Top Machine Learning Engineer Jobs

The AI/ML Engineer designs and operates a production-grade orchestration platform, focusing on multi-agent workflows, LLM integration, and backend systems. Responsibilities include managing cloud-native infrastructure, ensuring system reliability and observability.

The Staff AI/ML Engineer will develop and prototype AI/ML algorithms, analyze datasets, and mentor team members in an interdisciplinary environment.

Design and deploy object detection and tracking modules; develop ML algorithms; guide team members; improve data governance and quality assessment.

Lead the development of AI/ML infrastructure for simulations. Collaborate on realism models, scale distributed systems, and ensure alignment with business goals.

Design and productionize ML models and infrastructure to assess autonomous vehicle behavior in simulation and field. Operate large-scale ML and data systems, build simulation workflows, combine human judgments with ML for training and evaluation, and collaborate across teams to deploy state-of-the-art models and improve model quality.

Designs, trains, evaluates, deploys, and monitors scalable AI/ML models and data pipelines to meet federal customer requirements. Leads model architecture, testing, integration, and iterative improvement while ensuring data and model quality and presenting results to stakeholders.

As a Senior Staff Machine Learning Engineer, you will drive AI product development, collaborate with cross-functional teams, and enhance ML models at scale.

The Senior Staff Machine Learning Engineer will develop and scale ML systems for content classification, moderation, and decision-making, ensuring quality and compliance across Spotify's platform.

Design, develop, and deploy machine learning models and AI platforms. Preprocess and analyze large datasets, implement scalable cloud-based solutions, integrate models into applications, monitor and optimize model performance, document code and processes, and collaborate with cross-functional teams to deliver AI solutions.

The Staff Machine Learning Engineer will lead ML strategy, architect AI infrastructure, mentor engineers, and develop impactful AI systems.

Design and implement agentic systems using LLMs and agent frameworks, build multi-agent workflows and RAG pipelines, optimize scalable agent architectures, collaborate on experiments and feature integration, and ensure quality through reviews, testing, and documentation.

Embed with one or two Autonomy teams to enable large-scale replay/recompute workflows: implement data flows, onboard models, ensure metric evaluation and visualization adoption, debug full-stack issues, document workflows, and translate team feedback into simulation platform improvements.

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.