Top Senior Machine Learning Jobs

Design and validate threat models for agentic AI systems, create adversarial experiments and synthetic environments, develop static and dynamic risk analysis, fine-tune and evaluate models, and build production-ready guardrails, monitoring, dashboards, and evaluation infrastructure. Optimize security systems for latency, cost, reliability, and measurable real-world performance while translating research into product capabilities.

Own the strategy, design, implementation, and productionization of machine learning capabilities for an unpublished R&D game project. Build reusable training and evaluation pipelines, apply reinforcement learning to improve decision-making, establish research-to-production and reliability practices, guide experimentation, and strengthen engineering standards. Collaborate across engineering, design, data science, and analytics while mentoring engineers and addressing technical risks.

Design, build, and maintain optimization technology, full-stack systems, and data-intensive scheduling infrastructure. Improve optimization algorithms using linear optimization, machine learning, and generative AI. Lead complex technical initiatives, establish testing and observability, support scalable operations, troubleshoot customer issues, participate in on-call rotations, and mentor engineers while partnering closely with product and engineering teams.

Develop and deploy machine learning systems for humanoid robot manipulation, covering data collection, annotation, synthetic data generation, model training, simulation, hardware deployment, and fine-tuning. Diagnose vision, calibration, sensor fusion, model, and data bottlenecks to improve reliability. Build real-time robotic software using modern computer vision and deep learning, while mentoring engineers and driving architectural excellence across the robotics organization.

Build and scale production machine learning systems for personalization and marketing optimization. Design end-to-end AI, reinforcement learning, recommendation, propensity, uplift, and lifetime-value pipelines; implement CI/CD, model versioning, monitoring, and orchestration; and lead agentic AI workflow automation. Collaborate with data science, engineering, marketing, and operations teams to productionize solutions. Provide technical leadership, mentor engineers and data scientists, and influence ML and data strategy.

Lead technical direction for Pinterest’s foundational machine learning capabilities in visual representation learning, multimodal content understanding, and LLM-based personalization. Drive initiatives from research and experimentation through productionization and adoption. Build reusable embeddings, models, semantic signals, and ML infrastructure using distributed training and data pipelines. Collaborate across product, engineering, data science, and applied science while mentoring technical teams and guiding multi-quarter roadmaps.

Designs and integrates AI/ML models, agents, and workflows for secure, enterprise-scale mission capabilities. Responsibilities include developing agent orchestration, RAG pipelines, vector database integrations, MCP tool brokers, model routing, evaluation and benchmarking pipelines, observability, governance guardrails, and production AI services. Partners with data scientists and mission analysts to operationalize prototypes and researches emerging AI tooling.

Designs and integrates production AI/ML systems for a secure enterprise AI platform supporting cyber and national mission operations. Responsibilities include building LLM agent workflows, RAG pipelines, vector database integrations, model routing, MCP tool brokers, evaluation frameworks, observability, and governance guardrails. The role partners with data scientists and mission analysts to productionize prototypes and research while evaluating emerging AI technologies.

Lead full-stack development and technical strategy for Copilot Discover: design, build, and operate scalable ML/deep-learning models and prompt strategies; implement responsive TypeScript/React/NextJS/Web Components front-ends; develop REST APIs and data layers; ensure performance, scalability, and reliability; collaborate across UX, backend, and ML teams; participate in code reviews and architecture decisions.

Own the design, provisioning, operation, and optimization of cloud-based GPU clusters supporting large-scale model training and inference. Partner with AI engineers to improve training performance and hardware utilization, contribute to ML libraries and infrastructure tooling, develop GPU compute strategies, optimize multi-cloud capacity and costs, and improve reliability, testing, documentation, and engineering practices.

Leads model distillation, parameter-efficient fine-tuning, reinforcement-learning alignment, dataset development, quantization-aware training, and evaluation for multimodal AI models deployed on automotive edge hardware. Sets architectural direction for optimization pipelines, selects foundation models, manages continuous improvement loops, and ensures compact models retain reasoning, vision, language, accuracy, and safety performance after compression.

Leads the architecture, development, and deployment of AI/ML integrations and scalable event-driven backend systems for distributed clinical applications. Builds Kafka-based services, secure APIs, database integrations, CI/CD pipelines, containerized cloud deployments, and observability platforms. Develops agentic AI, LLM, and RAG solutions while collaborating with data scientists, engineers, and product stakeholders. Drives technical standards, design reviews, code quality, and continuous engineering improvement.

Leads the design, development, deployment, and operation of production AI/ML solutions, including generative AI, RAG, agentic workflows, conversational AI, predictive models, and automation. The role establishes responsible AI practices, integrates enterprise data and APIs, improves evaluation and observability, partners with cross-functional teams, mentors engineers, and drives initiatives from proof of concept through production scale.

Build and optimize end-to-end search and discovery systems, including machine learning pipelines, serving infrastructure, and relevance solutions. Develop AI products using deep learning and large language models, improve search quality, deliver complex systems with measurable business impact, collaborate across teams, remove technical blockers, and recruit and develop engineering talent.

Develop and operate production AI/ML systems, including distributed model infrastructure, automated evaluation and testing, GPU optimization, secure CI/CD, and performance analytics. The role owns features end to end from architecture through production, performs model benchmarking and error analysis, and manages reliable deployment across multi-node clusters.

Build and adapt AI models for customer-specific scientific workflows. Lead post-training using SFT and reinforcement learning, design evaluation systems, analyze model failures, and drive iterative quality improvements. Partner with AI researchers and software engineers to integrate reliable model capabilities into production workflows. The role requires strong Python and PyTorch skills, deep familiarity with language, multimodal, or agentic models, and the ability to translate customer needs into technical solutions.

Build and scale production AI systems for Prophetic’s real estate intelligence platform. Responsibilities include developing agentic systems, data pipelines, inference workflows, evaluation benchmarks, geospatial processing, and machine learning applications across hundreds of millions of records. The role requires shipping reliable Python services, optimizing throughput and cost, integrating research into products, and collaborating with engineering and product teams.

Lead Zoox’s ML performance optimization team and roadmap for scalable training and inference. Oversee ML platform design, distributed training, GPU utilization, memory and communication optimization, model compression, profiling, and low-latency vehicle inference. Hire and mentor engineers while collaborating with ML research, software, data, product, and hardware teams to deliver measurable performance improvements across autonomous driving systems.

Leads scientific and technical direction for AI across structural heart applications. Designs, trains, validates, and deploys advanced machine learning models, including foundation, generative, multimodal, clinical language, imaging, and waveform models. Evaluates emerging technologies, establishes scientific standards, mentors technical staff, and partners with engineering, clinical, quality, and regulatory teams on safe, fair, robust, and compliant AI products. Builds data pipelines and MLOps capabilities while supporting model validation, risk management, monitoring, and regulatory documentation.

Lead development and deployment of predictive and ML models for credit risk, collections, and operations. Mentor data scientists, promote MLOps and modern tools, present insights to stakeholders, and drive experimentation with techniques such as causal inference and GenAI.

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 productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

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.

Leads the Scalable Machine Learning team and owns technical strategy for data generation, orchestration, distributed training, and shared ML infrastructure. Responsibilities include managing engineers, setting multi-quarter roadmaps, coordinating GPU scheduling and cloud infrastructure, advancing MLOps practices, influencing peer teams, delivering multi-organization initiatives, and maintaining high reliability across shared systems.