Top Machine Learning Engineer Jobs

Designs, builds, and operates production machine learning systems at scale. Develops deep learning models for sequential, multimodal, and spatio-temporal data supporting risk assessment, engagement, and event detection. Partners with research, product, and platform teams to deploy reliable ML products across cloud and edge environments. Responsibilities include software engineering, AWS-based deployment, complex SQL, large-scale data processing, model orchestration, and cross-functional delivery of measurable mobility and road-safety impact.

Lead the architecture, development, and deployment of scalable machine learning systems, including the AI/ML integration connecting Xometry with partner manufacturing platforms. Build real-time ML serving, data pipelines, MLOps, governance, monitoring, and observability. Drive technical roadmaps, optimize latency and throughput, ensure software quality and security, collaborate across engineering and product teams, and mentor engineers while delivering production AI capabilities.

Lead the architecture and delivery of scalable machine learning systems, including real-time model serving, MLOps, monitoring, governance, and automated deployment. Own the AI/ML integration connecting Xometry with partner design and manufacturing platforms. Develop cloud-based production systems, optimize model latency and throughput, establish technical standards, mentor engineers, and collaborate with engineering, product, data science, and business teams.

Designs, trains, evaluates, and deploys AI/ML and deep learning models for mission-critical applications. Analyzes complex datasets, develops predictive algorithms, integrates models into operational systems, and supports testing, validation, deployment, and lifecycle management. Collaborates with engineers, data scientists, and mission stakeholders; documents AI/ML initiatives; researches emerging technologies; and may support LLM, generative AI, NLP, cloud, distributed computing, and MLOps capabilities.

Develop and deploy computer vision modules and machine learning models within AI pipelines. Integrate models with data and software engineering teams, support prototypes, automate data pipelines, build internal tooling, and monitor production performance. The role also involves optimization, infrastructure and security support, staying current with computer vision advancements, and collaborating cross-functionally.

Build and operate Unity’s machine learning infrastructure, including data and feature pipelines, distributed training workflows, orchestration, model serving, monitoring, validation, and testing. The role involves delivering ML platform projects end-to-end, troubleshooting complex pipeline issues, improving reliability and reproducibility, and collaborating with ML engineers, researchers, managers, and partner teams.

Run and evaluate machine learning training experiments for autonomous robots, curate and clean real-world fleet data, improve data-labeling structures, track model performance metrics, and collaborate with Teleoperations to improve data collection. The role focuses on production ML for perception, decision-making, and control, including imitation learning, reinforcement learning, and deployment evaluation on live robotic systems.

Own end-to-end machine learning model quality for autonomous robots, including dataset curation, training, evaluation, and production deployment. Work with multimodal sensor data from cameras, IMUs, LiDAR, and odometry to develop perception, planning, navigation, and manipulation models. Build low-latency, continuous inference systems and collaborate with teleoperations and fleet operations teams. Experience with large-scale multimodal models, distributed GPU training, production ML code, and cluster management is valuable.

Develop and maintain cloud software supporting cyber intelligence and AI/ML capabilities on AWS and large-scale compute clusters. Responsibilities include requirements analysis, Java development, cloud integration, distributed systems, RESTful services, testing, validation, troubleshooting, and sustainment. The role also involves Kubernetes, infrastructure as code, CI/CD, Linux scripting, Hadoop-based big-data technologies, microservices, LLMs, RAG, and security-conscious cloud engineering.

Design, develop, test, and productize AI/ML algorithms. Responsibilities include selecting and analyzing data, training and improving models, evaluating system performance, integrating scalable solutions, validating data and model quality, and identifying distribution issues. The role requires software engineering expertise, machine learning deployment experience, and oversight of iterative verification and validation processes.

Build and deploy scalable AI and machine learning products for clinical research, including patient recruitment, enrollment forecasting, and study feasibility. Develop ML models, LLM-powered agents, data pipelines, and MLOps infrastructure using cloud services and CI/CD automation. Collaborate with cross-functional teams to integrate AI into clinical research workflows, monitor model performance, and advance reliable, unbiased solutions that improve patient outcomes.

Build, evaluate, integrate, and operationalize machine learning, deep learning, and generative AI capabilities for federal mission applications. Responsibilities include developing models and datasets, creating AI services and interfaces, integrating APIs and pipelines, deploying scalable and reproducible solutions, documenting workflows, and improving capabilities through technical and user feedback. The role collaborates with software and data engineers, technical leaders, subject-matter experts, and mission users in a controlled government environment.

Designs, deploys, and supports scalable AI/ML solutions, including predictive models, large language model applications, agentic workflows, and RAG pipelines. Builds production-grade MLOps and DevOps practices covering CI/CD, versioning, testing, monitoring, retraining, and rollback. Manages cloud deployments, observability, security, governance, HIPAA-aligned data practices, API integrations, documentation, and troubleshooting across data, infrastructure, model, and orchestration layers.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Own end-to-end machine learning systems for email threat detection, from data strategy and modeling through production deployment, observability, capacity planning, and rollout. Set architecture and technical direction for model serving, retrieval, GPU workloads, and LLM-based systems. Benchmark alternatives, resolve production incidents, establish reproducible ML standards, mentor engineers, and communicate technical recommendations to product and executive leadership.

Designs, develops, and maintains backend services, REST APIs, data structures, and system architecture for conversational AI and automation applications. Integrates services with front-end systems, scheduling platforms, and EHRs. Implements authentication, authorization, HIPAA-compliant security controls, and secure PHI/PII handling. Optimizes performance, scalability, latency, reliability, and stability while collaborating with product, QA, integration, and engineering teams.

Designs, builds, and operates production machine learning pipelines and platforms, including data ingestion, training, validation, deployment, monitoring, rollback, and incident response. The role emphasizes ML CI/CD, infrastructure automation, reproducibility, observability, model drift detection, cost optimization, and reducing operational toil. It collaborates with data science, engineering, infrastructure, security, product, and SRE teams while contributing to technical roadmaps, mentorship, and the growth of the MLOps practice.

Build and own production-grade machine learning systems spanning data, training, evaluation, deployment, and observability. Translate research prototypes into reliable infrastructure, establish engineering standards, maintain experimentation pipelines, and partner closely with researchers. The role requires strong distributed systems experience, Python expertise, familiarity with PyTorch or JAX, and experience with tools such as Ray, Slurm, or Kubernetes.

Design, build, and deploy production machine learning systems across workflow automation, data enrichment, predictive insights, and agent-based workflows. Own the full ML lifecycle, including data ingestion, model training, evaluation, deployment, and feedback loops. Integrate AI into backend services, APIs, data pipelines, and customer-facing products while shaping architecture and long-term AI strategy. Collaborate with product, engineering, and customers to deliver scalable, reliable AI solutions.

Build and operate production machine learning training systems on Amazon SageMaker using AWS Trainium. The role focuses on optimizing PyTorch code for Trainium hardware, understanding NeuronCore compilation, diagnosing device-level issues, tuning distributed training for cost and throughput, and delivering production pipelines. The engineer will collaborate directly with client and internal teams in a forward-deployed engineering environment.

Explore, prototype, and productionize AI solutions for clinical data review, focusing on LLMs, agentic AI, RAG, and intelligent workflows. Build modular agent systems, APIs, and interfaces; integrate clinical data sources; evaluate and monitor AI performance; and ensure scalable, reproducible, traceable, auditable solutions aligned with regulatory expectations. Collaborate with clinical experts, product managers, and engineering teams while contributing to AI innovation strategy.

Own and scale AI-native platform infrastructure, including Kubernetes clusters, microservices, multi-cloud foundations, networking, authentication, observability, security, cost optimization, and developer tooling. Improve reliability through safe deployments, autoscaling, SLOs, incident response, and standardized infrastructure patterns. Collaborate with researchers and engineers to design, operate, and evolve scalable systems for machine learning, simulation, and robotics workloads.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Own the production runtime for Phare’s machine learning stack by deploying, serving, and scaling models across GPU inference endpoints and batch or streaming workflows. Build progressive delivery pipelines, automated rollbacks, observability, alerting, and incident response processes. Manage model registries, retraining lifecycles, versioning, data lineage, security controls, and reproducible releases using Kubernetes, Terraform, Docker, and CI/CD.