Top Senior Machine Learning Jobs

Build and own production machine learning infrastructure for growth initiatives, including feature pipelines, point-in-time-correct training data, batch inference, model registries, retraining automation, validation safeguards, and production monitoring. Partner closely with data science and data engineering to productionize models, prevent data leakage and train-serve skew, and ensure reliable model-driven promotions.

Build and ship differentiated AI experiences for sales teams, develop domain-specific AI capabilities, shape the company’s AI/ML strategy, identify high-value machine learning use cases, prototype LLM-powered products, productionize robust features, and recruit and mentor AI/ML engineers.

Build and operate machine learning, optimization, and control systems that allocate paid marketing budgets, pace spending, forecast user value, and improve growth outcomes. Develop production data pipelines, training and inference workflows, monitoring, experimentation, and safe fallback systems. Partner with Growth Marketing, Data Science, Engineering, and Finance to define objectives, evaluate causal impact, and make reliable decisions under uncertainty.

Develop and deploy AI/ML solutions for semiconductor physical design workflows. Improve chip power, performance, and area through machine-learning methods, automate design flows, and create algorithmic solutions for PPA optimization. Responsibilities include P&R, timing closure, IR/Signal-EM convergence, power and noise analysis, floorplanning, power and clock distribution, chip assembly, and back-end verification across multiple projects.

Designs, develops, tests, deploys, and optimizes large-scale machine learning systems and algorithms. The role translates customer and business requirements into scalable, robust data-driven solutions, processes massive datasets, and delivers production insights. Responsibilities include researching emerging machine learning methods, collaborating cross-functionally, developing testing strategies, and integrating new techniques into system architecture.

Design, train, and validate machine learning models that improve simulated LiDAR, radar, and camera realism for autonomous vehicle simulation. Build datasets from real and simulated sensor data, integrate 3DGS and NeRF reconstruction, develop generative augmentation methods, define sensor realism metrics, reduce sim-to-real gaps, and mentor engineers and interns.

Own and develop Arc Institute’s machine learning infrastructure for virtual cell models, including model evaluation dashboards, single-cell data pipelines, training-set quality controls, and lab-in-the-loop systems. Build scalable train-and-evaluate CI, support distributed multi-node training, improve GPU utilization and throughput, and create validation frameworks for AI-generated software. Partner directly with ML researchers and scientists while setting technical direction for production platforms.

Lead the technical strategy, architecture, development, and productionization of large-scale machine learning and pricing systems for Uber’s real-time marketplace. Develop deep learning, causal, reinforcement learning, optimization, and algorithmic pricing solutions serving billions of trips. Mentor engineers, influence cross-team technical direction, communicate with executives, and deliver high-impact systems for millions of drivers.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Build and operate production search, ranking, recommendation, and multimodal AI systems for a global jewellery marketplace. Own machine-learning models end to end, including feature development, training, evaluation, deployment, monitoring, and low-latency serving. Develop contextual and conversational search, image-based retrieval, learning-to-rank systems, and embedding pipelines. Define ranking experiments, evaluate LLM behavior, and design graceful recovery from probabilistic failures across multi-tenant storefronts.

Design and develop machine-learning approaches for vehicle control, including learned vehicle dynamics models, adaptive controllers, data pipelines, evaluation metrics, and simulation tooling. Integrate learned models into safety-critical closed-loop systems and validate algorithms through offline experimentation, simulation, and on-vehicle testing. Collaborate with engineers and research scientists to combine classical control with AI for scalable self-driving technologies.

Lead a team of 5–6 engineers building Waymo’s marketplace platform and infrastructure. Develop ML infrastructure for training and deploying models that support real-time pricing, rider-vehicle matching, positioning, and supply-demand optimization. Build experimentation, monitoring, analysis, and simulation systems for evaluating deployed models and improving marketplace decisions. The role requires technical leadership, people management, backend programming, and production ML experience.

Develops applied AI and machine learning capabilities for biopharmaceutical development. Builds novel models, data pipelines, and user-facing solutions to support project decisions, experimental design, investigations, and biologics process development. Leads strategic modeling programs, advises scientific teams, applies advanced statistics and algorithms, and collaborates with business and IT stakeholders. The role also advances LLM applications, cloud-based solutions, intellectual property, and data-driven development practices.

Own evaluation systems and model-quality improvements for a generative content platform producing AI-generated ecommerce content. Build datasets, rubrics, automated judges, quality gates, and labeled training data from brand feedback. Quantify qualitative improvements, establish approval thresholds, and drive fine-tuning or retrieval experiments into production. The role requires strong software and system design skills, production service ownership, formal machine learning or statistics training, and experience measuring correctness in non-deterministic systems.

Develops predictive models and data-driven solutions by analyzing complex datasets and applying advanced statistical and machine learning techniques. The role communicates insights to stakeholders, collaborates with cross-functional teams, and integrates analytics into business strategy. It requires extensive data analysis experience, an active TS/SCI clearance with polygraph, and U.S. citizenship. The position is onsite in Herndon, Virginia, with less than 10% travel.

Develop and deploy perception, generative AI, and online mapping algorithms for L4 autonomous trucks. Responsibilities include building robust real-time software for safety-critical systems, integrating maps and vehicle sensors, improving mapping performance, leading features from concept through delivery, and coordinating stakeholders. The role requires deep learning model development and deployment, applied research, machine learning algorithm design, Python, and C++, with sensor fusion or online mapping experience preferred.

Build scalable machine learning pipelines, data and training infrastructure, distributed processing workflows, and reusable software systems for foundation-model research. Own codebase health, testing, reliability, maintainability, and performance across ML workflows. Collaborate with researchers and engineers to translate model and experiment requirements into robust systems supporting large datasets, large models, multimodal data, video, robotics, and embodied AI.

Develop advanced autonomy algorithms for physical AI, including data mining, deep scene understanding, causal modeling of ego-vehicle behavior, and robust identification of complex driving edge cases. Deploy algorithmic solutions for real-world, safety-critical autonomous systems and help establish high technical standards within Uber’s AV Labs team.

Develop and own end-to-end machine learning models for Uber Freight’s marketplace, including cost prediction, booking probability, demand elasticity, pricing, matching, recommendations, and optimization. Partner with backend engineers to deploy models to production and ensure reliable performance. Apply predictive modeling, causal inference, constrained optimization, reinforcement learning, and marketplace design to solve complex logistics problems.

Build and operate production machine learning and backend systems for AI data and evaluation products. Own a business vertical end to end, from identifying opportunities and setting direction to delivering revenue-generating products. Partner with customers, product managers, and engineers; drive adoption, customer value, and execution quality. Apply machine learning across multimodal data, LLM evaluation, physical-world data, robotics, audio, video, and related domains.

Develop autonomy algorithms and foundation models that extract semantic insights from complex driving data. Design scalable machine learning systems, manage sensor dependencies, optimize datasets through data processing and auto-labeling, and deploy models into production. Collaborate with platform, product, and security engineering teams while providing technical leadership. The role requires expertise in Python, Linux, modern ML frameworks, and preferably autonomous driving, safety-critical systems, C++, and high-performance computing.

Lead the technical direction and development of Uber’s real-time marketplace matching optimization systems. Build and productionize machine learning and decision systems using reinforcement learning, deep learning, causal inference, optimization, and control methods. Formulate ambiguous problems, develop behavioral models, create simulation and experimentation frameworks, and deploy scalable online inference systems. Collaborate cross-functionally, lead 0-to-1 initiatives, improve marketplace performance, and mentor engineers.

Develop autonomy algorithms that add semantic understanding to real-world driving data. Design onboard systems, manage sensor dependencies, define evaluation metrics, and build scalable production solutions for safety-critical autonomous vehicle systems. Collaborate with platform, product, and security engineering teams to deploy onboard systems.

Lead machine learning engineering for fraud prevention and account integrity. Shape technical roadmaps, formulate problems, design and productionize large-scale ML systems, oversee model pipelines and system architecture, conduct A/B testing, and manage rollouts. Mentor and develop MLE talent while applying machine learning, statistics, and optimization techniques to real-world risk and trust challenges.