Top Machine Learning Engineer Jobs

Design and implement scalable AI/ML-driven HealthTech microservices, contribute to technical architecture, write high-quality code, perform code reviews, develop proofs-of-concept, troubleshoot production issues, and document work while collaborating in agile teams.

Lead end-to-end optimization of LLM/VLM inference and training infrastructure: deploy and benchmark inference stacks, diagnose performance regressions, implement quantization and compression workflows, improve latency/throughput/GPU utilization and cost per token, and collaborate with kernel, platform, research, and customer teams to productionize solutions.

Join Hiring.Cafe as a Founding Machine Learning/AI Search Engineer to develop AI recommender systems and scalable infrastructure, contributing to a transformative job search engine.

The Staff ML/AI Platform Engineer will design, prototype and deploy AI/ML models, optimize pipelines, mentor junior engineers, and collaborate closely with other teams to develop machine learning solutions.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

The Cloud and ML Infrastructure Engineer will maintain complex systems, manage AWS production infrastructure for ML/AI workloads, and design monitoring systems for algorithms.

This role involves building ML infrastructure for monetization, including developing data pipelines, model training platforms, and real-time serving systems, while ensuring performance and reliability.

As a Machine Learning Engineer at FanDuel, you'll develop scalable ML applications, enhance user experience through intelligent systems, and collaborate across teams to validate AI insights.

The Machine Learning Engineer will design and implement search systems, develop scalable serving systems for ML, and collaborate with teams to productionize AI insights, requiring engagement with big data and machine learning frameworks.

Design and implement AI solutions to automate workflows, enhance decision-making, and ensure security in sensitive environments, requiring deep technical expertise.

The role involves building and managing data infrastructure and pipelines for machine learning, automating model retuning, and ensuring efficient data flow from collection to model training.

The Senior ML Software Engineer will create and improve capabilities for mapping data using machine learning, collaborating with engineers and data scientists, developing models, and writing production code.

Design, develop, and maintain large-scale distributed systems and optimize the ML model delivery pipeline. Collaborate with research and backend teams to improve model training, serving, performance, and scalability.

As an Autonomy Engineer, you will design and implement scalable data pipelines, manage ML workflows, and improve AI training infrastructure for drones.

The Software Engineer II, Machine Learning will productionize ML models, build tools for ML democratization, and maintain current platforms.

The Senior Machine Learning Engineer will design and build distributed systems for personalized content recommendations, ensuring deployment and optimization of ML platforms.

Lead the design and optimization of batch and streaming data pipelines, ensuring high-quality data delivery for AI and analytics, while mentoring and promoting engineering best practices.

Lead end-to-end silicon diagnostic pipeline using AI/ML to analyze scan failures and process data, build models for failure classification and predictive yield, orchestrate AI-based EDA tools, and drive cross-functional root-cause analysis to improve yield and time-to-market.

Design and implement real-time lane-fusion algorithms that combine multi-source perception and temporal data to produce stable 3D lane and road representations. Research solutions for occlusion, fragmentation, forks/merges, and construction zones. Build a 3D laneline pipeline with camera perception, optimize for low latency and high availability on safety-critical vehicle platforms, and collaborate with cross-functional teams to deploy on target systems.

Lead the Snowflake Machine Learning Platform development, ensuring operational excellence and collaborating on innovative ML solutions to enhance customer capabilities.

Develop and deploy perception ML models for autonomous vehicles (detection, sensor fusion, tracking, classification, intent). Own end-to-end lifecycle: POC, model iteration, onboard deployment, monitoring, and optimization for latency and scale.

Lead and own the ML/AI function: architect multi-modal agentic systems, define evaluation frameworks and datasets, productionize reliable scalable ML, ensure compliance, and build and mentor a high-performing ML team.

Build and ship full-stack features for a production AI/LLM platform: implement polished React UIs from specs, own database and access-control/security, integrate multi-provider LLMs (RAG, agentic workflows), and formalize QA by extending test suites with integration and end-to-end coverage.

Design, develop, test, and support scalable AI-enabled applications and services. Implement LLM, Generative AI, and RAG capabilities, build RESTful APIs and microservices, automate CI/CD and testing, perform code reviews, troubleshoot production issues, and collaborate with product and architecture teams in Agile delivery.

Develop and productionize AI/ML systems across the full lifecycle: design agentic workflows, integrate models into applications, implement multi-agent orchestration, build data pipelines, write scalable code, monitor and optimize production AI, and collaborate with cross-functional teams.