Top Senior Machine Learning Jobs

Build and operate Wealthsimple’s ML and LLM platform infrastructure. Responsibilities include designing model-routing systems, scaling GPU workloads on Kubernetes, managing LLM serving with vLLM and related tools, developing evaluation and observability frameworks, enabling self-serve ML tooling, and optimizing inference cost, latency, reliability, and model performance across cloud and self-hosted environments.

Designs, develops, fine-tunes, evaluates, and deploys machine learning and generative AI models, including LLMs, RAG systems, embeddings, and agents. Builds end-to-end ML pipelines, integrates Google Cloud data services, implements monitoring and drift detection, and applies responsible AI, security, governance, and compliance practices. Partners with stakeholders, data engineers, platform teams, DevOps/SRE, and junior engineers to deliver reliable AI solutions.

Develop and deploy enterprise-scale AI and machine learning solutions for secure government and intelligence missions. Build LLM-powered workflows, agent-based automation, data pipelines, and production ML systems. Integrate AI into operational platforms, conduct model evaluation and testing, optimize workflows, and ensure scalability, reliability, security, and governance. Collaborate with mission users, analysts, software developers, and leadership while providing technical guidance and mentoring.

Design, build, optimize, and scale machine learning infrastructure for training, evaluating, deploying, and serving large language models. Develop reusable workflow automation abstractions, distributed training and inference pipelines, model evaluation and monitoring frameworks, and latency optimizations. Collaborate with machine learning, data infrastructure, analytics, engineering, and product teams while establishing best practices for ML and data engineering.

Contribute to machine learning model design, training, and evaluation for fraud detection. Communicate findings, develop metrics, and support production ML workflows while ensuring compliance and model governance.

Design, develop, and deploy AI-powered healthcare solutions using Java and Spring Boot microservices. Implement HL7 FHIR and Da Vinci interoperability standards across prior authorization, claims, eligibility, and provider domains. Build Kafka-based event-driven services, integrate relational databases, automate CI/CD, deploy with Docker and Kubernetes on AWS or Azure, and establish monitoring using Splunk, ELK, Prometheus, and Grafana.

Design and develop a high-performance, multi-tenant ML training platform for autonomous vehicle systems. Responsibilities include building orchestration, control plane, observability, data and model pipelines, performance optimization, profiling tools, and scalable infrastructure. The role provides technical direction, collaborates across ML, infrastructure, autonomy, and safety teams, and promotes engineering excellence and reusable tooling for machine learning engineers.

Design and maintain data pipelines and BI infrastructure, ensure data quality, optimize SQL and transformations, build and validate predictive and ML models, support deployment and BI integration, and collaborate with stakeholders to deliver scalable analytics solutions.

Develop and deploy geospatial AI/ML capabilities for DoD and intelligence customers. Apply machine learning, data analytics, computational modeling, clustering, and statistical methods to structured and unstructured data to generate predictive and prescriptive intelligence insights. Integrate diverse data sources, optimize analytical workflows, support operational missions, lead projects, and translate research and commercial practices into actionable solutions for intelligence analysis and resource allocation.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.

Build and operate production retrieval-augmented generation systems, including embeddings, vector retrieval, grounded language-model generation, citation validation, prompt and schema design, model packaging, serving, rollback, and telemetry. Develop bounded model assistance for narrative extraction with provenance and rule-context tracking. Maintain resilient serving paths in secure government cloud environments, including restricted or air-gapped deployments.

Leads a team developing and shipping state-estimation systems for autonomous driving, including multi-object tracking, road-property estimation, and collision-avoidance functionality. Responsibilities include designing classical and machine-learning solutions, analyzing system performance and failures, collaborating with robotics and systems teams, validating solutions, and deploying production-grade perception systems to vehicles.

Architect and operate infrastructure for distributed large-model training and deployment across accelerator fleets. Build scheduling tools, high-volume multimodal data pipelines, model compression and compilation workflows, and observability systems. Optimize compute, storage, and memory performance while partnering closely with researchers to accelerate experimentation and production deployment. The role requires technical leadership, PyTorch expertise, distributed training experience, ML platform ownership, multimodal systems experience, and on-site work five days per week.

Designs, develops, deploys, and operationalizes machine learning solutions using Python, PySpark, Azure technologies, and large-scale telemetry datasets. Builds scalable data pipelines, trains and evaluates models, applies MLOps practices, and communicates insights through dashboards and presentations. Collaborates with data engineers, data scientists, architects, product owners, and business stakeholders to deliver enterprise machine learning solutions.

Lead hands-on development and deployment of large multimodal machine learning models for physical hardware. Responsibilities include data preparation, neural network training, evaluation, real-world testing, hyperparameter experimentation, GPU infrastructure design, and improving model reliability, latency, and experimentation throughput across multiple workstreams.

Build and operate production machine learning systems and MLOps infrastructure for financial services. Responsibilities include developing feature and training pipelines, model-serving systems, CI/CD, automated retraining, monitoring, alerting, observability, and progressive model rollouts in GCP and Kubernetes. The role partners with data scientists and business teams to improve reliability, performance, scalability, cost efficiency, and deployment velocity while applying AI coding agents to infrastructure and ML workflows.

Design, train, and deploy machine learning models and perception software for autonomous aircraft. Develop real-time AI algorithms using deep learning, 3D computer vision, and sensor fusion for navigation and detect-and-avoid systems. Build model training, evaluation, data management, simulation, and analysis tooling while collaborating with software, controls, sensing, and flight engineering teams. Optimize production autonomy systems and mentor engineers through strong software practices.

Set technical vision for Pinterest’s Product ML Infrastructure, leading distributed model training, fine-tuning, evaluation, and high-scale CPU/GPU inference. Improve GPU efficiency, data loading, execution, kernels, memory, compilation, quantization, scheduling, and capacity. Build reliable, observable training and serving platforms, drive architecture decisions and migrations, partner with AI/ML teams, mentor senior engineers, and establish operational standards across the organization.

Develop and operationalize AI/ML capabilities for secure Department of Defense mission systems. Responsibilities include building agentic workflows, production APIs, MLOps pipelines, model validation and monitoring processes, and integrations with MSS Foundry and Databricks. The role requires production AI/ML deployment experience, Python proficiency, lifecycle management expertise, and collaboration with engineering and mission teams. Candidates must hold an active TS/SCI clearance and have at least five years of relevant experience.

Own the end-to-end machine learning lifecycle, including data preparation, dbt pipelines, feature engineering, model development, deployment, monitoring, and evaluation. Apply ML to risk assessment, cost prediction, image analysis, and business operations. Contribute to architecture and strategy while partnering with Engineering, Product, Operations, and Business teams. Build scalable solutions using Python, AWS, Kafka, Django or FastAPI, PostgreSQL, and GitHub Actions.

Lead the technical strategy, architecture, and delivery of large-scale machine learning systems for autonomous driving. Develop end-to-end AI methods, define dataset and auto-labeling requirements, mentor senior engineers, influence multiple teams, and integrate autonomy technology across Uber. The role requires expertise in Python, Linux, PyTorch, autonomous systems, and scalable ML infrastructure, with preferred experience in C++, CUDA, ROS, foundation models, and high-performance optimization.

Develops machine learning software algorithms, supports data pipeline design, integrates model pipelines, monitors data quality, and engages with stakeholders for analytic solutions.

Develops AI/ML algorithms and ETL infrastructure supporting LLM, NLP, predictive analytics, and other customer solutions. Builds and maintains data pipelines, curates evaluation data, improves scalability and automation, and collaborates with researchers and developers using Agile methods. The role requires Python-based data engineering, machine learning experience, and an active TS/SCI clearance, with preferred experience in cloud, containers, distributed computing, reinforcement learning, GenAI operations, and GPU programming.

Designs, develops, deploys, and integrates production-grade AI/ML systems for national security missions. Responsibilities include building NLP, LLM, computer vision, and deep learning models; developing scalable data pipelines, APIs, microservices, and cloud or edge services; optimizing and evaluating models; and applying MLOps, security, reliability, and software engineering practices. The role collaborates with mission experts and engineering teams and requires an active TS/SCI clearance.

Build and deploy production machine learning systems across computer vision, multimodal modeling, structured data, and agentic AI. Own projects from problem definition through monitoring, develop data and evaluation pipelines, establish feedback loops, and translate experiments into practical technical recommendations. Collaborate with product, engineering, operations, annotation teams, and domain experts while delivering maintainable code, documentation, and scalable ML solutions.