Top Machine Learning Jobs in Miami, FL

Lead development and optimization of the compiler stack for ML systems, collaborate across teams, integrate and deploy products, map ML operations to hardware, and drive compiler infrastructure innovation and performance debugging.

The Solutions Engineer will drive the adoption of AI-powered solutions in the electric utility sector, providing technical consulting and customer enablement. Responsibilities include gathering requirements, architecting solutions, leading RFI/RFP processes, and delivering training. Extensive travel is required for client engagement.

You will build and own end-to-end ML systems, focusing on training, evaluation, deployment, and integration with applications, while ensuring quality and efficiency.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

The VP of Research, Machine Learning will set research direction for AI systems, focusing on context representation, reasoning, and evaluation frameworks while ensuring product safety and alignment.

Responsible for building and maintaining end-to-end ML pipelines and deploying production-grade ML systems. Tasks include model fine-tuning, evaluation, and collaboration with engineering teams.

The role involves developing and deploying machine learning models for forecasting within the Camus platform, requiring collaboration and technical depth in ML.

Design, develop, and deploy end-to-end AI/ML applications (RAG, chatbots, agentic workflows). Build scalable data pipelines, apply graph analytics, entity resolution, and NLP for risk scoring, anomaly detection, and investigative prioritization. Develop models, REST APIs, and deploy/manage solutions in AWS to support federal mission use cases.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

Seeking a Senior Machine Learning Engineer to design and implement ML systems, optimize deployment, and collaborate with data teams, focusing on scalability and performance.

As a Staff Machine Learning Engineer - AI Tech Lead, you will lead the design of AI systems for security operations, focusing on scalable architectures, AI safety, and mentoring. Responsibilities include overseeing technical evaluations, production workflows, LLM fine-tuning, and collaboration with teams to enhance AI capabilities.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Define and execute predictive modeling and ML strategy for credit risk, servicing, and collections. Lead cross-functional initiatives, oversee model development, deployment, and monitoring, advise senior leadership on data strategy, represent analytics in regulatory and audit forums, and mentor peers while fostering organizational partnerships.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

The Senior ML Engineer will develop and deploy full-stack AI solutions, manage LLM integration, and collaborate closely with defense clients to enhance real-world applications in secure environments.

As a Senior MLOps Engineer, you will manage production ML systems, define lifecycle strategies, optimize ML pipelines, and collaborate with cross-functional teams to enhance ML operations.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

Lead technical direction and hands-on development of ML and AI Context systems for HubSpot CRM: model development, productionization, retrieval, embeddings, evaluation, monitoring, and cross-team architectural leadership to deliver measurable product and business impact.

The role involves defining and refining Guidewire's AI narrative, crafting audience-specific messages, leading competitive positioning, and creating technical marketing assets to convey the value of AI-driven solutions in the P&C insurance sector.

Lead and grow a senior machine learning and causal inference research team; define long-term research strategy; balance foundational and applied projects; partner cross-functionally to translate research into product impact; build external academic collaborations, publications, and a high-performing research program.

Design, develop, and scale software systems for machine learning algorithms powering personalization. Collaborate with researchers and product teams to implement production-ready, distributed solutions, enable offline experiments and A/B tests, and improve engineering practices and systems across teams.

Lead ML initiatives to improve Sales productivity and automation: define roadmap, design and deploy production ML models and AI agents, build measurement pipelines with analytics engineers, iterate from performance and seller feedback, and act as senior technical partner across product, engineering, and Sales to drive measurable revenue impact.

Lead ML technical strategy for Live Ads, designing and deploying forecasting, targeting, bidding, auction, and yield-optimization models. Build and evaluate large-scale production ML systems, create online/offline evaluation frameworks, partner with product to set objectives and trade-offs, and communicate results to stakeholders and leadership.

Design and build scalable training and inference systems for LLMs and multimodal/media models. Optimize end-to-end training (data pipelines, distributed training, mixed precision) and inference (KV cache, batching, quantization). Scale models into production, lead efficiency initiatives, and mentor engineers and scientists to improve latency, scalability, and reliability.