Top Machine Learning Engineer Jobs

Support development of LLM agents, RAG pipelines, and fine‑tuned models. Contribute to portfolio projects, run experiments and ablations, prepare technical documentation and demos, assist discovery workshops, and help implement evaluation and governance pipelines.

Lead development and optimization of a data‑intensive AI benchmarking stack: build Python-based backend systems and APIs, implement model evaluation and analyses, create interactive data visualizations, run custom enterprise analyses, and help evolve benchmarking methodology.

This role involves building and optimizing ML systems and frameworks, collaborating with teams to enhance research and development in ML models.

Design and scale infrastructure for machine learning models, build pipelines, and optimize for performance in a collaborative remote environment.

As a founding Machine Learning Engineer, you will improve core models, optimize LLM inference, develop data pipelines, and ship features directly to users.

Design and implement high-performance APIs and microservices using Python and AWS, focusing on generative AI and scalable applications.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

Develop and optimize machine learning models for defense and intelligence applications, including computer vision, time-series analysis, embeddings-based change detection, and satellite imagery analytics. Build scalable preprocessing and inference pipelines, integrate models with software platforms, and establish testing, validation, monitoring, and reliability frameworks. Collaborate with product managers, data scientists, and engineers to deploy high-throughput solutions globally.

Build and improve machine learning systems for ad ranking, relevance, and optimization. Responsibilities include developing active learning and LLM-assisted labeling workflows, owning experimentation and evaluation, and deploying low-latency inference services. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while ensuring production reliability, scalability, latency, and data quality. Candidates will use Python, Go, AWS, distributed systems, and AI-assisted development tools.

Train, fine-tune, and deploy vision-language-action and robot manipulation models across the full pipeline. Handle data curation, pre-training and fine-tuning, sim-to-real distillation, debugging, evaluation, and iterative autonomous improvement on physical robots.

The role involves developing machine learning models for neural data, collaborating with teams, and exploring new architectures. The candidate should have applied ML experience and strong programming skills.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

Build production ML and data systems to measure and improve data quality. Develop expert-matching, quality prediction, anomaly detection, and evaluation infrastructure. Convert messy signals into models, metrics, and product improvements while partnering with engineers and domain experts and owning systems from design to deployment.

The role involves designing and scaling ML infrastructure for LLMs, including building training pipelines and deploying models in cloud and on-prem environments. Collaboration with engineers and managing GPU/TPU workloads is key.

The Machine Learning Engineer will collaborate on AI projects, develop algorithms, optimize solutions, and maintain coding standards within Kitware, contributing to various machine learning tasks.

Collaborate on machine learning projects, perform prototyping, validate algorithms, and troubleshoot code for robustness in AI and computer vision.

The Staff ML Engineer will lead architecture decisions for ML infrastructure, operate real-time computer vision systems, and mentor engineers, ensuring high-quality ML deployment and operational excellence at SimpliSafe.

As a Machine Learning Engineer, you'll design and deploy large-scale models for advertising and commerce systems, optimizing for performance and relevance while maintaining ML pipelines.

The Senior Principal Engineer will lead the development of ML systems at SoundCloud, focusing on recommendation and personalization techniques while mentoring engineers and driving strategic direction.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Lead the Scene Generation team to define technical vision and roadmap for neural rendering and generative models. Turn research (NeRF, 3D Gaussian splatting, diffusion/flow models) into production-quality camera/LiDAR/radar sensor simulation, own framework end-to-end, integrate and scale pipelines in cloud/VDI environments, drive cross-team alignment, and mentor/hire engineers.

Train and iterate custom ML models on manufacturing and design data to estimate cost and complexity, generate designs, and extract structured data from engineering documents. Build end-to-end pipelines from data cleaning and labeling through training, evaluation, and deployment. Collaborate with engineering and manufacturing teams to source datasets and identify opportunities to improve workflows with custom models.

The AI/Machine Learning Engineer will design, develop, and deploy AI models for healthcare, collaborating with teams to enhance patient outcomes using advanced data analytics and machine learning techniques.

Lead development and optimization of LLMs and Mixture-of-Experts models, drive research for performance and efficiency (quantization, on-device inference, RAG, agents), integrate models into product, mentor engineers, and deliver cross-functional initiatives.

Develop and implement state-of-the-art synthetic sensor simulation methods (photorealistic images, lidar, radar). Train and evaluate ML models for synthetic data, analyze realism and utility, answer sensor-autonomy questions, and collaborate across autonomy, infrastructure, and systems teams. Role scoped as Senior/Staff IC with potential to grow into Tech Lead.