Top Senior Machine Learning Jobs

Leads the end-to-end development and productionization of AI/ML solutions for trade surveillance and regulatory compliance. Responsibilities include model development, LLM and generative AI experimentation, MLOps pipelines, data engineering collaboration, API integration, monitoring, responsible AI governance, and technical mentorship. The role guides projects from requirements through implementation and delivery while partnering with data science and software engineering teams.

Lead and mentor AI/ML engineers building production-scale machine learning and LLM systems for autonomous medical coding. Define engineering strategy, architect scalable inference and data-processing platforms, establish evaluation and monitoring frameworks, and ensure accuracy, security, privacy, explainability, and regulatory compliance. Collaborate with data science, clinical, product, software, legal, and compliance teams to integrate AI capabilities into healthcare workflows and SaaS products.

Design and develop high-scale distributed AI infrastructure services supporting model training and inference on Azure Machine Learning. Build backend services using C#, Go, Rust, and C++ on Kubernetes and Docker. Operate reliable, secure, low-latency systems with 24/7 availability, provide operational ownership, collaborate with engineers and researchers, and provide technical leadership.

Leads and scales the engineering organization responsible for satellite imagery analytics, machine learning models, and data pipelines. Oversees engineering managers and distributed teams, staffing, delivery, operating models, classified and unclassified environments, foundation-model adoption, cost management, and cross-team commitments. Requires production experience with machine learning, computer vision, or geospatial analytics, plus experience managing engineering leaders and working with export-controlled and government-cleared environments.

Develop embedded machine learning and robotics solutions, including control, computer vision, sensor fusion, detection, and regression algorithms. Responsibilities include writing production Python and C++ code, implementing ROS-based robotic systems, building ML training and deployment pipelines, creating efficient inter-process communication libraries, and optimizing ML software for embedded and hardware-accelerated devices.

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.

Designs, optimizes, and deploys machine learning models for mobile, embedded, and specialized edge devices. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, performance profiling, and production deployment under compute, memory, energy, and connectivity constraints. The engineer will collaborate cross-functionally and address on-device privacy and security, with preferred experience in NPU/DSP toolchains, federated learning, safety-critical deployments, open-source edge AI, and on-device LLM optimization.

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.

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.

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.

Design, develop, and operate scalable Voice AI platforms, including audio acquisition, conversation management, speech generation, model integrations, and voice harnesses. Build distributed systems across regions and datacenters, develop LLM-based applications and agents, improve SRE tooling, enforce software engineering practices, collaborate with customers and product managers, and mentor engineering colleagues.

Design, develop, and deploy scalable Voice AI platforms and distributed systems across regions and datacenters. Build audio acquisition, conversation management, speech generation, model integrations, and voice harness capabilities. Partner with product managers and customers, improve SRE tooling, enforce software engineering best practices, and mentor engineering colleagues. The role requires extensive experience with LLM applications, AI agents, programming, Kubernetes, and highly available workloads.

Develops, trains, deploys, and maintains deep learning models and ML pipelines for electro-optical and infrared image processing. Responsibilities include data ingestion, feature engineering, model validation, inference, rapid prototyping, troubleshooting, system maintenance, and evaluating emerging ML tools. The role collaborates with AI, machine learning, and computer vision engineers supporting space-based defense and intelligence applications.

Leads AI/ML infrastructure networking initiatives by benchmarking switches, validating ASIC and fabric features, and guiding workload deployments. Develops AI-driven network automation using Python, Ansible, SONiC, Junos, and related tools. Tests protocols, congestion behavior, traffic distribution, resiliency, and high-performance network operations. Documents architectures, methodologies, configurations, results, and recommendations for scalable AI/ML network fabrics.

Develop and deploy AI/ML models for consumer banking use cases including lead scoring, next-best-action, retention, cross-sell, and customer prioritization. Define success metrics, analyze complex datasets, communicate recommendations to nontechnical stakeholders, and partner with product, technology, governance, risk, and operations teams. Ensure models meet regulatory and model risk standards through documentation, monitoring, drift detection, and retraining.

Develop and deploy advanced AI and machine learning models for branch network optimization, resource allocation, sales effectiveness, and workforce performance. Work with geospatial, demographic, transactional, and behavioral data; collaborate with business, governance, UX, and technology stakeholders; translate model results into business recommendations; and support regulatory model reviews. Apply techniques including spatial and graph-based modeling, reinforcement learning, optimization, and behavioral modeling.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Develop and deploy reliable machine learning, LLM-powered agent, and agentic workflow systems for government and enterprise customers. Integrate models with tools, APIs, MCP servers, and data sources; build evaluation frameworks for performance, reliability, and safety; improve Chariot, the company’s AI operations platform; and support mission-critical deployments. Collaborate with data scientists, software engineers, DevOps teams, and customers in secure government environments.

Develop and deploy reliable machine learning, LLM, agentic, and data-centric systems for government and enterprise customers. Build and evaluate AI agents, workflows, tool integrations, and retrieval-augmented systems; measure performance, reliability, and safety; and improve the Chariot AI operations platform. Collaborate with customers, data scientists, software engineers, and DevOps teams on mission-critical deployments. The role is fully remote or hybrid in Austin, with travel up to 20%.

Build and operate cloud-based AI and machine learning solutions for enterprise services. Develop efficient, modular software in Go or Python; collaborate with product, development, and quality teams; and own solutions through design, testing, deployment, and delivery. Deploy and scale services in Kubernetes using Helm and CI/CD, maintain observability, resolve production incidents, participate in on-call rotations, and perform root-cause analysis against service-level objectives.

Design, build, deploy, and operate production LLM-powered agents for JPMorganChase’s Commercial and Investment Bank. Responsibilities include Graph RAG, agent memory, context and authorization management, multi-agent workflows, MCP integrations, evaluations, guardrails, cloud deployment, observability, resiliency, and security. The role partners with business, product, engineering, and external stakeholders to deliver supported AI solutions.

Leads AI/ML engineering teams and technical strategy for enterprise-scale healthcare and pharmacy solutions. Oversees development, deployment, governance, MLOps, security, and operations for generative AI, LLM, NLP, predictive analytics, and agentic AI capabilities. Partners with product, engineering, data science, and executives to align technology with business goals, evaluate emerging technologies, improve automation, and develop engineering talent.

Leads the design, deployment, and optimization of production AI and machine learning systems, including LLMs and generative AI. Architects scalable solutions, drives MLOps, manages model evaluation and monitoring, and improves system reliability and cost efficiency. Collaborates with product, data, and platform teams while providing technical leadership through design reviews, mentorship, and AI strategy.

Provides technical leadership for enterprise AI and machine learning engineering, including data integration, governance, architecture, model development, deployment, monitoring, and innovation. Partners with data scientists, engineers, business teams, and enterprise data warehouse teams to build cloud-based big data solutions using Google Cloud and AWS. The role supports advanced analytics, communicates roadmaps and business value, and develops scalable tools and methodologies for data science initiatives.