Top Senior Machine Learning Jobs

Develop, design, and maintain AI/ML software solutions; collaborate with engineers and leadership; build tools to analyze large-scale data; debug, profile, optimize, and test software; create documentation and user support materials; leverage modern LLM tools to accelerate development.

Build and re-architect Shipt's LLM-native search stack including retrieval and ranking systems. Design multi-task and multi-objective models, integrate personalization signals, develop hybrid dense/sparse retrieval, and apply generative retrieval and query expansion. Analyze search data, report findings to leadership, and partner with senior scientists and ML engineers to improve relevance, revenue trade-offs, and operational metrics.

As a Machine Learning Engineer, you'll develop and enhance driving models, lead projects on personalized driving, and collaborate across teams to ensure system performance for automated driving.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

The role involves developing and maintaining behavioral models for road users to enhance autonomous vehicle decision-making, collaborating with other teams and optimizing outputs for safety and effectiveness.

The Senior Machine Learning Engineer at Grailed will enhance personalization and recommendations, develop AI/ML solutions, and collaborate across departments for data-driven insights.

Develop algorithms in machine learning and statistics to address complex challenges. Design data pipelines and deploy solutions using cloud infrastructure.

Design, build and deploy production AI/ML solutions from complex, distributed farm data. Engineer features, select models, run experiments, move models to scalable real-time production, monitor and observe AI systems, evaluate emerging AI technologies, and collaborate with product, engineering and domain teams. Contribute to user-facing features with React/TypeScript as needed.

Design, build, and deploy production-grade AI/ML solutions that convert complex, distributed farm data into actionable intelligence. Engineer features, select models, run experiments, and move models to scalable, real-time production. Integrate, fine-tune, monitor, and observe models on cloud infrastructure (AWS, Databricks). Partner with product, engineering and domain teams and contribute to user-facing experiences when needed.

Design, develop, and deploy AI/ML models and pipelines for chemical and biological threat characterization. Build data preprocessing, embedding, entity extraction, and model monitoring systems; integrate models into secure cloud environments via APIs and MCP. Create model cards, explainability tools, and support knowledge graphs. Ensure DoD cybersecurity compliance, participate in Agile teams, and assist knowledge transfer and documentation.

Design and implement advanced ML models (reinforcement learning, neural nets, metaheuristics) for logistics optimization, predictive analytics, anomaly detection, and real-time decision systems. Lead architecture design for scalable, secure AI systems, mentor teams, align technical roadmaps with defense requirements, and conduct feasibility studies and technology evaluations for contested and uncertain logistics environments.

Design and scale real-time bidding systems and bid forecasting; develop ML, reinforcement learning, and control-based bidding and pacing algorithms; analyze marketplace dynamics; run offline evaluations and online A/B experiments; collaborate with product, infrastructure, and engineering teams to deliver measurable advertiser ROI.

Build and maintain large-scale user understanding, conversion prediction, and ad ranking models. Monitor and calibrate model performance, address data drift and delayed feedback, and improve recommendation and bidding strategies. Set long-term technical direction with other leads and communicate complex technical concepts to non-engineering stakeholders.

Design, optimize, and scale training and inference workloads for personalization and continual learning. Productionize research prototypes, improve latency/reliability/efficiency, optimize per-user serving and memory retrieval, and work on distributed training across GPUs and nodes. Collaborate with researchers, platform engineers, and customers; establish engineering practices, testing, on-call, and security posture.

Design, build, and maintain end-to-end ML and data solutions: analyze and preprocess client data, implement ML and deep learning models, deploy LLM/GenAI solutions, build data pipelines, monitor model performance, integrate APIs, collaborate with cross-functional teams, and communicate insights to stakeholders.

Lead development of scalable AI/ML capabilities for Mail Intelligence: build LLM- and ML-based personalization, data pipelines, tools, and observability. Mentor engineers, collaborate cross-functionally, run A/B tests, implement feedback loops, and balance cost/quality/speed tradeoffs to deliver production-grade solutions.

Design, build, and deploy scalable distributed deep learning pipelines and infrastructure. Optimize hardware utilization, profile and benchmark training, manage large datasets, and enable reproducible ML workflows in cloud environments. Collaborate with ML scientists and engineers to align pipelines with research goals and drive performance, reliability, and operational best practices for large-scale model development.

Design, build, and productionize large-scale ML models (CTR, retrieval, conversion, recommendation, search). Lead end-to-end experimentation and online A/B testing, improve model serving and pipelines for low-latency scalable inference, translate product requirements into ML solutions, and mentor/lead other ML engineers.

Lead design and deployment of offline 3D reconstruction, lane-line detection, and automatic mapping pipelines from multi-sensor logged data. Develop pose estimation, sensor fusion, and perception models; implement MLOps, data ingestion/curation, and quality measurement. Serve as project lead, mentor engineers, and define analytics and ML standards to scale auto-labeling and map creation.

Design, develop, test, and productize AI/ML algorithms: select and process data, run statistical analysis and ML models, perform test & evaluation and V&V, integrate systems, retrain models, verify model quality, and convert prototypes into scalable solutions.

Design and implement LLM‑powered and agentic workflows for intelligence missions. Capture user workflows, integrate AI with APIs and enterprise data, ensure production reliability and security in classified environments, and collaborate with operators and mission leadership to deliver AI-enabled decision support.

The role involves leading ML strategy, architecting systems, designing algorithms, collaborating with teams, and mentoring engineers in AI for enterprise analytics.

As a Senior ML/AI Engineer, you'll design and deploy intelligent systems for forecasting, sentiment analysis, and autonomous decision-making. Responsibilities include building ML models, developing agentic architectures, and maintaining production systems at scale.

The role involves applying AI/ML techniques to develop model systems for healthcare, collaborating across teams, and conducting R&D.

This position involves designing algorithms, analyzing user data, optimizing workflows, and collaborating with cross-functional teams to enhance grocery product discovery and personalization at Uber.