Top Machine Learning Engineer Jobs

Develop and optimize large-scale ML training systems for autonomous-driving foundation, VLM, and VLA models. Responsibilities include distributed training, mixed precision, reinforcement learning performance, GPU kernel development, profiling, benchmarking, and debugging across compute, compiler, framework, and networking layers. The role collaborates with researchers, data engineers, and hardware engineers to improve training efficiency, scalability, and cost.

Lead ML training performance for large-scale VLM, VLA, foundation-model, and reinforcement-learning systems. Design distributed parallelism, mixed-precision strategies, gradient checkpointing, GPU kernels, profiling workflows, and performance benchmarks. Own RL optimization strategy and roadmap, diagnose bottlenecks across compute and networking layers, partner with research and engineering teams on architecture, and mentor engineers.

The Senior Applied Scientist will design and optimize credit risk models using machine learning, collaborating with cross-functional teams to translate business problems into technical solutions and drive scalable risk decisions.

Develop and implement machine learning models for semiconductor applications, including signal processing, sensor data analysis, and edge computing. Responsibilities include feature engineering, data preprocessing, model evaluation and optimization, ML pipeline development, and collaboration with data scientists and engineers. The role requires Python proficiency, machine learning framework experience, data modeling skills, and knowledge of ML deployment processes.

Leads architecture, development, deployment, scalability, reliability, and operational excellence for enterprise AI and machine learning platforms. Transforms AI research prototypes into production solutions involving LLMs, agentic systems, responsible AI, and advanced inference. Establishes MLOps, observability, governance, incident response, and reliability practices while mentoring engineers and serving as a senior technical escalation point for complex production challenges.

Build and deploy reliable AI agents that automate client workflows and integrate with enterprise systems. Responsibilities include mapping processes, designing automation roadmaps, developing evaluation frameworks and regression tests, monitoring agent performance, instrumenting audit trails, scaling systems to production, and leading workforce adoption and change management. The role also codifies reusable patterns across engagements and requires customer-facing collaboration, production AI experience, Python or TypeScript development, Secret clearance, and up to 10% travel.

Design, develop, test, evaluate, and deploy AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Provide AI/ML engineering support using algorithms, software development, and cloud computing. The role requires five years of AI/ML development, cloud-based deployment, and Python or R experience, plus U.S. citizenship and an active TS/SCI clearance with polygraph.

Build and operate Curinos’ Databricks-native AI and ML platform. Responsibilities include developing deployment tooling, CI/CD pipelines, infrastructure-as-code, observability, governance, model monitoring, LLM evaluation frameworks, and scalable inference systems. The role partners with data scientists and engineers to productionize ML models and AI applications while supporting reliability, incident response, reproducibility, auditability, and operational excellence in a regulated FinTech environment.

Develop, research, optimize, and deploy computer vision AI/ML algorithms for remote sensing, including automatic target recognition and multimodal EO/SAR data fusion. Responsibilities include processing large datasets, training supervised and unsupervised deep learning models, deploying solutions to cloud environments, reviewing research, documenting capabilities, and potentially leading junior engineers. The role requires an active TS/SCI clearance and supports defense and commercial applications.

Develop, train, evaluate, deploy, and maintain computer vision and machine learning models for warfighter performance and visual defect or anomaly detection. Translate operational needs into maintainable ML solutions, design validation strategies, optimize model thresholds, and integrate inference into software systems. Collaborate with data engineers, data scientists, architects, subject matter experts, and product owners while communicating technical findings and tradeoffs.

Build and deploy production AI/ML features across the model lifecycle, including GenAI systems, RAG pipelines, data pipelines, evaluation infrastructure, and MLOps. Apply machine learning to manufacturing, supply chain, computer vision, predictive maintenance, forecasting, inventory, and logistics problems. Collaborate with engineering, manufacturing, and operations teams to deliver measurable outcomes.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Build and deploy production AI agents, agentic workflows, and ML-enabled enterprise features within Java/Spring microservices. Implement evaluation, monitoring, guardrails, safety, testing, and observability for LLM applications. Collaborate with AI scientists, engineers, product managers, and technical specialists to productionize models, integrate RAG and tool calling, prototype solutions, and improve AI capabilities. The role also involves design reviews, agile planning, software quality ownership, and staying current with emerging AI technologies.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Own applied machine learning and LLM systems end to end, translating ambiguous business problems into datasets, models, evaluation frameworks, production pipelines, and deployed services. Build Python-based data and modeling infrastructure, improve LLM extraction and research agents, manage observability and reliability, and communicate technical tradeoffs with stakeholders. The role requires autonomous execution in a lean, onsite New York City engineering environment.

Build production machine learning benchmarks and evaluation systems for foundation models. Own backend infrastructure including data pipelines, execution environments, storage, and orchestration, as well as sandboxed environments for agentic evaluations. Partner with researchers, enterprise customers, and company leadership to scope and deliver technical solutions. Identify scalable evaluation patterns, communicate clearly, and operate effectively in an ambiguous, high-ownership startup environment.

Build and operate Linux-based ML training clusters and inference cloud infrastructure. Responsibilities include access management, hardware validation, job scheduling, container orchestration, CI/CD, observability, incident response, capacity planning, autoscaling, cost tracking, and production security. The role supports heterogeneous accelerators, large-scale training, LLM serving, developer workflows, and reliable model deployments while collaborating across training, inference, product, and technical teams.

Own the architecture and long-term evolution of hybrid, multi-tenant AI compute platforms across bare-metal OpenShift and public-cloud services. Define distributed training networking, GPU utilization and cost models, GitOps governance, model-serving standards, workload placement, identity and security controls, SLOs, disaster recovery, and platform upgrade strategies. Partner with AI, privacy, and security teams to deliver HIPAA-compliant infrastructure for training and inference workloads.

Build, train, evaluate, and deploy production machine learning and LLM systems for legal document intelligence. Develop NLP solutions for information extraction, summarization, search, AI agents, and document analysis. Create scalable ML pipelines, evaluation frameworks, and reliability metrics while optimizing model performance. Collaborate with engineering, product, legal, data, and security teams to deliver secure AI capabilities and translate business problems into production solutions.

Build and improve Kensho’s machine learning platform, tooling, and infrastructure. Develop robust, auditable ML workflows; support model fine-tuning, reinforcement learning, and LLM or agent evaluation; improve production observability; and help detect performance, drift, and decay issues. Collaborate with ML and infrastructure teams, evaluate third-party tools, promote engineering best practices, and enable faster experimentation and reliable product delivery.

Designs, develops, and productionizes machine learning, deep learning, generative AI, LLM, simulation, and optimization solutions. Responsibilities include building and scaling model-serving pipelines, improving accuracy and performance, integrating models into production, developing project architectures, defining ETL requirements, and collaborating with data scientists, engineers, analysts, and stakeholders. The role requires office attendance five days per week in Arlington, Virginia.

Build and scale end-to-end machine learning infrastructure for computer vision and multimodal AI systems. Responsibilities include developing training and deployment pipelines, managing GPU cloud infrastructure, optimizing low-latency model serving, and establishing monitoring and evaluation frameworks for reliable production performance. The role partners with research, computer vision, and product teams to operationalize models across warehouse environments.

Build and deploy production machine learning applications, APIs, inference services, and data platforms. Engineer large-scale pipelines using distributed technologies, data modeling, ETL/ELT, transformation, and data quality practices. Translate defense and mission needs into scalable solutions across cloud, containerized, and GPU/CPU environments while collaborating with engineering, data, and mission teams.

Designs, deploys, and supports production AI/ML solutions across data ingestion, feature engineering, model training, MLOps, monitoring, and lifecycle management. Builds AWS cloud-native applications, generative AI systems, knowledge graphs, and real-time or batch pipelines. Partners with stakeholders on analytical solutions, ensures data quality and AI governance, leads technical design, and mentors engineers.

Train and fine-tune multi-task transformer models for autonomous driving using JAX/Flax on TPUs. Build data and evaluation pipelines, conduct rigorous model evaluations, contribute production-quality code, and communicate findings through design documents and presentations. Collaborate with engineering, data science, and evaluation stakeholders.