Top Artificial Intelligence Jobs

Operate and monitor robots in real time using controllers and multi-monitor setups, perform scene resets, follow SOPs, log shift activity, and collaborate with a teammate to maintain continuous robot uptime, safety, and task accuracy.

Lead development of ground and flight software for lunar and deep-space missions, manage full software development lifecycle, and mentor engineers.

The role involves generating high-quality leads through targeted outreach in a hybrid model, collaborating with sales and marketing teams to refine strategies and measure success by pipeline growth.

As a Business Development Representative at Decoda Health, you will manage outbound campaigns, identify clinics, qualify leads, and book meetings. You will work closely with the head of sales, focusing on top-of-funnel sales without any inbound safety net. Proven sales skills and 1-2 years of relevant experience are ideal.

Develop the core systems for multimodal AI models, focusing on building model serving platforms and enhancing developer experience with tools and APIs.

The Senior HPC & GPU Infrastructure Engineer maintains GPU compute clusters, leads system reliability, manages Linux environments, and optimizes ML infrastructure.

The GPU Kernel Engineer will design and optimize GPU kernels for large-scale AI systems, integrating them into ML frameworks and collaborating across teams to enhance performance.

Design and build AI-powered features leveraging LLMs and retrieval systems. Integrate data systems and create applications for sports analytics. Collaborate with cross-functional teams for high-quality product development.

As an MLOps/ML Platform Engineer, you will build and manage ML systems, optimize workloads, and ensure production model reliability while collaborating across teams.

The Sr. Football Data Scientist will build, deploy, and refine analytical models for football insights, managing large datasets and collaborating with teams for high-quality analytics solutions.

The Senior Machine Learning Engineer will develop and maintain algorithms/services for multi-agent AI, deliver cloud-native architecture, and optimize ML models for performance.

The Principal Software Engineer will lead the design and development of scalable AI-native cybersecurity systems, drive technical initiatives, and mentor engineers while collaborating closely with cross-functional teams.

Design, build, and scale the infrastructure for AI-driven security products, improve deployment and operational practices, and enhance developer experience.

The role involves building tools for engineers, improving CI/CD processes, optimizing deploy pipelines, and enhancing developer workflows.

As Operating Partner at Gitwit's Bentonville Studio, you'll lead discovery, shape product strategy, and support Builders in Residence in developing AI-native companies through hands-on collaboration with the Tulsa studio.

The Staff Software Engineer will shape and build new AI-driven products, driving technical direction and collaborating with cross-functional teams. Responsibilities include defining project scopes, making foundational technical decisions, shipping products rapidly, and adapting based on user feedback, all in a dynamic environment focused on venture creation.

The CEO & Co-Founder will build Yask, an AI company for physical operations, focusing on product-market fit, team building, and driving customer adoption through hands-on management and product development.

As a Product Engineer, you'll prototype and build tools that connect AI models with lab systems, collaborating with scientists and researchers to refine products.

Own design, deployment, and iteration of an RL-driven trading agent using real capital. Design reward functions with strict downside risk constraints, build evaluation and simulation frameworks, transition heuristic production systems to learning-based approaches, and serve as sole RL technical lead responsible for end-to-end data modeling, deployment, monitoring, and safety.

Lead development of a production autonomous-agent intelligence layer: implement closed-loop learning from trade outcomes, build evaluation and automated strategy generation, monitor fleet performance, manage risk and capital allocation, own model training/inference pipelines (including LLM hosting/fine-tuning), and build telemetry and data capture for real-time market adaptation.

Lead U.S. financial services go-to-market: own full sales lifecycle, build and qualify pipeline, engage C-level stakeholders, position distributed ledger and tokenization solutions, collaborate with product and engineering, and scale repeatable enterprise sales motions.