Top AI & Machine Learning Jobs

Design, develop, and optimize agentic, LLM-powered applications and autonomous systems. Build scalable data services, backend APIs, microservices, workflow orchestration, integrations, and cloud-native deployments using Python and AWS. Implement observability, monitoring, and operational tooling while evaluating emerging technologies. Collaborate across teams, participate in Agile ceremonies, conduct peer reviews, and establish engineering best practices.

Own the architecture and evolution of W&B ML workflow systems, including backend services, APIs, SDKs, and frontend components. Design scalable systems, lead live-traffic migrations, resolve complex cross-service issues, reduce technical debt, and improve reliability. Collaborate with product, design, customers, and ML platform teams to deliver workflow solutions. Mentor engineers and establish scalable engineering practices across distributed systems, data platforms, APIs, event-driven infrastructure, or full-stack development.

Build and improve ML platform tooling, infrastructure, workflows, and observability for machine learning models, LLMs, and agentic applications. Collaborate with ML and infrastructure teams, evaluate and integrate open-source tools, automate fine-tuning and evaluation pipelines, and debug distributed systems across infrastructure, networking, and application layers.

Build and improve Kensho’s ML platform, tooling, infrastructure, and workflows. The role supports ML engineers by developing scalable, auditable processes for model fine-tuning, reinforcement learning, LLM and agent evaluation, and production observability. Responsibilities include integrating open-source solutions, debugging distributed systems, monitoring model and agent performance, and promoting engineering best practices across teams. The position requires collaboration across ML, infrastructure, SRE, and security teams.

Build data products and automations by translating business questions into technical requirements, assessing data and process gaps, and developing analytics solutions. Create actionable Power BI dashboards, support cross-functional decision-making, and guide junior data scientists and citizen developers. The role requires strong skills in Python, SQL, data analytics, AI, automation, data modeling, business requirements analysis, communication, and project management.

Defines enterprise data and AI infrastructure architecture for physical AI and autonomous systems. Designs data domains, canonical models, data products, GPU platforms, distributed training environments, simulation pipelines, MLOps workflows, and edge deployment architectures. Leads technology evaluation, technical standards, implementation planning, and cross-functional delivery across cloud and on-premises environments. Enables engineers and data scientists to develop, validate, and deploy scalable AI models for autonomous construction equipment.

Leads a multidisciplinary data science and AI pod focused on AI-assisted engineering tools, adoption, analytics, and productivity measurement. Develops technical plans, guides deployment and troubleshooting, supports Agile product ownership, reports actionable insights, and mentors data science, machine learning, GenAI, and software engineering professionals. Partners with users, product owners, architects, engineers, and business stakeholders to shape scalable enterprise AI enablement services while applying responsible AI principles.

Design, deploy, optimize, and scale production AI and agentic workloads on DigitalOcean’s cloud. Build inference benchmarks, automation tooling, deployment frameworks, operational playbooks, and reference architectures. Validate AI platform capabilities, optimize distributed inference and GPU performance, support customer migrations, and collaborate with product, research, infrastructure, GPU, model, and ecosystem partners. Enable technical teams and strategic customers while influencing platform roadmaps and accelerating production adoption.

Deploys, optimizes, and scales production AI and agentic workloads for strategic customers on DigitalOcean’s AI-Native Cloud. Builds inference, runtime, orchestration, benchmarking, automation, and deployment frameworks; tunes GPU and distributed-system performance; validates new platform capabilities; and develops reusable reference architectures and operational playbooks. The role also partners with customers, vendors, model providers, and internal engineering teams to accelerate production adoption and platform maturity.

Design, deploy, optimize, and scale production AI and agentic workloads for strategic customers. Build benchmarking systems, automation tooling, deployment frameworks, starter kits, and operational playbooks. Optimize distributed inference, GPU efficiency, latency, reliability, and cost across AI runtimes and orchestration platforms. Validate new platform capabilities, influence product roadmaps, collaborate with infrastructure and model partners, and enable customer-facing technical teams. The role requires up to 30% travel for customer engagements, workshops, conferences, and collaboration.

Design and implement production multi-agent AI systems for marketing compliance. Build ML pipelines, fine-tune models, develop prompt and evaluation frameworks, translate compliance rules into agent logic, and create monitoring, explainability, and feedback-loop infrastructure. Collaborate with engineering, product, implementation, and compliance teams to integrate AI features and improve customer-specific recommendations.

Design, train, adapt, and evaluate reasoning and cognition models for unmanned autonomous systems. Develop multimodal, embodied, and traditional AI/ML capabilities that operate under edge constraints, incomplete information, intermittent communications, and large multi-agent deployments. Establish reproducible training workflows, benchmarks, simulation and real-world evaluations, and model feedback loops. Partner with perception, MLOps, simulation, autonomy, hardware, and inference teams to deliver reliable models and communicate technical tradeoffs and performance evidence.

Leads a team developing reasoning and cognition models for unmanned autonomous systems across multiple domains. Defines model roadmaps, training methods, datasets, evaluations, and deployment strategies for edge environments. Oversees research, model adaptation, simulation and real-world testing, multi-agent coordination, and performance optimization. Recruits and develops AI/ML engineers while communicating technical tradeoffs and delivering production-ready models to partner teams.

Own and build Confido's ML platform: design and operate end-to-end ML pipelines, infrastructure-as-code and CI/CD, optimize training/inference and agent workloads for latency/cost/throughput, integrate model data flows with data engineering, and ensure reliability, observability, security, and production model quality with online evals and human-in-the-loop review.

The AI Application Engineer will design and deploy AI solutions, integrate them into cloud services, and collaborate with teams to enhance operational workflows.

Architect and develop production-grade GenAI systems, including LLM-powered agents, RAG pipelines, tool integrations, multi-agent coordination, memory systems, and chatbot experiences. Lead AI productization across engineering, product, and research teams; establish architecture, infrastructure, observability, coding standards, and delivery practices. Mentor engineers, guide technical roadmaps, and ensure scalable end-to-end deployment across frontend, backend, and ML components.