Top AI & Machine Learning Jobs

Build and own agent orchestration, inference serving, APIs, and internal tools that turn model capabilities into multi-step workflows. Implement skills/tools for automated and interactive workflows, optimize inference for latency and throughput, integrate models into products, and establish engineering practices like testing, CI/CD, and observability.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

Architect and ship production AI systems and agent workflows with strong focus on explainability, auditability, and compliance for financial services. Build Python/FastAPI tooling, distributed event-driven backends, evaluation and monitoring pipelines, and occasional React/TypeScript UIs. Drive schema, API, and system architecture decisions and establish validation and quality-assurance patterns for AI outputs.

Embed with enterprise customers to spec, implement, and ship production integrations that turn large enterprise APIs into agent-usable tool surfaces. Build eval harnesses, QA tooling, per-customer SDKs/patches, run customer syncs, and operate tooling as self-service while contributing code to the monorepo and improving agent-tool interfaces.

Design and lead end-to-end AI solution architectures, ensuring scalability, security, and compliance. Translate business and regulatory requirements into technical designs, evaluate tools/platforms, document architecture standards, coordinate technical teams, and support deployment and ongoing improvements.

As an AI Research Engineer, you'll conduct research in AI and automated reasoning, creating innovative solutions, testing systems, and contributing to scientific publications while collaborating with engineering teams.

Lead technical sales and implementations for Nitra's AI platform: drive technical discovery, design, demos, POCs, integrations, and go-live. Ensure customer adoption, collect feedback to influence product roadmap, build scalable implementation playbooks, and troubleshoot complex issues alongside Engineering.

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.

Design, build, and deploy agentic multi-agent AI systems and RAG pipelines for document intelligence and procurement workflows. Integrate and evaluate LLMs, implement orchestration frameworks, build human-in-the-loop approvals, and collaborate on production deployment, observability, and secure architectures for government acquisition environments.

Design, build, and ship production multi-agent LLM systems including orchestration, memory/retrieval, safe write-back to customer systems, evaluations and regression tests, adversarial hardening, LLM model-agnostic integration, and cost/latency/reliability ownership at enterprise scale.