Top Machine Learning Engineer Jobs

Conduct an open-ended machine learning research project tied to Lyft products. Responsibilities include developing and evaluating reinforcement learning, personalization, and sequential decision-making models; building production-quality ML pipelines; analyzing experiments; collaborating with product and technical teams; communicating findings; and preparing research for publication.

Own the architecture, deployment, performance, and reliability of Stickerbox’s production machine learning pipeline. Optimize latency, cost, batching, caching, GPU utilization, model serving, and inference across image generation, speech recognition, and character models. Lead deployment strategies, capacity planning, observability, safety enforcement, automated quality checks, migrations, release engineering, and on-call operations for a global consumer hardware launch.

Develop and integrate computer vision and machine learning algorithms for autonomous agricultural and off-highway equipment. Responsibilities include geometric computer vision, SLAM, localization, mapping, triangulation, depth estimation, camera calibration, data collection and processing, model development, training, optimization, deployment, and monitoring. The role uses cameras, LiDAR, radar, edge devices, robotics, cloud technologies, C++, and Python while collaborating onsite with engineering teams.

Advance learning-based 3D reconstruction for photorealistic simulation environments using Gaussian Splatting, NeRFs, and related computer vision techniques. Develop dynamic scene reconstruction, novel view synthesis, multi-sensor rendering, scalable feed-forward models, evaluation benchmarks, and production-quality ML systems. Collaborate with research, infrastructure, autonomy, and product teams while solving customer challenges and influencing architecture.

Develop and productionize diffusion and video generation models for realistic, controllable autonomous-driving simulation. Build multi-camera generation, scenario augmentation, neural reconstruction, scalable training and inference, and evaluation benchmarks. Collaborate with research, infrastructure, autonomy, product, and customers to deliver generative simulation capabilities and influence technical architecture.

Design, develop, test, debug, and maintain scalable software applications and prototypes using Java and Python. Work in a Linux full-stack environment, integrate customer-tailored large language models, design accessible interfaces, connect external requirements tools, troubleshoot performance issues, conduct code reviews, support planning, and mentor junior engineers. The role requires extensive software engineering experience, a bachelor’s degree or equivalent experience, DoD8570 IAT2 compliance, and TS/SCI clearance with polygraph.

Lead research, design, and implementation of machine learning algorithms for computer vision, multimodal data, generative AI, and robotics in surgical platforms. Develop and evaluate vision-action models, datasets, validation methods, and scalable pipelines; integrate algorithms into robotic systems; advance research through patents and publications; and collaborate with engineers, clinicians, users, and academic partners to develop AI technologies for robotic-assisted surgery.

Designs, builds, migrates, and operates AWS workloads and platform infrastructure. Responsibilities include cloud architecture, infrastructure as code, security, cost optimization, environment provisioning, lifecycle management, DevOps and CI/CD, container platforms, and AI foundation enablement. The role uses Terraform, CloudFormation, AWS CDK, Python, Docker, Kubernetes, and numerous AWS services while applying the AWS Well-Architected Framework.

Lead technical direction and people for an AI/ML engineering squad to design, build, and deploy production-grade ML and Generative AI (LLMs, RAG, agentic systems). Drive MLOps, CI/CD, model monitoring, Responsible AI compliance (HIPAA/PHI), architecture reviews, and mentor 5–10 engineers to deliver secure, production solutions.

Build and improve enterprise conversational search and question-answering systems using IR and generative AI. Implement retrieval, matching, ranking, and summarization across heterogeneous knowledge sources, develop LLM-enabled features, and create evaluation metrics addressing enterprise constraints like security, privacy, and scalability.

Improve enterprise search relevance for a question-answering platform by crafting and optimizing ranking features, building algorithm frameworks, enabling parallel ranking experiments with tooling, analyzing query logs, and developing metrics to evaluate and boost answer quality across heterogeneous knowledge sources.

Designs, develops, and deploys enterprise Generative AI solutions using LLMs, semantic search, and AI orchestration. Builds monitoring systems, improves AI architecture, supports prompt engineering and evaluation, reviews code, and mentors engineers. Collaborates with product leaders and stakeholders to deliver secure, scalable, customer-facing AI applications. Requires Azure OpenAI, Azure AI Search, Semantic Kernel, DevOps, CI/CD, testing, and advanced Python expertise.

Leads the architecture, development, and optimization of production-grade machine learning infrastructure on Azure. Designs scalable training and inference environments, implements MLOps practices, automates ML workflows, and establishes reusable deployment frameworks. Partners with data science, DevOps, and platform engineering teams to productionize models, strengthen security and observability, and ensure reliable, compliant systems. Mentors junior engineers and drives ML infrastructure strategy across multiple use cases.

Design, build, and operate production-grade GenAI and RAG platforms at scale. Develop retrieval pipelines, evaluation frameworks, lifecycle management, CI/CD, observability, safety controls, cost monitoring, and incident workflows. Partner with product, platform, and governance teams to meet enterprise reliability, security, and compliance standards. Mentor engineers and establish best practices for scalable, trustworthy AI systems.

Design and scale semantic data and AI infrastructure, including embedding pipelines, knowledge graphs, ontologies, vector and hybrid retrieval, RAG systems, and LLM-powered applications. Build production-grade APIs and data pipelines, optimize private GPU inference, establish evaluation and reliability frameworks, and ensure explainability, attribution, and safe outputs. Collaborate across product, analytics, data engineering, and ML teams while mentoring engineers and translating business needs into grounded AI systems.

Design, build, and productionize core AI systems for document understanding, forecasting, and agentic workflows. Handle messy data ingestion, evaluation, monitoring, anomaly detection, entity reconciliation, and backtesting to deliver reliable, product-focused ML solutions.

Lead development of an AI-ready research data ecosystem for vaccine R&D: design integrated data architectures, automate ingestion and transformation pipelines, define semantic/metadata frameworks, build knowledge graphs and RAG systems, and develop multimodal predictive and translational ML models. Provide technical leadership, mentor teams, and collaborate across scientists, bioinformatics, and enterprise data organizations to accelerate AI-driven vaccine discovery and decision-making.

Founding ML engineering hire to design and build production ML systems: training and inference pipelines, serving, feature workflows, monitoring, retraining, governance, and operational practices. Partner across engineering, data, product, and applied science to productionize models, implement observability, prevent degradation, and set platform standards and hiring bar for future ML engineers.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Build reliable machine learning and data systems that transform assay videos, scientific data, and experimental outcomes into reproducible models, evaluated predictions, and experiment recommendations. Responsibilities include data and feature pipelines, model training and evaluation infrastructure, batch and online inference systems, monitoring, data quality, versioning, observability, and interfaces across computer vision, molecular modeling, active learning, and laboratory workflows.

Develop and optimize real-time bidding strategies, online auction mechanics, and machine learning models for advertising campaigns. Responsibilities include event prediction, feature engineering, user embeddings, clustering, fraud detection, cross-device identification, data mining, and production support. The role requires advanced Python, strong algorithms and statistics knowledge, machine learning expertise, and substantial RTB or comparable online technology experience.

Provides AI/ML systems engineering and technical advisory support to a U.S. Government program office. Responsibilities include acquisition planning, source selection, AI/ML technology and architecture evaluation, Agile SAFe program activities, alternatives analysis, lifecycle systems engineering, risk and schedule management, technical assessments, and executive briefings. The role supports space and ground systems, vendor evaluations, research and development, integration, testing, deployment, and coordination across government stakeholders.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Design, implement, and deploy ML models for autonomous driving (detection, tracking, trajectory generation). Apply advanced techniques (imitation learning, RL, foundation models), collaborate cross-functionally, and support production ML systems and model deployment.

Leads the design and development of large-scale search and discovery relevance systems, including machine learning infrastructure, serving, pipelines, and AI products. Drives technical strategy, execution, operational excellence, and business impact while partnering with senior stakeholders. Explores deep learning and large language model innovations, improves search quality, resolves complex technical challenges, and recruits and develops high-performing engineering talent.