Top Machine Learning Jobs

Design, build, deploy, and scale production machine learning systems across data processing, training, evaluation, inference, and operations. Convert research innovations into reliable product capabilities, develop reusable infrastructure, resolve performance and scalability challenges, contribute to architecture, and coordinate complex technical projects. The role also emphasizes security, privacy, AI safety, Responsible AI, cross-functional collaboration, and mentoring engineers.

Develop and operationalize AI/ML, computer vision, and automatic target recognition solutions using multi-INT and geospatial data. Build MLOps pipelines for continuous model integration and deployment, create analytics products, and apply best practices for version control, reproducibility, and governance. Collaborate with engineers, clients, and users while designing, testing, and deploying scalable models and software in cloud or hybrid environments.

Leads the development and production deployment of advanced machine learning and generative AI systems across NLP, speech, recommendations, information retrieval, and agentic workflows. Designs LLM-powered applications, fine-tunes models, applies reinforcement learning, evaluates performance, and implements safety, privacy, and governance controls. Collaborates cross-functionally to deliver scalable, reliable solutions for banking use cases and measurable customer and business outcomes.

Build production AI and machine learning software for large-scale computing and AI infrastructure. Responsibilities include developing anomaly detection, forecasting, root cause analysis, LLM workflows, agentic systems, and operational analytics using Go, Python, and React. The role spans data engineering, model development, evaluation, deployment, monitoring, and continuous improvement while collaborating with engineering, architecture, product, and customer teams.

Designs, deploys, and maintains production AI/ML solutions for document digitization using generative AI and LLMs. Responsibilities include model lifecycle management, scalable Python and cloud pipelines, AWS infrastructure, monitoring, validation, security, and continuous improvement. The role also involves dashboard development, modernization of legacy document workflows, cross-functional collaboration, mentoring junior engineers, and adoption of emerging AI technologies.

Lead AI solutions development in finance using statistical methods and machine learning. Mentor, implement models, ensure compliance, and collaborate for effective tool integration.

Lead the architecture and development of large-scale machine learning systems, distributed training infrastructure, ranking systems, and production ML services. Drive complex initiatives from concept through deployment, establish engineering standards, partner with product and engineering leadership, and mentor data scientists and engineers. Build reliable, low-latency, high-availability AI platforms using advanced ML frameworks, accelerators, and big data technologies.

Build, deploy, scale, and monitor machine learning models and production services. Responsibilities include model development, feature selection, data pipeline construction, distributed data processing, cloud and Kubernetes operations, CI/CD automation, testing, model governance, responsible AI, and collaboration with product and data science teams.

Designs and deploys enterprise-scale AI/ML and Generative AI solutions. Responsibilities include AI/ML architecture, MLOps, CI/CD, model monitoring, drift management, APIs, microservices, cloud and containerized deployments, technical design reviews, reusable engineering standards, and mentoring. Partners with data scientists, engineers, product, security, and operations teams to deliver production-ready systems.

Build and scale Airwallex’s machine learning platform for risk and fraud decisioning. Responsibilities include designing data and model infrastructure, productionizing training and serving workflows, optimizing GPU-based training and inference, enabling experimentation, and partnering with engineers, data scientists, product managers, and risk specialists. The role is based in Seattle with regular office work.

Leads Adyen’s Credit Risk Modeling team, overseeing the design, productionization, deployment, monitoring, and improvement of machine learning models and rule-based decision systems for credit underwriting. The manager provides hands-on technical leadership, mentors machine learning scientists and data engineers, shapes architecture and product roadmaps, collaborates cross-functionally, recruits team members, and builds an inclusive, high-performing engineering culture.

Develop, scale, and maintain complex AI and natural language processing models for Verneek’s AI platform. The role involves deploying large-scale AI/NLP systems to production, solving advanced machine learning research problems, and working with frameworks such as PyTorch. Engineers may also contribute to natural language understanding, dialogue systems, semantic parsing, transfer learning, and limited-data learning in an early-stage startup environment.

Leads the design, development, deployment, and support of enterprise applications enhanced with AI and machine learning. Establishes MLOps, CI/CD, testing, monitoring, governance, and reusable engineering practices. Partners with product, data, architecture, and business teams to deliver scalable digital products, improve engineering quality and performance, ensure cybersecurity and responsible AI compliance, evaluate emerging technologies, and mentor engineers.

Collaborate with scientific teams to identify high-impact AI opportunities, define future-backward implementation plans, assess data needs, and build and evaluate machine learning solutions. The intern will use scalable GPU resources, develop benchmarks, iterate across model approaches and scientific domains, and produce comprehensive analyses supporting new company creation.

Develop an end-to-end evidence management and synthesis system using data pipelines, publication search and deduplication, machine learning, and LLM workflows. The intern will integrate AI tools, manage data, collaborate with cross-functional stakeholders, and present findings. Required skills include Python, SQL, machine learning concepts, systems design, communication, and current enrollment in a relevant bachelor's or master's degree program.

Build and evaluate secure machine learning systems, investigate AI vulnerabilities and adversarial attacks, develop defensive techniques, prototype emerging technologies, and transition AI capabilities to government sponsors. Collaborate with researchers and technical teams, communicate complex concepts, mentor junior staff, and support applications including computer vision, NLP, planning, robotics, and trustworthy AI.

Lead automated recommendation research and development through agentic AI, building LLM agents that generate hypotheses, implement changes, run experiments, and learn from results. Develop generative retrieval models using Semantic IDs, take research prototypes into production, improve ML infrastructure integrations, and evaluate system quality and impact. Provide hands-on technical leadership through architecture, implementation, reviews, documentation, mentorship, and cross-functional collaboration.

Develop and deploy machine learning models, datasets, algorithms, and training recipes to improve AI safety for multimodal LLMs and agentic systems. Work across LLM security, frontier risks, agentic safety, multi-turn evaluation, content safety, hallucinations, and fairness. Apply post-training methods including SFT and RLHF/RLAIF, conduct safety evaluations and research, and collaborate cross-functionally with engineering, data science, and research teams.

AI Interns support real-world data and machine learning projects using Python, Jupyter Notebooks, and SQL. Responsibilities include data analysis, basic machine learning, research, project documentation, and progress updates. The role is designed for students pursuing degrees in mathematics, computer science, engineering, or related fields and provides hands-on experience during a full-time summer internship.

Develop real-time intraoperative intelligence software for robotic surgery. Responsibilities include designing and implementing computer vision and machine learning algorithms, developing user-facing software, translating clinical requirements into technical specifications, supporting integration and testing, producing formal documentation, reviewing patents, and contributing to system architecture. The role requires expertise in computer vision, machine learning, GPU optimization, and production software development using C++ or Python.

Develop and demonstrate AI and data solutions for aerospace engineering projects. Responsibilities include designing architectures, building proof-of-concepts and machine learning systems, writing code, creating demos, supporting cloud and data engineering initiatives, and delivering production-ready solutions. The intern will collaborate cross-functionally, participate in design thinking, gain mentorship, and work on real-world projects using technologies such as Python, cloud platforms, machine learning, LLMs, and computer vision.

Leads the design, development, deployment, and automation of production-grade machine learning and optimization solutions. Builds scalable data and ETL pipelines, internal ML and ERP tools, and reusable engineering frameworks. Ensures reliability through software engineering and DevOps practices, evaluates emerging technologies, documents architectures, collaborates cross-functionally, and mentors team members. Work includes AWS, Databricks, optimization modeling, web applications, databases, orchestration, containers, and machine learning frameworks.

Build and deploy production search and AI systems using Python, React, cloud-native services, retrieval and ranking pipelines, ML inference, Elasticsearch, vector databases, and LLM integrations. Develop scalable APIs, microservices, frontend components, and real-time GCP architectures. Own testing, observability, CI/CD, infrastructure, monitoring, and on-call responsibilities while contributing to search relevance tuning, experimentation, technical design, code reviews, and mentorship.

Lead Treeswift’s machine learning team and technical direction, improving model quality, training and inference pipelines, MLOps, and edge deployment. Develop models using sensor data such as images, video, LiDAR, and point clouds. Establish dataset curation, evaluation, and feedback processes, collaborate with software, robotics, hardware, and product teams, and mentor engineers across distributed offices.

Design, develop, and operate scalable VoIP infrastructure supporting AI-driven voice workloads. Integrate telephony systems, LLM applications, and real-time communication platforms while improving observability, reliability, deployment, and SRE practices. Build tested, maintainable software across telephony and AI layers, support production environments, collaborate with engineering and product teams, and mentor colleagues.