on NVIDIA Jetson platforms; processing EO/IR imagery; building simulation and playback tools; and supporting modeling, field testing, calibration, and algorithm validation.

Develop software for semiconductor equipment control, robotics, motion hardware, computer vision, and machine learning systems. Responsibilities include requirements and design specifications, C#/C++ implementation, unit and system testing, software integration, documentation, customer support, and cross-functional collaboration. The role develops image-processing algorithms and machine-learning models for object detection, tracking, segmentation, and depth estimation, while supporting Linux, APIs, databases, and containerized applications.