Top Senior Machine Learning Jobs

Design, develop, test, debug, and maintain scalable software applications and prototypes using Java and Python. Work in a Linux full-stack environment, integrate customer-tailored large language models, design accessible interfaces, connect external requirements tools, troubleshoot performance issues, conduct code reviews, support planning, and mentor junior engineers. The role requires extensive software engineering experience, a bachelor’s degree or equivalent experience, DoD8570 IAT2 compliance, and TS/SCI clearance with polygraph.

Lead large-scale machine learning initiatives, develop and deploy production ML models, optimize data pipelines and distributed systems, and build cloud-based ML platforms. The role uses Python, Java, Go, Scala, and modern ML frameworks and requires technical leadership across architecture, modeling, infrastructure, and engineering talent development.

Lead large-scale machine learning initiatives, develop and deploy production ML models, optimize cloud-based data pipelines and infrastructure, and build intelligent systems using advanced algorithms and open-source frameworks. The role involves programming in multiple languages, distributed computing, Kubernetes, and cloud platforms. It also includes mentoring and recruiting engineering talent, evangelizing best practices, and driving technical innovation across the organization.

Lead hands-on development and production deployment of machine learning, large language model, and generative AI solutions. Set technical direction, make architecture decisions, establish MLOps pipelines, monitor model performance, optimize and fine-tune generative models for NLP, and ensure reliability and scalability. Partner with product, data science, engineering, and business stakeholders, communicate technical results clearly, mentor engineers, and guide delivery across multiple workstreams.

Design, evaluate, and deploy machine learning policies for autonomous vehicle interactions, including yielding, lane changes, merges, and cut-ins. Integrate learned models into closed-loop simulation, evaluate safety metrics, and transition models into production. Collaborate across prediction and planning teams while replacing rule-based heuristics with scalable, data-driven solutions. The role requires strong C++ software engineering, machine learning expertise, autonomous systems experience, and cross-functional collaboration.

Develop, train, deploy, and optimize AI/ML models for object detection, data triage, search, inference, automated discovery, and decision-making. Manage model versioning systems, contribute to AI/ML capability development, and identify model vulnerabilities while supporting a government customer.

Build and operate production LLM-powered agents and machine learning systems for customer risk identification and threat detection. Translate security heuristics into agent logic, collaborate with threat researchers, develop data requirements and pipelines, evaluate precision and recall, and deploy solutions through CI/CD. The role also requires production reliability ownership, observability, incident debugging, cloud infrastructure expertise, and senior staff-level technical leadership.

Lead the design, development, deployment, and operation of machine learning models and platforms at scale. Build distributed ML infrastructure, data pipelines, cloud-based architectures, and production services. Develop and test application code, automate deployment, monitor and retrain models, and apply CI/CD, governance, security, and responsible AI practices. Collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Design, build, deploy, and operate machine learning models, platforms, and data pipelines at scale. Develop production ML software, cloud architectures, distributed systems, and automated testing and deployment workflows. Monitor and retrain models, optimize infrastructure, ensure responsible and explainable AI practices, and collaborate with Product, Data Science, and Agile teams to solve complex business problems.

Build and deploy enterprise agentic AI solutions, including agents, MCP tools, plugins, orchestration architectures, evaluation harnesses, and secure APIs. Integrate agents with search and collaboration platforms, instrument production behavior, manage cost and latency, and test against prompt injection and tool vulnerabilities. The role requires backend software engineering, production agent development, MCP experience, LLM evaluation, and AI coding assistant expertise, with preferred experience in managed runtimes, retrieval systems, and enterprise security.

Lead development and deployment of an agentic AI/ML pipeline for identifying, prioritizing, and pricing high-cost healthcare claims. Responsibilities include model development, confidence scoring, HITL safety design, ML lifecycle management, monitoring, workflow automation, responsible AI, and enterprise-scale analytics using Databricks and Snowflake. The role also evaluates emerging AI technologies and supports regulatory-aware automation and healthcare data transformation.

Designs, deploys, and scales AI and machine learning solutions for enterprise healthcare applications. Responsibilities include building and tuning production models, developing MLOps frameworks and deployment pipelines, monitoring model performance, automating workflows, evaluating emerging AI technologies, and ensuring security, scalability, responsible AI, and governance standards. The role collaborates with software engineers, data scientists, and business stakeholders.

Lead development and operation of scalable machine learning training platforms across AWS and Kubernetes. Optimize distributed GPU workloads, training performance, reproducibility, cost, and observability. Build CI/CD automation, standardized containers, governance, and self-service workflows for deep learning and generative AI training. Partner with platform and data engineering teams on security, access, and infrastructure standards. Lead responsible adoption of AI-assisted development tools and coach engineers on secure, compliant validation practices.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Lead software engineers and applied AI/ML practitioners while developing secure, scalable production systems in Java or Python. Integrate LLMs, generative AI, NLP, RAG, and coding assistants into software engineering workflows. Establish responsible AI governance, validation standards, testing, security, and operational practices. Guide system architecture, experiments, algorithm implementation, productionization, code quality, automation adoption, and technical decision-making across global teams.

Build and deploy scalable regulatory model applications and cloud modernization solutions using PySpark, Python, Databricks, and AWS. Design secure, resilient architectures; develop distributed data processing systems; write tested, documented production code; support reliability and observability; and collaborate with product, data, model, and platform teams. The role may also enable AI agent development through LLM workflows, orchestration, evaluation, and guardrails.

Build and support scalable, production-grade regulatory model applications and cloud modernization solutions. Design distributed data processing systems using PySpark, Python, and Databricks; implement secure and resilient AWS architectures; write tested, documented code; and contribute to architecture reviews and release readiness. Collaborate with product, data, model, and platform teams. Experience enabling AI agent development, including LLM workflows, orchestration, evaluation, and guardrails, is a plus.

Build and maintain scalable machine learning infrastructure, including distributed GPU training pipelines, model promotion workflows, monitoring integrations, and ML frameworks. Optimize training throughput, support model deployment and observability, and collaborate with product, architecture, data science, and engineering teams. The role requires strong Python, cloud, ML framework, big data, training and inference system, and enterprise design experience, with preferred expertise in recommendation systems, CUDA, containers, orchestration, and data storage.

Lead the technical direction and implementation of Current’s machine learning platform, covering feature pipelines, training data, model training, serving, monitoring, reproducibility, and production-readiness standards. Establish scalable workflows, improve training-serving consistency, support data scientists, define model governance contracts, evaluate build-versus-buy tooling, and drive adoption across engineering, data science, risk, marketing, and finance.

Leads technical architecture for software, AI/ML, and cloud systems supporting aircraft readiness. Designs, deploys, and improves machine learning models for predictive maintenance; performs data engineering and feature extraction; modernizes legacy applications; builds secure cloud infrastructure, CI/CD pipelines, containers, and infrastructure-as-code solutions. Integrates language models, supports RMF and ATO compliance, enforces security and privacy standards, and mentors junior technical staff while serving as the primary technical contact for government stakeholders.

Develop and deploy physical AI systems on real robots, including learned perception, vision-language-action models, imitation and reinforcement learning, world models, autonomous behavior, and sensor-data pipelines. Prototype approaches in simulation and field environments, optimize models for latency and edge deployment, and collaborate across robotics, controls, embedded, mechanical, and product engineering teams. The role also contributes to long-term Physical AI architecture and research direction.

Designs and scales production-grade data and machine learning pipelines for fraud, payment risk, and abuse prevention. Responsibilities include ETL and feature engineering, model training and deployment, data and model monitoring, incident response, code reviews, mentoring, and collaboration with data scientists, engineers, and product managers. The role supports reliable, explainable decision-making across financial and operational systems.

Develop statistical and machine learning techniques, quantitative models, and scalable automation for evaluation pipelines. Build data-processing infrastructure, improve evaluation efficiency, and solve ambiguous problems across statistics, ML, and large-scale systems. Collaborate with engineers, data scientists, product managers, and leadership while maintaining code quality in a complex codebase.

Design and lead cloud and in-car data mining strategies for autonomous vehicle perception and end-to-end models. Build AI-driven software to accelerate data mining, collection, delivery, and corner-case resolution. Develop operational processes using big data, statistics, and machine learning to ensure high-quality training data from production and collection vehicles. Collaborate with platform teams and balance cost, engineering quality, and delivery timelines.

Leads Aptive’s AI strategy and roadmap while personally delivering production machine-learning systems for forecasting, route optimization, retention, pricing, lead qualification, and customer and technician support. Builds and manages the ML and data science team, partners with data engineering on infrastructure, establishes model deployment and monitoring practices, and oversees responsible AI governance. The role requires measurable business impact, executive-to-engineering communication, and hands-on model and pipeline review.