Top Machine Learning Engineer Jobs

Design and optimize on-vehicle perception inference: allocate CPU/GPU resources, compress and quantize large multimodal models, build TensorRT model conversion pipelines, and deliver production-quality low-latency C++/CUDA inference for power- and thermal-constrained vehicle SoCs.

Build software tools and infrastructure for Atlas humanoid robot behavior development, spanning data pipelines, ML training, experiment evaluation, simulation, diagnostics, dashboards, and deployment. Analyze robot-run failures, improve policy and data visibility, support hardware and simulation environments, and maintain reliable testing and CI/CD workflows. Collaborate with research scientists, ML engineers, data operations teams, and robot demonstrators to accelerate the collect-train-evaluate cycle.

Build and scale machine learning infrastructure, training systems, inference workflows, and agentic AI tools for protein design research. Improve distributed training performance, GPU utilization, reliability, and scalability while partnering with AI scientists and protein engineers. Translate research prototypes into reusable production systems, contribute to technical direction, evaluate emerging ML tools, and support scientific communication.

Develop and deploy AI-enabled features, including LLM and RAG workflows, for ontology-based intelligence applications. Integrate data streams, migrate cloud platforms across AWS, OpenShift, and Kubernetes, and build secure, scalable software using Python, Java, and Scala. Participate in Agile/Scrum delivery, DevSecOps, testing, CI/CD, code hardening, and vulnerability remediation. Collaborate with infrastructure, security, and mission teams while supporting complex network deployments and mentoring junior developers.

Leads architecture and development of production-grade mapping and localization ML systems for autonomous vehicles. Designs neural models for detection, segmentation, tracking, depth estimation, 3D reconstruction, and semantic mapping; owns data strategy, evaluation, deployment, and fleet validation. Mentors engineers, drives technical roadmaps, collaborates cross-functionally, and advances vectorized HD mapping, foundation models, and real-time optimization.

Leads the technical direction of an autonomous vehicle perception stack. Responsibilities include architecting scalable perception systems, developing scene detection and runtime activation, improving robustness against sensor degradation and adverse weather, integrating multimodal models, managing ML and data pipelines, monitoring field performance, leading testing and feature delivery, and mentoring engineers. Requires strong robotics, computer vision, machine learning, real-time systems, software architecture, and team leadership experience.

Lead design and delivery of production-grade ML and Generative AI platforms. Define technical vision, own full SDLC, architect scalable distributed ML systems, partner with cross-functional teams to integrate and operationalize models (RAG, agents, LLM fine-tuning) at scale.

Design, build, and deploy scalable ML systems across cloud and edge devices. Prototype research into products, implement training/evaluation/deployment pipelines, optimize large models, and ensure secure, privacy-preserving production ML infrastructure while collaborating with scientists and product teams.

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.

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 role involves developing scalable ML algorithms, optimizing LLM inference performance, managing model serving pipelines, and collaborating with data engineers for efficient knowledge graph generation.

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.

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.

Design, train, and deploy custom deep learning models for radar and camera (EO/IR) semantic segmentation, detection, and temporal tracking. Manage dataset design, collection, synthetic augmentation, and ML-Ops pipelines. Optimize models for resource-constrained edge hardware and collaborate with fusion/autonomy engineers for integration into multi-target tracking and SLAM systems.

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.

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.

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.

Design, build, and own Quince's end-to-end ML platform (training, serving, feature pipelines, monitoring). Create developer-facing MLOps tooling, select core components, optimize GPU and cloud costs, ensure scalable, reliable production deployments, lead RCAs, and mentor engineering teams to raise platform and operational standards.

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

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.

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.

Research and build large-scale neural reconstruction and generative models to complete and synthesize photorealistic 3D spaces from sparse/noisy captures. Design and optimize training and inference pipelines for multi-view, point cloud, mesh, and video data, translate prototypes into production systems, define evaluation benchmarks, and collaborate with product and engineering teams to integrate models into Matterport's digital-twin platform. Contribute to publications, patents, and the strategic roadmap.

Design, build, and deploy AI/ML capabilities (LLMs, RAG, embeddings, vector search) into secure cloud environments to accelerate cyber analyst workflows. Integrate models with APIs, monitor performance and drift, support retraining, perform experiments/AoAs, and provide stakeholder training and operational guidance in government settings.

Research and develop machine learning algorithms and software prototypes for sponsor problems. Perform data collection, modeling, fusion, visualization, statistical analysis, and present technical reports. Collaborate with staff, supervise junior personnel and students, and integrate ML prototypes into research and pre-production environments.

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