Top Senior Machine Learning Jobs

Leads enterprise AI architecture for generative AI, agentic systems, traditional machine learning, and computer vision. Designs secure, scalable platforms; establishes MLOps and LLMOps standards; prototypes and writes production code; leads client solutioning and pre-sales; ensures regulatory compliance; mentors architects and engineers; and governs technical standards across strategic engagements.

Leads the technical direction and end-to-end delivery of machine learning and LLM systems for merchant quality, integrity, relevance, and shopping discovery. Responsibilities include architecture, implementation, evaluation, experimentation, monitoring, safety, scalability, and production rollout. The role establishes ML engineering standards, partners cross-functionally, mentors engineers, influences technical roadmaps, and supports future hiring while improving user trust and shopping outcomes.

Develop and operationalize machine learning systems powering AI-driven insights from structured and unstructured financial data. Build NLP, generative AI, LLM, summarization, semantic search, classification, and prediction solutions. Responsibilities include model architecture, training, deployment, monitoring, optimization, scalable data pipelines, technical reviews, mentoring, research translation, and cross-functional collaboration with engineering, product, and data teams.

Design, train, deploy, and optimize computer vision models for automated vehicle damage detection. Build high-throughput containerized microservices using REST/gRPC, maintain Kafka data pipelines, and own the full machine learning lifecycle from experimentation through production optimization. Collaborate with stakeholders to develop scalable, reliable inspection solutions and lead technical design efforts.

Own the ML platform powering production training pipelines, model serving, deployment, orchestration, observability, and multi-region scaling. Drive major infrastructure initiatives, improve reliability and cost efficiency, lead incident response, collaborate across teams, and set the technical vision for ML operations. The role is hands-on, requiring production distributed-systems expertise, Kubernetes and cloud infrastructure knowledge, technical leadership, mentoring, and strong communication.

Own and evolve Braze’s ML platform for production-scale training, deployment, serving, observability, reliability, and cost efficiency. Lead complex infrastructure initiatives, including multi-region model serving, customer-specific model pipelines, CI/CD tooling, orchestration, and cloud identity. Set technical direction, manage incidents, collaborate across teams, improve engineering quality, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.

Own and evolve Braze’s ML platform for training, deploying, serving, and monitoring models at global scale. Lead complex infrastructure initiatives, multi-region model serving, CI/CD, orchestration, reliability, cost optimization, and incident response. Set technical direction, improve production quality, collaborate across teams, mentor senior engineers and data scientists, and connect platform decisions to business outcomes.

Develop and deploy deep learning models for electro-optical and infrared image processing. Build ML pipelines for data ingestion, feature engineering, training, and inference; collaborate on computer vision solutions; prototype systems for operational integration; troubleshoot and maintain software; and evaluate emerging ML tools and techniques.

Leads architecture and delivery of an AI-enabled claims orchestration platform connecting AI agents, rules, workflows, RPA, validation, observability, and human escalation. Builds reusable integration standards and durable execution patterns, partners with technology and business stakeholders, and recruits and coaches a team of 5–20 AI/ML and automation engineers. Remains hands-on while translating emerging AI capabilities into secure, scalable production solutions for claims operations.

Develop and deploy machine learning perception and controls systems for a cardiac surgical robot. Build image-to-robot registration, pose estimation, sensor-fusion, data, simulation, and evaluation pipelines using fluoroscopy and echocardiography. Collaborate with robotics, controls, clinical, and physician teams to improve device models and controllers. Deploy real-time models on robotic systems and produce validation and design documentation for FDA design controls.

Develop optimized GPU algorithms and high-performance machine learning solutions for vector search, databases, and data processing. Responsibilities include designing, implementing, benchmarking, debugging, and optimizing C++, CUDA, and Python libraries; developing clustering and visualization algorithms; collaborating cross-functionally; and contributing to open-source projects such as cuML, cuVS, and RAFT.

Develop and evaluate multimodal deep learning and reasoning models for Edge AI, covering data collection, annotation, preprocessing, training, post-training, reinforcement learning, knowledge distillation, and rigorous evaluation. Build agentic and multi-agent reasoning systems, modify transformer architectures, and prototype in Python using PyTorch or TensorFlow. Document findings through reports, publications, and conference submissions while exploring emerging approaches such as world models, JEPA, diffusion, and deep research automation.

Lead the training, optimization, scaling, and debugging of large language and multimodal generative models for scientific discovery. Develop efficient training workflows, identify performance bottlenecks, guide technical strategy, mentor engineers, and collaborate with researchers and domain experts to translate advanced machine learning research into impactful solutions.

Build and operate production machine learning models and risk decisioning systems that detect fraud and abuse across Link. Responsibilities include analyzing large datasets, developing features and experiments, deploying and monitoring models, designing data pipelines, and improving real-time payment authorization. The role requires ownership from problem definition through launch and iteration, with collaboration across Engineering, Product, Data Science, and Risk teams.

Design, evaluate, and deploy applied machine learning solutions across language, vision, recommendation, and structured data. Build scalable training and inference pipelines, optimize models for production performance, develop data-quality and monitoring tooling, and apply safety, fairness, and reliability evaluations. The role combines research, experimentation, production engineering, cross-functional collaboration, technical communication, mentoring, and influence over the AI roadmap.

Optimize end-to-end AI training and inference systems for throughput, latency, and cost. Responsibilities include GPU and kernel optimization, distributed training, model compression, attention and KV-cache optimization, compiler tuning, data pipeline optimization, benchmarking, hardware evaluation, and production performance improvements. The role requires Python and C++ expertise, strong knowledge of GPU architecture and distributed systems, and collaboration with engineering teams to deliver measurable AI workload gains.

Build and operate large-scale data systems supporting AI training and evaluation pipelines. Responsibilities include data ingestion, transformation, quality assurance, lineage, dataset versioning, reproducibility, and high-throughput delivery to training jobs. The role requires operating petabyte-scale storage and pipelines, applying distributed systems and data modeling expertise, and following strong software engineering practices including testing, CI/CD, and code review. Collaboration across teams and support for multimodal and frontier model training pipelines are also expected.

Design, develop, deploy, and optimize machine learning models and production services for healthcare products. Build data pipelines, feature workflows, and training datasets; apply testing, validation, MLOps, cloud infrastructure, and monitoring practices. Collaborate with technical and non-technical stakeholders, improve model performance, develop reusable tools and frameworks, evaluate emerging AI technologies, support incident remediation, and provide technical guidance. The role requires production machine learning experience and proficiency in Python, SQL, and Unix environments.

Lead neural decoding and machine learning strategy for brain-computer interfaces, taking algorithms from research through real-time deployment in human clinical studies. Develop closed-loop decoders, calibration and adaptation methods, inference runtimes, and clinical deployment processes. Define data and evaluation standards, integrate models across software, firmware, hardware, and robotics, support regulated development, debug cross-functional issues, and mentor ML engineers.

Lead the technical ML strategy and own scalable machine learning pipelines across Salesforce security and compliance. Develop automated retraining, model optimization, MLOps, infrastructure-as-code, CI/CD, monitoring, and drift detection solutions. Productize generative AI and agentic workflows, collaborate across data and product teams, ensure governance and regulatory compliance, deliver measurable business outcomes, and mentor junior engineers.

Design, productionize, and operate machine learning models and rule-based decision systems for credit underwriting. Build scalable pipelines for feature engineering, training, validation, deployment, monitoring, and continuous improvement. Optimize model performance, collaborate with software, credit, product, risk, and data teams, and promote strong engineering and MLOps practices across production ML systems.

Lead the end-to-end development and operation of machine learning systems supporting customer retention, ranking, matching, recommendations, pricing, and lifetime value growth. Build scalable data pipelines and ML infrastructure across batch and real-time environments, while implementing model monitoring, observability, deployment, and performance optimization. Collaborate across engineering and science teams and promote strong software engineering practices.

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.

Design, train, evaluate, and productionize machine learning models (including embeddings and foundation models) on large-scale data. Partner with product and engineering to run experiments, improve customer experiences, and deploy scalable solutions using Spark and AWS.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.