Top Machine Learning Jobs

Own numerical analysis and model parity for compressed, quantized, and optimized autonomous-vehicle models. Develop Jacobian/Hessian sensitivity tools, connect numerical differences to driving behavior, diagnose training instabilities, and build low-overhead observability for distributed training. Investigate floating-point errors, edge cases, and implementation divergence while translating analytical results into deployment decisions and production-ready tooling.

Develop and maintain AI-driven autonomous planning, scheduling, and optimization systems for Earth observation satellite operations. Build reinforcement learning algorithms, simulation and evaluation environments, and multi-objective optimization pipelines for satellite constellation tasking. Collaborate with aerospace engineers, mission operators, software engineers, and product teams to translate mission requirements into deployable systems. The role requires strong Java and Spring Boot experience, software engineering fundamentals, and machine learning or optimization expertise.

Design, develop, deploy, and maintain production machine learning models and infrastructure. Build optimized data pipelines, distributed systems, cloud-based architectures, and automated testing and deployment processes. Monitor and retrain models, ensure responsible and explainable AI practices, and collaborate with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.

Develops scalable, production-ready AI/ML and generative AI models, training, evaluation, and inference pipelines in Python. Deploys and manages models using MLOps, CI/CD, containerization, and microservices. Requires expertise with PyTorch and TensorFlow, substantial relevant experience, and an active Top Secret clearance with willingness to obtain a polygraph.

Research emerging AI workloads and computing architectures, evaluate memory and storage requirements, and analyze performance, power, cost, bandwidth, capacity, latency, and scalability tradeoffs. Develop simulations, benchmarks, models, and analytical tools for next-generation memory and storage concepts. Characterize workloads across CPUs, GPUs, accelerators, and memory-centric platforms while collaborating with architects and engineers on technology assessments and proof-of-concept evaluations.

Build and lead large-scale, real-time machine learning systems for Pinterest’s growth content and notification ranking. Drive model improvements through A/B testing, own training and serving infrastructure, establish evaluation and monitoring practices, and guide technical direction across teams. Mentor engineers, conduct rigorous reviews, collaborate cross-functionally, apply state-of-the-art research, and improve engineering quality, system reliability, latency, and cost efficiency.

Leads and mentors machine learning engineers while setting team roadmaps, guiding technical architecture, and delivering production-scale models. Oversees the full ML lifecycle, including data collection, labeling, experimentation, training, testing, deployment, and quality measurement. Collaborates with product, engineering, DevOps, sales, recruiting, and labeling teams. Also manages security practices, risk, compliance, hiring, onboarding, and engineer development.

Leads and mentors machine learning engineers developing and deploying large-scale models. Partners with product, engineering, DevOps, labeling, sales, and recruiting teams to define roadmaps, deliver product milestones, and improve model quality. Oversees the full ML lifecycle, including data collection, labeling, experimentation, training, testing, deployment, security, and risk management. Contributes to technical architecture, modeling strategies, hiring, onboarding, and engineer development.

Designs, builds, and operates production-grade data, machine learning, and generative AI solutions. Responsibilities include scalable batch and streaming pipelines, lakehouse architecture, data modeling, governance, model deployment, MLOps, CI/CD, infrastructure as code, observability, and agentic software development. The role may support fixed-price client delivery across Azure, AWS, and GCP or internal analytics on Azure Databricks, including data integration and quote-to-cash automation.

Develop machine learning systems to detect AI misuse, anomalous behavior, coordinated attacks, and agentic risks. Build synthetic data pipelines, classifiers, monitoring systems, threat models, evaluations, and mitigations for prompt injection and other harms. Conduct research in automated red-teaming and adversarial robustness while taking projects from exploratory research through production deployment and communicating technical findings to nontechnical stakeholders.

Build shared AI capabilities for an industrial AI platform, including anomaly detection, time-series and multimodal models, data quality methods, knowledge-graph mapping, and AI agents. Develop evaluation frameworks, productionize models through serving and monitoring, and package reusable platform services. The role requires collaboration with platform engineering and product teams to deliver reliable, scalable AI capabilities across multiple applications and customers.

Develop production machine learning systems for Glean’s Enterprise Brain, focusing on user understanding, task prediction, LLM workflows, personalization, reinforcement learning, ranking, and agentic systems. Build scalable evaluation and benchmarking loops, maintain ML pipelines and knowledge graphs, improve data and model quality, and collaborate cross-functionally to deliver reliable enterprise AI solutions.

Summer interns will work with portfolio managers, quants, and front-office teams to analyze financial data, develop models and trading signals, build AI- and LLM-powered tools, and contribute to analytics libraries. The program includes training in macroeconomics, financial markets, trading strategies, risk management, Python, Excel, and AI tools. Interns will research trade ideas, support market models, and may be considered for the firm’s graduate program.

Deploy and operate machine learning, language, and speech services within secure DoD cloud environments. Migrate retrieval and modeling workflows, optimize model quality and performance, build structured evaluations, maintain release and data provenance records, and implement secure retrieval permissions and logging. The role requires production-scale model operations, IL5 experience, compliance collaboration, and full lifecycle ownership from concept through launch.

Develop and characterize ReRAM technology, including device behavior, variability, reliability, modeling, and array analysis. Build AI-enabled engineering solutions using machine learning, generative AI, physics-informed AI, and AI agents for semiconductor modeling, simulation, EDA automation, data analysis, and workflow improvement. Collaborate across semiconductor engineering teams and translate technical challenges into practical software, modeling, and automation tools.

Build LLM- and agent-based prototypes and production solutions, develop ML models where appropriate, prioritize reuse and scalability, collaborate with product, platform, and front-end partners, and support business development through technical delivery and client-facing engagement.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Own the end-to-end architecture of a production exploitability analysis engine using probabilistic modeling, graph ML, agentic AI, and security analytics. Lead zero-to-one development, technical direction, architecture and code reviews, validation and calibration, AI safety and governance, and cross-functional partnerships. Mentor senior engineers and make consequential system decisions involving attack paths, identity graphs, risk scoring, and uncertainty modeling.

Lead the architecture and hands-on development of cloud-native infrastructure for ServiceNow’s Agentic AI and conversational platforms. Design scalable Kubernetes-based distributed systems, drive automation and observability, establish engineering standards, and ensure highly available, secure, and resilient production services. Partner across engineering and product organizations, influence technical strategy, lead initiatives from concept through deployment, evaluate emerging technologies, and mentor senior engineers.

Develop and operationalize scalable machine learning and advanced analytics solutions with business impact. Lead projects through the software development lifecycle, provide technical guidance, build prediction and optimization applications, contribute to ML platforms, and mentor teammates. The role requires collaboration with global AI/ML teams and expertise across cloud infrastructure, data engineering, MLOps, and software architecture.

Owns a major subsystem of a zero-to-one exploitability analysis engine, such as probabilistic scoring, exposure graphs, entity resolution, or calibration. Designs, delivers, and scales production systems; establishes quality metrics, AI safety guardrails, and technical direction. Partners with product, security R&D, and SecOps, leads reviews and workstreams, mentors engineers, and applies machine learning, graph analytics, LLMs, and probabilistic modeling to security problems.

Build, deploy, scale, retrain, and monitor machine learning models and infrastructure in production. Develop distributed training and serving platforms, optimized data pipelines, cloud-based architectures, and automated testing and deployment systems. Collaborate with Product and Data Science teams, apply advanced ML techniques, operate Kubernetes-based services, and ensure models meet governance, security, responsible AI, and explainability standards.

Leads machine learning engineering teams while designing, deploying, scaling, monitoring, and governing production ML models and platforms. Responsibilities include building distributed ML systems, cloud architectures, data pipelines, CI/CD automation, model retraining, responsible AI practices, and resilient production services. The role combines hands-on technical leadership with mentoring, recruiting, talent development, and cross-functional collaboration with Product and Data Science teams.

Design, develop, and deploy secure AI/ML and full-stack solutions for healthcare underwriting and benefits modernization. Build RAG pipelines, integrate LLMs and agentic AI capabilities, develop Python-based machine learning systems, and expose models through production APIs. Collaborate with engineering, architecture, product, data, and underwriting teams while ensuring model availability, security, performance, testing, monitoring, and responsible AI use.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build tooling for profiling and debugging, integrate kernels into the ML runtime, collaborate with compilers and architecture teams, and deliver high-performance, production-grade libraries meeting strict latency, throughput, and reliability targets.