Top Machine Learning Engineer Jobs

As a Founding ML Engineer, you'll develop ML models for lead optimization, automate demand generation processes, build data pipelines, and collaborate with teams to enhance growth through AI solutions.

The Founding ML Research Engineer will design and evaluate ML models, develop experimentation pipelines, and contribute to AI research while optimizing training processes.

The role involves researching, developing, and deploying deep learning models, optimizing machine learning lifecycles, and collaborating with teams to enhance manufacturing applications.

Lead technical vision and development of large-scale, low-latency DNN models for ads conversion. Mine text, visual, and user signals, use LLMs and AI tools to accelerate analysis, automate workflows, and mentor engineers while collaborating on new ad products.

Lead design and delivery of cloud-based pipelines that convert multi-sensor AV logs into VLM/VLA training datasets. Build VLM-assisted auto-labeling, generate language-grounded reasoning labels, mine rare scenarios, define dataset schemas and quality metrics, and operationalize continuous dataset delivery. Mentor engineers and partner with model teams to improve downstream model performance using distributed compute and scalable MLOps.

Build and improve ML and data pipelines, ship full-stack product features, and enhance Mac and Windows desktop applications. Work directly with enterprise customers to clarify requirements, deploy solutions, and turn customer learnings into repeatable product improvements. The role requires high ownership, rapid execution, strong engineering practices, and measurable customer outcomes across applied ML, AI systems, product, and infrastructure.

Develop, optimize, and deploy computer vision and machine learning algorithms for autonomous robotics systems. Train custom models, including object detection, segmentation, and 3D vision architectures, for embedded platforms. Collaborate across teams to integrate algorithms into products, research emerging computer vision techniques, and maintain documentation and unit tests. The role requires strong PyTorch, C++, Python, multi-view geometry, deep learning, and robotics expertise.

Design, implement, and maintain scalable ML pipelines for training, deployment, and monitoring. Automate data preprocessing, feature engineering, and validation. Productionize models with data scientists and backend engineers, ensuring reliable, secure, and efficient ML infrastructure and optimized training and inference performance.

Design and scale production AI/ML systems, including autonomous agents, LLM integrations, retrieval pipelines, and virtual assistants with voice capabilities. Build and optimize models, training and deployment pipelines, APIs, and cloud-native services. Apply agent frameworks, vector databases, RAG, fine-tuning, observability, and MLOps practices. Collaborate on architecture, evaluate emerging reasoning and tool-use approaches, and mentor engineering peers.

Designs, develops, and deploys end-to-end AI/ML solutions for financial services, including generative AI applications, LLMs, RAG systems, data pipelines, and production machine learning models. Builds AWS-based infrastructure and ETL workflows, performs model validation and risk assessments, establishes benchmarking and monitoring frameworks, and ensures compliance with banking regulations, privacy requirements, and ethical standards. Collaborates with technical and business stakeholders to identify AI opportunities and communicate complex concepts.

Develop and deploy applied ML and computer vision models to understand technical drawings and specifications. Work on multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to build customer-impacting solutions.

Build and maintain ML infrastructure and data systems for medical imaging: distributed training, RL training stack, high-throughput data pipelines, centralized storage, and production serving to accelerate research-to-production iteration.

Design, build, and maintain batch and streaming data pipelines on GCP (BigQuery, dbt, Airflow, Pub/Sub). Productionize ML workflows (training, inference, CI/CD) using Cloud Run, GKE, and Vertex AI. Support real-time inference, RAG/LLM assistants, graph-based recommendations, and model monitoring. Collaborate with cross-functional teams to deliver scalable, reliable data products and observability for downstream ML and analytics.

Develop and prototype AI/ML-driven automation for ASIC/SoC design and implementation, collaborating with academia and cross-functional teams to apply GenAI, RL, GNNs and CNNs to VLSI CAD flows, pilot solutions on real designs, and improve power, performance, area, and quality.

Build and maintain scalable ML and generative AI platforms and backend infrastructure; deploy and manage production applications; create services exposing ML/AI products; implement RAG solutions and vector databases; integrate LLM orchestration and foundation model APIs; collaborate cross-functionally and mentor teammates.

Lead development of large-scale ranking, personalization, and recommendation systems for Roku's entertainment assistant. Design and deploy ML models (LLMs, transformers, RL, bandits), build feature platforms and data pipelines, run online A/B experiments, and mentor engineers while balancing engagement, retention, relevance, and monetization objectives.

Lead development, training, optimization, and edge deployment of perception models for maritime autonomous vehicles. Curate fleet data, run measurable ML experiments, build data pipelines, establish evaluation frameworks, and integrate models with vehicle software to validate real-time performance on embedded hardware.

Optimize inference performance of large multimodal foundation models across cloud and on-robot targets. Diagnose bottlenecks, apply quantization/pruning/distillation, tune kernels (CUDA/Triton), build benchmarking and regression detection, and translate research models into deployment-ready implementations.

The Machine Learning Engineer will propose technical initiatives, lead design discussions, translate challenges into plans, and uphold engineering standards.

The Senior AI/ML Engineer will build scalable AI systems, work with LLMs, optimize models, and deploy solutions using AWS while ensuring ethical standards.

Lead technical direction for autonomous mineral refining by designing, validating, and deploying RL-based control models. Own autonomy roadmap, set simulator and modeling standards, solve sim-to-real and non-stationarity problems, partner with domain experts and leadership on techno-economic decisions, and mentor engineers while delivering production ML that runs on live plant equipment.

Develop and train reinforcement-learning control models in realistic simulators, refine training environments and reward logic, analyze and improve model performance, close the sim-to-real gap with plant data, write well-tested production code, and collaborate with process and chemistry experts to deploy autonomous controllers optimizing recovery, reagent use, energy, and uptime.

The Senior Python Engineer designs, develops, and deploys machine learning solutions, ensuring scalability and integration into production systems, while collaborating with engineering and data teams.

Design, build, and operationalize production-grade AI/Machine Learning solutions and MLOps services. Deploy models and API services to AWS and GCP using CI/CD and IaC, implement workflow automation, monitor post-production performance, research generative/ML techniques, and mentor junior engineers while collaborating with cross-functional data, cloud, and architecture teams.

As a Senior Machine Learning Engineer, you will own ML infrastructure and data systems, scaling them for large volumes of video data and supporting deep learning training and experimentation.