Top Machine Learning Jobs

Develop deep learning models using imitation and reinforcement learning to generate and evaluate autonomous vehicle trajectories. Build production machine learning pipelines, metrics, and error-analysis tools, leveraging large-scale infrastructure. Collaborate with Planning, Perception, Simulation, and Systems Engineering teams to improve autonomous driving behavior in complex urban environments.

Design, build, evaluate, and productionize machine learning and LLM-based agentic systems for healthcare products. Own projects end-to-end, including problem framing, dataset development, modeling, evaluation, deployment, monitoring, and safety guardrails. Build ML infrastructure, data pipelines, inference systems, and evaluation frameworks. Collaborate with clinicians, product managers, and engineers, while setting technical direction and mentoring team members.

Build shared machine learning capabilities for AI-native platform products, including document extraction, foundation models, model routing, evaluation, agentic systems, and continuous improvement. Translate urgent product-team needs into reusable platform abstractions, own capabilities through production, optimize accuracy, latency, cost, and reliability, and develop feedback and retraining loops that improve deployed systems across regulated institutional workflows.

Leads Pinterest’s advertiser measurement and signal-quality engineering organization, defining multi-year strategy across machine learning, data platforms, experimentation, privacy, and reliability. Manages managers and senior technical leaders, partners with product and cross-functional teams, drives scalable solutions for advertiser challenges, improves model quality and data integrity, establishes governance mechanisms, communicates with executives, and builds an inclusive high-performing organization.

Develop and extend reinforcement learning infrastructure for Waymo’s autonomous driving platform. Conduct experiments, tune parameters, compare results, and evaluate new reinforcement learning technologies for model training, simulation, and quality evaluation. The intern will collaborate with software engineering and research teams to improve autonomous driving models and systems.

Design, build, and operate scalable production machine learning platforms and MLOps systems. Own the ML lifecycle, including feature engineering, deployment, monitoring, governance, retraining, model versioning, and real-time inference. Build Feature Stores, Model Registries, CI/CD/CT pipelines, and data pipelines across cloud and streaming environments. Ensure performance, reliability, observability, security, regulatory compliance, and explainability while collaborating with data, software, and business teams and mentoring junior engineers.

Develops advanced machine learning, deep learning, signal processing, optimization, and numerical modeling algorithms for biomedical signals and next-generation medical devices. Responsibilities include algorithm design, implementation, testing, documentation, data analysis, disease detection and prediction modeling, patentable innovation, and collaboration with hardware and software engineering teams.

Develop AI/ML algorithms and ETL infrastructure for LLM, NLP, and time-series solutions. Build scalable data pipelines, curate evaluation datasets, implement machine learning models, optimize data delivery, automate processes, and develop software using Agile methodologies. The role collaborates with researchers and developers and may involve cloud services, distributed computing, containerization, GenAI operations, reinforcement learning, and GPU-based algorithm implementation.

Design, test, deploy, monitor, and improve machine learning models and algorithms supporting compliant data management applications. Lead model architecture, data pipeline integration, metrics interpretation, governance, and scalable production solutions. Serve as a primary AI/ML point of contact while leading geographically distributed teams and communicating with customers and stakeholders.

Build scalable data infrastructure for LLMs and autonomous AI agents. Responsibilities include automated fine-tuning pipelines, real-time telemetry and evaluation, synthetic data generation, data quality and curation, MLOps integration, and vector database management for RAG workflows. The role requires strong Python and SQL skills, experience with LLM APIs, open-source models, orchestration tools, cloud infrastructure, distributed processing, and synthetic data techniques.

Lead Osmo’s Applied AI organization building olfactory intelligence models for prediction, formulation, reformulation, molecule generation, and deformulation. Hire and develop ML engineers and research scientists, set the model roadmap and evaluation standards, own enterprise accuracy commitments, design data and annotation strategies, and move research models into reliable production APIs. This hands-on leadership role partners closely with chemistry, sensory science, software, product engineering, and perfumers while remaining involved in code reviews and experimentation.

The Senior Research Engineer will develop and deliver scalable ML solutions, collaborate with teams, and apply innovative technologies in AI to impact professionals in legal and accounting fields.

Develop machine analytics products using Python, SQL, statistical modeling, machine learning, and industrial IoT data. Build web applications, dashboards, APIs, ETL pipelines, and containerized solutions for machine monitoring, anomaly detection, failure diagnosis, forecasting, and technical support. Collaborate with engineering and service teams to translate diagnostic challenges into practical tools and communicate analytical findings to technical and non-technical stakeholders.

Designs, deploys, and operates production machine learning systems for identity resolution, scoring, deliverability, and personalization. Responsibilities include feature engineering, model training, real-time inference, graph database integration, monitoring, drift detection, retraining, low-latency APIs, and scalable data processing. The role also contributes to MLOps standards, GPU acceleration, and ML architecture while partnering with platform engineering.

Build and optimize high-volume, real-time inference systems for speech and language models. Responsibilities include improving model serving with quantization, batching, caching, attention, and decoding techniques; developing high-performance GPU systems; scaling distributed inference across Kubernetes and multi-node environments; profiling and optimizing code; containerizing models; and ensuring reliable production operation.

Own machine learning modeling for a software platform, adapting large language models, building training data pipelines, and developing evaluation methods. Run experiments, refine models, and collaborate with researchers and systems engineers to deploy dependable models in production. The role focuses on electronic design automation and hardware design data, including netlists, RTL, surrogate models, and graph neural networks. It also involves post-training LLMs, distributed training or inference, and model optimization.

Develops search relevance solutions for an enterprise question-answering platform. Responsibilities include creating and optimizing ranking features, building frameworks for diversified ranking, enabling parallel ranking experiments, and developing metrics to evaluate search performance. The role applies information retrieval, machine learning, text processing, and natural language understanding to address lexical gaps, semantic understanding, and data sparsity while collaborating with product, UX, customer success, and engineering teams.

Build, deploy, scale, and operate production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, managing cloud-based distributed systems, automating testing and deployment, monitoring production models, and applying responsible AI practices. The role collaborates with Product and Data Science teams in an Agile environment and uses technologies such as Python, PyTorch, Spark, Kubernetes, and cloud platforms.

Design, build, deploy, monitor, and maintain production machine learning models and infrastructure. Develop optimized data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Apply modeling, feature selection, validation, responsible AI, and explainability practices while collaborating with product, data science, and engineering teams in an Agile environment.

Own and lead the development, migration, and operation of recommendation systems for Fable and Everand. Responsibilities include moving Fable’s recommender to the Orion ML platform, extending ranking and retrieval capabilities, incorporating social signals, managing production quality and reliability, designing A/B tests, partnering with product managers, leading cross-team delivery, and improving engineering practices.

Build and deploy manufacturing data systems, machine-vision inspection solutions, and machine-learning models across supplier factories. Integrate MES, equipment, test, image, and genealogy data to improve traceability, yield, quality, process control, and root-cause analysis. Develop automated analytics, dashboards, alerts, and reusable tools; lead deployments through NPI and mass production; collaborate with manufacturers and vendors; and define the long-term manufacturing intelligence roadmap.

Design and develop machine learning algorithms for target ROAS, automatic bidding, and ads pacing. Explore bid landscapes, optimize advertiser value and revenue, conduct A/B experiments, and collaborate with product, sales, and engineering teams on new ad products and bidding strategies. Provide staff-level technical leadership, drive cross-team alignment, and use AI-assisted development and productivity tools to improve engineering workflows.

Build and maintain scalable machine learning models and end-to-end ML pipelines for logistics automation. Responsibilities include data exploration, model deployment and monitoring, production infrastructure improvements, collaboration with engineering and product teams, code reviews, architectural guidance, and technical mentorship.

Serves as chief architect for Sony Interactive Entertainment’s Data & Intelligence organization. Defines enterprise data and machine learning architecture, standards, reusable platforms, governance, experimentation, activation, and AI lifecycle practices. Chairs architecture reviews, leads complex cross-organizational initiatives, advises executives, removes technical blockers, and mentors senior technical professionals. The role is hands-on, requiring architecture authorship, prototyping, code and design reviews, and delivery of scalable data, ML, and decisioning systems serving millions of consumers.

Lead the design, development, deployment, and operation of production-grade generative AI and LLM solutions. Build data and ML pipelines, fine-tune and evaluate models, implement RAG and prompt engineering, and establish MLOps practices on AWS. Drive use cases across insurance workflows, ensure security and responsible AI compliance, mentor engineers, and collaborate with product, engineering, security, finance, and go-to-market teams.