Top AI Engineer Jobs

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.

Design, orchestrate, and optimize large-scale LLM pre-training across 1,000+ GPUs. Implement 3D parallelism, manage GPU clusters (SLURM/Kubernetes), optimize InfiniBand/RDMA networking and memory, and automate checkpointing and failure recovery for long training runs.

Build agentic, reasoning-driven systems on top of LLMs; own full-stack AI product features from prompt design to orchestration, tooling, monitoring, and UX. Prototype with design, run LLM evaluation and A/B tests, integrate emerging methods, and collaborate directly with founders and customers to deliver product-facing AI features.

The Senior AI/ML Engineer will build scalable AI systems, work with LLMs, optimize models, and deploy solutions using AWS while ensuring ethical standards.

Build RoboForce's internal AI-native OS: design, prototype, and productionize agents, automations, workflows, and templates. Identify high-leverage AI opportunities, drive cross-functional adoption, define usage and security standards, partner on infrastructure, and act as technical chief-of-staff. On-site five days weekly.

Build and maintain backend and data pipelines powering ads, personalization, and retention systems. Contribute to recommendation algorithms, feature pipelines, experimentation infrastructure, revenue operations tooling (commission/quota), and observability for large-scale user and engagement data. Collaborate with product, sales, and finance to ship reliable, low-latency production systems and monitoring.

Design, prototype, and integrate generative AI solutions and agents across products. Collaborate with cross-functional teams to deploy, test, and optimize AI workflows, educate developers, and engage with the enterprise systems management community through webinars and content.

Design, build, and operate end-to-end training and inference infrastructure for large language and multimodal models. Improve efficiency (memory, parallelism, kernel optimizations), ensure robust scalable training and RL pipelines, optimize low-latency/high-throughput serving (quantization, caching, speculative decoding), manage multi-GPU and multi-cloud orchestration, and productionize new algorithms with strong observability and reproducibility.

Develop and maintain AI-driven features, design scalable data structures and pipelines, and enhance platform capabilities while collaborating with teams.

Own and evolve the AWS-based platform powering product and AI workloads. Design IaC (Pulumi), build CI/CD pipelines, manage observability, respond to incidents, support LLM workflows (Bedrock, SageMaker), and partner with engineering and ML teams to improve reliability, scalability, and developer experience.

The Senior AI NodeJS Engineer develops AI Agentic solutions, collaborating with teams, managing tasks, ensuring quality code, and staying updated on AI technologies.

Design, build, and operationalize production-grade AI/Machine Learning solutions and MLOps services. Deploy models and API services to AWS and GCP using CI/CD and IaC, implement workflow automation, monitor post-production performance, research generative/ML techniques, and mentor junior engineers while collaborating with cross-functional data, cloud, and architecture teams.

The AI Sales Engineer will manage customer interactions from demo to engagement, utilizing AI expertise to enhance customer experience and drive growth.

Lead the technical architecture and implementation for LangSmith, collaborating with teams, mentoring juniors, and ensuring code quality.

The Full Stack Engineer will develop and implement features for the LangSmith platform, collaborate with teams, ensure high coding standards, and engage with enterprise customers.

The AI Sales Engineer will build relationships with customers, lead product demonstrations, handle objections, and act as a domain expert in ML. Responsibilities include collaborating with sales and technical teams to create value propositions and developing educational materials about ML and MLOps.

As an AI QA & Infrastructure Engineer, you'll design and execute test plans, maintain testing environments, track quality metrics, and improve QA processes for Aidaly's products.

Lead ML/AI analytics across STARZ telemetry and operational data to detect anomalies, forecast trends, and surface actionable signals. Build repeatable signals frameworks, apply anomaly detection/classification/clustering/time-series models, leverage generative AI/LLMs for summarization and RCA, define alerting and escalation, partner with engineering and product, and mentor junior analysts.

The Lead Infrastructure and Reliability Engineer will enhance GPU operations, define scalability strategies, and develop organizational strengths in a high-demand AI infrastructure setting.

As a Software Engineer intern, you'll work on AI-driven projects spanning data management, onboard systems, ML infrastructure, simulations, and technical infrastructure, collaborating with experts in various fields.

Looking for new graduate software engineers to work in areas like Data Platform, Onboard Systems, or Technical Infrastructure focusing on AI and autonomy technologies.

As a foundational Software Engineer at Niva, you'll define AI strategy, develop multi-agent AI platforms, and engage with customers to enhance workflows.

The Software Engineer will maintain and evolve the data platform, run LLM ETL pipelines, transform agent outputs, improve system reliability, and support incident response.

Develop and fine-tune vision-centric large language models and visual encoders, build evaluation benchmarks, collect and prepare visual training data (including for RLHF), prototype architectures for production VLMs, publish research, and mentor junior researchers within a small applied ML research team.

As an AI Product Engineer, you will develop innovative LLM-powered solutions, enhance AI systems, and collaborate with cross-functional teams to improve customer experiences.