Top Senior Machine Learning Jobs

Develop and scale distributed machine learning infrastructure for large-scale LLM pre-training. Responsibilities include extending frameworks such as DeepSpeed and FSDP, implementing distributed optimizers, managing multi-node GPU launches, building experiment tracking and monitoring, improving reliability and performance, and maintaining data-loading and checkpointing workflows. The role requires strong Python, systems engineering, distributed training, multi-node debugging, and large-scale pre-training experience.

Build and scale end-to-end reinforcement learning infrastructure for foundation models. Responsibilities include extending distributed training frameworks, integrating rollout and inference systems, implementing reward and trajectory processing, configuring multi-node GPU launches, monitoring experiments, optimizing performance, and developing checkpointing and failure-recovery systems. The role requires strong Python and ML systems engineering experience, hands-on LLM RL pipeline ownership, and expertise with distributed training and policy optimization methods.

Own the applied AI layer of a software platform by building production AI services, retrieval systems, structured extraction pipelines, evaluation suites, guardrails, and agentic workflows. Manage model performance, reliability, latency, cost, and monitoring. Translate product goals and user feedback into measurable experiments and shipped improvements while leading complex projects and maintaining dependable live systems.

Own Arcade’s end-to-end machine learning lifecycle, including training pipelines, model development, evaluation, production telemetry, and releases. Build compact, efficient models for tool selection, routing, retrieval, recommendations, and agent memory. Productionize models for customer VPCs and air-gapped environments through quantization, optimization, packaging, and serving. Set the ML strategy and stack, establish evaluation practices, convert agent traces into training data, and help grow the ML team.

Apply machine learning within complex programs and contracts, using advanced programming languages and frameworks such as Python, Java, C, R, PyTorch, and TensorFlow. The role requires expert knowledge of multiple programming languages, applied machine learning experience, and advanced academic training in artificial intelligence, data science, computer science, deep learning, or mathematics. An active TS/SCI clearance with polygraph is required.

Develop and optimize real-time bidding strategies, online auction mechanics, and machine learning models for advertising campaigns. Responsibilities include event prediction, feature engineering, user embeddings, clustering, fraud detection, cross-device identification, data mining, and production support. The role requires advanced Python, strong algorithms and statistics knowledge, machine learning expertise, and substantial RTB or comparable online technology experience.

Provide SETA systems engineering and acquisition support for AI/ML software and machine learning analytics programs supporting the NRO Ground Enterprise. Responsibilities include AI/ML data analysis, model development and evaluation, software architecture, cloud-system assessments, contractor oversight, technical roadmaps, acquisition documentation, program risk tracking, and technical briefings. The role requires SharePoint development, web technologies, automation tools, agile practices, responsible AI knowledge, and an active TS/SCI clearance with polygraph.

Designs, develops, tests, deploys, maintains, and enhances machine learning and AI solutions for healthcare. Responsibilities include managing ML codebases and pipelines, collaborating with clinical and research teams, implementing scalable workflows on HPC resources, reviewing code, troubleshooting models and data analysis, supporting publications, and developing reliable production services. The role also involves technical leadership, system debugging, on-call support, and cross-functional collaboration within the Innovation Office.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Build and evaluate machine learning models that generate high-definition maps from raw sensor data. Develop statistical evaluation methods, scalable distributed data-processing pipelines, and backend infrastructure for global mapping. Collaborate with onboard engineering teams to improve mapping formats, manage the full modeling lifecycle, and measure system-wide impact. The role emphasizes autonomous vehicle mapping, large-scale systems, quantitative experimentation, and end-to-end software engineering ownership.

Designs, develops, tests, deploys, monitors, and improves machine learning algorithms and production models for business and national security missions. Integrates data pipelines, evaluates model performance, ensures data and output quality, and transforms prototypes into scalable solutions. Requires expertise in machine learning frameworks, statistical analysis, model architecture, software and data engineering principles, and verification and validation. May lead distributed teams and serve as a technical point of contact.

Designs, builds, and operationalizes enterprise AI, generative AI, machine learning, and agentic AI solutions. Develops integrations, orchestration services, reusable tooling, deployment pipelines, monitoring, and governance controls. Partners with architecture, platform, data, and product teams to deliver scalable, secure, compliant AI capabilities across the organization.

Develop and productionize machine learning and AI solutions for enterprise payroll products. Responsibilities include preparing large-scale HR and payroll data, building and evaluating models, prompts, and frameworks, and applying deep learning, NLP, generative AI, and agent technologies. Collaborate with product managers, software engineers, and data scientists to deliver scalable, reliable, and responsible AI capabilities supporting predictive analytics, recommendations, and payroll automation.

Lead forensic technology engagements by designing and deploying machine learning models, statistical algorithms, and data pipelines for digital investigations and electronic discovery. Collaborate with clients to define goals, analyze cloud and enterprise data, develop reusable automation components, communicate findings to executive audiences, and deliver training. The role also involves managing people on high-profile consulting engagements and providing client-facing technical leadership.

Design, train, deploy, monitor, and optimize speech and NLP models for a voice-agent platform. Build MLOps pipelines, APIs, and inference services while developing Android and iOS applications that integrate AI, voice, real-time communication, and backend services. Create frontend prototypes and dashboards, conduct model experiments, evaluate vendors, optimize performance, document systems, and maintain engineering quality across collaborative teams.

Build and operate production machine learning systems for Airbnb Payments, including LLM agents, real-time fraud detection, anomaly detection, personalization, and checkout optimization. Design, train, deploy, monitor, and continuously improve batch and streaming AI applications at global scale. Partner with product, engineering, operations, and data science teams, establish model governance and testing standards, and mentor engineers.

Leads enterprise AI and machine learning strategy, architecture, governance, evaluation, and business value realization. Designs and delivers scalable ML solutions, establishes technical standards and reusable capabilities, influences AI/ML direction across multiple product areas, and guides large cloud-based projects from conception through deployment. Collaborates across teams, shapes technology roadmaps, communicates with nontechnical stakeholders, and drives AI-enabled process transformation using modern software engineering, AWS, DevOps, and distributed delivery practices.

Build and improve machine learning systems for ad ranking, relevance, personalization, and optimization. Responsibilities include feature pipelines, model training and evaluation, active learning, LLM-assisted labeling and synthetic data generation, experimentation, and low-latency production inference. The role partners with product, data, and platform teams to improve advertiser performance and user engagement while maintaining reliability, latency, and data quality.

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.