Top Artificial Intelligence Jobs

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Lead product strategy and roadmap for enterprise AI coding platforms. Own backlog, vendor selection, integration with IDEs, CI/CD, and repos, ensure security/privacy/governance, drive adoption through enablement and metrics, and partner with cybersecurity, legal, and external AI vendors.

Build and operate infrastructure and platforms for high-traffic live event APIs. Improve deployment, monitoring, and alerting using AI/ML, lead scalability and thundering-herd solutions, run load testing and capacity planning, perform post-incident RCA, mentor engineers, and support live event coverage.

Lead enterprise data and AI strategy, build scalable data platform and governance, enable analytics and GenAI use cases, ensure regulatory compliance (HIPAA/CAP), and build and mentor a high-performing Data & AI organization aligned with technology, genomic, operations, and commercial teams.

Lead SimpliSafe's AI organization to define and execute company-wide AI strategy, own AI product roadmap, build cloud and on-device ML/CV platform, manage budget and hiring, partner with Product and Engineering, and translate cutting-edge research into consumer AI features for constrained devices like cameras.

Design, train, and deploy ML solutions with emphasis on generative AI and NLP (LLMs, RAG). Work with cross-functional teams to analyze text/transcription data, build scalable models, evaluate new algorithms, document results, and present findings to stakeholders to drive business value.

Lead development, implementation, and maintenance of forecasting and ML solutions for commercial demand. Analyze large datasets, monitor model performance, document workflows, support experimentation, and communicate results to cross-functional stakeholders while promoting best practices and adoption.

Develop intent-recognition models from gaze, gesture, and voice data; implement socially aware navigation that respects personal space; and design visual and auditory feedback for robot user interfaces. Run user studies and rapidly prototype AI-driven voice and visual interfaces.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agent microservices on Kubernetes; and create observability dashboards tracking token usage, latency, and agent reasoning.

Design and integrate vision and audio models into agent reasoning loops, optimize low-latency streaming voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from video and document sources.

Design and integrate vision and audio models into agent reasoning loops, optimize streaming latency for voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from videos and PDFs.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable microservices on Kubernetes; and create observability dashboards tracking token usage, latency, and agent reasoning.

Develop intent-recognition models from gaze, gesture, and voice data; implement socially aware navigation algorithms respecting personal space; and design visual and auditory feedback for robot user interfaces. Conduct user studies and rapid prototype AI-driven voice and visual interaction systems to test and refine HRI paradigms.

Develop multimodal intent-recognition models from gaze, gesture, and voice; implement socially aware navigation respecting personal space; design robot visual and auditory UI feedback; run user studies and rapidly prototype AI-driven voice and visual interfaces.

Design and implement AI platforms (front-end and back-end), collaborate on data preprocessing and model training, deploy AI models to production, build and maintain data pipelines, integrate AI with an Ethereum Layer 2/Web3 stack, and contribute to related company projects with potential to lead a team.

Independent full-cycle recruiter managing sourcing, client engagement, offer negotiation, and placements using AI-driven tools. Acts as commercial advisor, provides market insights, and maintains self-directed billing and activity in a results-only, remote environment.

Perform adversarial testing on language models and multimodal agents, implement guardrails and real-time filters for autonomous tools, and develop constitutional AI principles and RLHF alignment pipelines to ensure safe AI deployment.

Design and build multi-agent orchestration frameworks, implement tool-calling pipelines and reasoning loops, and optimize latency and token usage for real-time agentic workflows.

Design and implement HRI systems including intent-recognition from gaze/gesture/voice, socially aware navigation, and robot visual/auditory feedback. Conduct user studies and rapidly prototype AI-driven voice and visual interfaces to evaluate interaction paradigms.

Design and run adversarial tests on language and multimodal models, build guardrails and real-time filters for autonomous tools, and develop constitutional AI principles and RLHF alignment pipelines to ensure safe AI deployment.

Manage model registries, continuous training loops, and A/B testing infrastructure. Deploy agentic models as scalable microservices on Kubernetes and build observability dashboards to track token usage, latency, and agent reasoning paths. Maintain MLOps pipelines and ensure scalable, observable AI infrastructure.

Operate and optimize large-scale LLM pre-training on 1,000+ GPU clusters using PyTorch, DeepSpeed, or Megatron-LM. Improve networking (InfiniBand/RDMA), memory management, checkpointing, and failure recovery. Manage SLURM/Kubernetes GPU clusters and apply systems engineering (C++, CUDA, Python) and 3D parallelism techniques.

Design and integrate vision and audio models into agent reasoning loops, optimize low-latency voice-to-voice streaming, and architect multimodal RAG systems for retrieving insights from videos and PDFs.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable Kubernetes microservices; and create observability dashboards tracking token usage, latency, and agent reasoning.

Optimize and compress large language and vision models for on-device inference. Build distillation and hardware-specific compilation pipelines, benchmark performance across NPU/GPU architectures, and deploy models to edge environments.