Top Machine Learning Jobs

Develop reusable RAG pipelines and MLOps capabilities for domain-specific knowledge repositories. Build document ingestion and processing workflows, integrate embedding models, vector databases, and LLM generation, and evaluate response quality. Automate model training, validation, deployment, monitoring, and data-source configuration. Collaborate with software developers, AI/ML engineers, and systems architects to deliver scalable, secure solutions for customer projects.

Own the roadmap for machine-learning-powered pickup and dropoff features. Train, evaluate, and deploy models for occupancy detection, rider interactions, pullover selection, and lost-and-found. Design onboard/offboard fallback systems, build distributed vehicle-to-cloud services, integrate end-to-end user-facing features, and measure impact through metrics, simulation, offline evaluation, and live experiments.

Conduct applied machine learning research, develop and evaluate models, analyze data, build production pipelines, monitor model performance, and communicate technical findings. Collaborate with engineers, data scientists, and product teams to create scalable solutions for compliance, risk mitigation, and secure financial services. The intern must be pursuing a PhD and returning to school in Fall 2027.

Supports the development of machine learning models and algorithms through data preparation, experimentation, evaluation, optimization, and deployment. Collaborates with engineers, data scientists, and product teams on scalable ML pipelines and production model monitoring. Writes maintainable code, documents technical decisions, participates in reviews, and explores emerging machine learning techniques for PayPal products.

Develop, train, evaluate, and prototype machine learning and deep learning algorithms for audio applications. Research audio ML and digital signal processing methods, create datasets and evaluation tools, integrate models into software or hardware platforms, and collaborate with interdisciplinary researchers and engineers. The intern will present technical findings and may contribute to future Bose products, patents, and publications.

Lead the development and deployment of learned autonomy skills for heavy equipment. Build robot-learning proof of concepts, define technical roadmaps and system architectures, implement reinforcement and imitation learning policies, develop data and training workflows, transfer policies from simulation to physical machines, integrate with robotics systems, and establish reliable evaluation. The role requires hands-on coding, hardware debugging, cross-team technical leadership, mentoring, and delivery of production-quality autonomy capabilities.

Leads a cross-functional team developing behavior evaluation, human benchmarking, and behavioral inference capabilities for autonomous vehicles. Designs and deploys production ML models, establishes technical roadmaps, and integrates behavior critics into simulation-based validation, release decisions, and performance monitoring. Partners with autonomy, simulation, safety, and product teams to assess driving behavior against human expectations for safety, comfort, and intuitiveness.

Design, build, fine-tune, deploy, and monitor production machine learning systems focused on LLMs, RAG, agentic orchestration, and multimodal data. Own the full ML lifecycle, establish MLOps practices, guide architecture and technical standards, and collaborate with data engineering, product, scientists, and domain experts. Translate scientific and product requirements into reliable, scalable ML solutions while communicating technical tradeoffs to diverse stakeholders.

Build, deploy, scale, and monitor production machine learning models and infrastructure. Develop optimized data pipelines, cloud-based architectures, automated testing, and CI/CD workflows. Collaborate with product, data science, and Agile teams while applying responsible AI, model governance, and software engineering best practices. The role requires expertise in machine learning frameworks, distributed systems, cloud operations, Kubernetes, and production model deployment.

Design, prototype, validate, deploy, and monitor end-to-end machine learning solutions for Wealth Management. Collaborate with MLOps, validation, Risk/Legal/Compliance, and business stakeholders; run experiments and A/B tests; present modeling results and ensure production reliability.

Leads modeling strategy for Riot’s anti-cheat products, designing adaptive classifiers and anomaly-detection systems that identify bots, cheaters, and behavioral threats. Builds production-ready ML pipelines, prototypes novel approaches, establishes evaluation standards, improves feature and architecture strategies, and translates technical tradeoffs for cross-functional audiences. The role also mentors junior staff, supports recruiting, and bridges machine-learning research with scalable production systems.

Own machine learning projects from scoping through production, developing reliable multi-agent AI systems. Design agent workflows, safety limits, memory and context management, model routing, evaluation pipelines, benchmarks, metrics, error monitoring, prompt structures, search and indexing systems, and document-processing validation layers. Conduct rigorous ML experiments, test systems end to end, write technical specifications, and communicate findings. The role is full-time and on-site five days per week in New York City.

Build, train, fine-tune, evaluate, optimize, and deploy advanced language models for mission-focused applications. Develop NLP and full-stack software, monitor model performance, manage model versions, and collaborate with cross-functional teams. The role requires strong machine learning and software engineering skills, AWS experience, familiarity with containerization, and a current Top Secret/SCI clearance with favorable polygraph.

Optimize large language model inference across multi-node GPU clusters for production AI agents. Responsibilities include implementing and benchmarking performance improvements, profiling GPU and memory bottlenecks, building evaluation harnesses, integrating model architectures, and applying distributed inference, batching, quantization, and systems-level optimization techniques.

Develop and deploy production-grade AI and machine learning systems using LLMs, RAG, and agentic AI frameworks. Build scalable data foundations, APIs, and cloud infrastructure while implementing monitoring, governance, observability, and MLOps practices. Partner with technical and business teams to deliver AI solutions for lead generation, recommendations, coverage optimization, and client engagement. Evaluate emerging technologies and ensure solutions are reliable, secure, maintainable, and aligned with business outcomes.

Lead the architecture and implementation of enterprise AI platforms, autonomous agent frameworks, ML infrastructure, and multi-tenant orchestration systems. Build scalable REST services, event-driven pipelines, MLOps capabilities, model monitoring, and secure low-latency inference systems using cloud-native technologies. Partner with engineering, product, data, SRE, and compliance teams while providing technical leadership for reliable AI systems serving millions of users and enterprise tenants.

Owns the infrastructure and production lifecycle for product and marketing machine learning models. Responsibilities include real-time model deployment, feature pipelines and stores, monitoring and alerting, CI/CD, retraining infrastructure, marketing model operationalization, data pipelines, ad-platform feedback loops, and dependency management. The role bridges data science and engineering while mentoring teammates and making pragmatic architecture decisions.

Lead the Machine Learning Ranking team, managing data scientists and ML engineers while driving career development, technical quality, and product outcomes. Partner with Product Management and Decision Science to define roadmaps, metrics, and marketplace experiments. Oversee machine-learning pipeline development, model deployment, and performance monitoring. Provide technical guidance on ML and AI applications, evaluate engineering work, and promote scalable, reproducible practices across the team.

Develop software and algorithms for autonomous agricultural and construction equipment. Responsibilities include sensor fusion, mapping, localization, object tracking, perception, vehicle planning, machine learning, data modeling, and complex software architectures. The role collaborates with agile engineering teams and requires expertise in autonomy, AI, machine learning, and software development using C++, Python, and MATLAB. Occasional travel and onsite work are expected.

Leads software engineering for a Machine Learning I/O team, developing secure production code and guiding design, testing, debugging, and operational stability. Drives enterprise adoption and governance of AI-assisted development tools, establishes validation standards for AI outputs, promotes secure and responsible AI practices, and influences technical decisions across teams. Provides technical direction, serves as a subject matter expert, and advocates for scalable cloud-native engineering practices.

AI/ML Engineering Intern supporting Parsons’ Digital Engineering Team with AI/ML application development, Python-based application development, deployment kits, and research and development across requirements management, modeling and simulation, testing, cybersecurity, and systems integration. The intern will learn the Parsons Digital Engineering Framework, participate in Agile ceremonies, and contribute to team projects and intern activities.

Morgan Stanley seeks a Machine Learning Researcher to solve critical problems with innovative research. Responsibilities include applying machine learning techniques and strong communication skills within a collaborative team. Ideal candidates have 4+ years of research experience and proficiency in statistical learning methods.

Evaluate, deploy, and optimize machine learning models for on-device execution on Android phones. Benchmark models on mobile hardware, optimize latency, memory, and power consumption, and apply quantization, compression, and distillation. Integrate models with Android applications, system services, and AI agent runtimes. Build hybrid local/cloud model routing and determine which workflows should run locally versus through cloud inference.

Leads the design, development, deployment, and optimization of scalable AI and generative AI solutions. Translates business needs into secure, production-ready architectures; applies foundation models, NLP, retrieval, vector search, and agentic workflows; and implements MLOps and LLMOps practices. Manages project workstreams, evaluates solution performance, ensures security and compliance, collaborates with stakeholders, and coaches junior team members.

Develop and deploy multimodal machine learning systems for autonomous drug-discovery laboratories. Responsibilities include computer vision, vision-language models, sensor and laboratory data integration, predictive modeling, agentic recovery workflows, evaluation pipelines, and scalable low-latency ML infrastructure. The role collaborates with scientists, robotics engineers, and software engineers to create reliable closed-loop experimentation systems.