Top Machine Learning Engineer Jobs

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models for complex business and national security mission requirements. Integrates data pipelines, evaluates model performance, applies statistical analysis, ensures data and output quality, and transforms prototypes into scalable production solutions. Provides system integration oversight and may lead distributed teams. Requires advanced education or equivalent experience and an active TS/SCI clearance with polygraph.

Leads the technical direction and end-to-end delivery of machine learning and LLM systems for merchant quality, integrity, relevance, and shopping discovery. Responsibilities include architecture, implementation, evaluation, experimentation, monitoring, safety, scalability, and production rollout. The role establishes ML engineering standards, partners cross-functionally, mentors engineers, influences technical roadmaps, and supports future hiring while improving user trust and shopping outcomes.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Develop backend features, automated workflows, APIs, streaming services, and machine learning pipelines using Python, Docker, Kubernetes, and AWS. Collaborate on deep learning and computer vision applications, improve system performance and reliability, resolve bottlenecks, and follow software development and coding best practices in an Agile environment. The role requires an active Top Secret/SCI clearance and supports defense and intelligence missions.

Develop deep learning architectures and end-to-end machine learning pipelines for intelligence and national security applications. Responsibilities include sourcing and curating data, training, evaluating, testing, and deploying computer vision models in AWS using Python and PyTorch. The role also involves producing high-quality software, researching academic literature, collaborating on innovative solutions, and presenting technical work.

Designs, develops, evaluates, and deploys machine learning models for sensor-data event detection, classification, and characterization in classified national-security environments. Builds end-to-end ML pipelines, integrates capabilities into operational software, evaluates failure modes, collaborates with scientists and analysts, and mentors junior developers. The role requires production-quality Python, deep knowledge of modern ML architectures, technical documentation, and active Top Secret clearance with eligibility for SCI and CI Polygraph.

Develop and scale vision-language-action models for robotic manipulation, owning dataset construction, training recipes, distributed GPU infrastructure, evaluation, and on-robot validation. Build reproducible ML pipelines, optimize multimodal data processing and model training, diagnose failures across data and robot behavior, and translate research into production-quality systems. Collaborate across research, robotics, hardware, data, and operations teams while mentoring engineers.

Develop and operationalize machine learning systems powering AI-driven insights from structured and unstructured financial data. Build NLP, generative AI, LLM, summarization, semantic search, classification, and prediction solutions. Responsibilities include model architecture, training, deployment, monitoring, optimization, scalable data pipelines, technical reviews, mentoring, research translation, and cross-functional collaboration with engineering, product, and data teams.

Develop scalable AI-enabled software integrating machine learning models, APIs, data pipelines, retrieval systems, and agentic workflows. Build backend services and cloud-native applications using Python, AWS, Docker, and Kubernetes. Operationalize machine learning capabilities, improve system performance and reliability, and support model inference and information retrieval in mission environments. Collaborate with engineers and researchers while following software development, testing, and quality practices.

Build evaluation infrastructure for Aircall’s AI voice, chat, and messaging agents. Responsibilities include designing benchmarks and metrics, training and fine-tuning TTS, ASR, and speech-to-speech models, developing automated regression and release gates, calibrating LLM-as-judge systems, analyzing failures, monitoring production quality and drift, and evaluating voice-specific scenarios such as accents, noise, barge-in, DTMF, latency, and tool failures.

Design and optimize BEV-based perception models that fuse camera, LiDAR, and radar data for autonomous driving and 3D scene understanding. Benchmark models on large-scale datasets, improve robustness and scalability, collaborate with research and deployment teams, and support production deployment. The role also requires compliance with quality management processes and continuous improvement. Key expertise includes deep learning, LiDAR-based 3D perception, sensor fusion, calibration, synchronization, and computer vision.

Design, build, deploy, and optimize large-scale machine learning models and infrastructure for Uber Maps, including high-throughput, low-latency prediction systems and APIs. Develop distributed ML pipelines, improve routing and ETA applications, explore optimization solutions, and provide technical leadership. Collaborate with product managers, scientists, designers, and engineers to solve complex, real-world mobility problems.

Develop and deploy deep learning models for electro-optical and infrared image processing. Build ML pipelines for data ingestion, feature engineering, training, and inference; collaborate on computer vision solutions; prototype systems for operational integration; troubleshoot and maintain software; and evaluate emerging ML tools and techniques.

Develop and deploy machine learning perception and controls systems for a cardiac surgical robot. Build image-to-robot registration, pose estimation, sensor-fusion, data, simulation, and evaluation pipelines using fluoroscopy and echocardiography. Collaborate with robotics, controls, clinical, and physician teams to improve device models and controllers. Deploy real-time models on robotic systems and produce validation and design documentation for FDA design controls.

Develop and deploy computer vision AI/ML algorithms for remote sensing, automatic target recognition, and multimodal EO/SAR data fusion. Responsibilities include processing large datasets, researching supervised and unsupervised methods, training and optimizing deep learning models, deploying solutions to cloud environments, mentoring engineers, communicating technical concepts, and preparing documentation. The role requires an active TS/SCI clearance and expertise in Python-based machine learning frameworks.

Build multimodal data pipelines combining vehicle sensor and 3D map data for machine learning. Prototype Vision-Language Model workflows, create evaluation datasets, measure precision and recall, improve detection accuracy, and integrate automated labeling into storage and simulation workflows.

Build and optimize machine learning models, ranking systems, retrieval systems, and personalized Search experiences for Spotify. Develop production-ready solutions from experimentation through deployment, explore conversational AI and agentic approaches, evaluate models through experimentation and performance metrics, and contribute to scalable machine learning infrastructure. Collaborate with engineering, product, design, and research teams to improve discovery for millions of listeners.

Design, deploy, optimize, and maintain advanced machine learning models for monetization applications. Translate AI research into scalable production systems, build data pipelines, improve model performance, collaborate with research, engineering, product, and design teams, and contribute to model monitoring, code reviews, and end-to-end problem ownership.

Designs and implements AI-enabled workflows for cyber and intelligence missions, including LLM-based agents, multi-step automation, API and data-store integrations, and production deployments in classified environments. Collaborates with mission users, analysts, developers, and leadership to translate operational workflows into reliable, secure capabilities. Requires Python experience, TS/SCI clearance with polygraph, and relevant AI/ML or software engineering experience.

Build and operate production machine learning models and risk decisioning systems that detect fraud and abuse across Link. Responsibilities include analyzing large datasets, developing features and experiments, deploying and monitoring models, designing data pipelines, and improving real-time payment authorization. The role requires ownership from problem definition through launch and iteration, with collaboration across Engineering, Product, Data Science, and Risk teams.

Leads a three-person computer vision team developing, training, optimizing, and deploying custom YOLO object-detection models for live full-motion video. Architects secure data and training pipelines, establishes annotation standards, manages dataset challenges, and optimizes inference using GPU acceleration. Integrates computer vision systems with video and software platforms while coordinating Agile delivery, technical direction, metrics, and code standards. The role requires advanced Python, deep learning, video processing, model deployment, and team leadership experience.

Optimize machine-learning inference for latency, throughput, and cost. The role profiles pipelines, improves kernels, batching, quantization, compilation, serving, and hardware utilization, while building serving and evaluation infrastructure. It partners with research and product teams to make models affordable at scale and treats compute cost as a core performance metric.

Build foundational MLOps and AI infrastructure for petabyte-scale multimodal media datasets. Responsibilities include data ingestion, storage, indexing, retrieval, model fine-tuning and inference infrastructure, monitoring, validation pipelines, cloud cost optimization, and CI/CD. The role also involves distributed systems, scalable APIs, automation, and hybrid cloud or media-processing infrastructure.

Develop and maintain open-source Python frameworks and ML developer tools, architect scalable libraries, grow early-stage projects, build real-time streaming capabilities, integrate modern frontend technologies, and collaborate with open-source contributors and the broader developer community.

Lead neural decoding and machine learning strategy for brain-computer interfaces, taking algorithms from research through real-time deployment in human clinical studies. Develop closed-loop decoders, calibration and adaptation methods, inference runtimes, and clinical deployment processes. Define data and evaluation standards, integrate models across software, firmware, hardware, and robotics, support regulated development, debug cross-functional issues, and mentor ML engineers.