Top Machine Learning Engineer Jobs

Design, optimize, and deploy ML models for resource-constrained edge devices. Focus on model compression, quantization, hardware-aware optimization, performance profiling, and production deployment to mobile/embedded platforms while addressing on-device privacy and security.

Design, train, test, deploy, and maintain machine learning models for DoD-focused solutions. Collaborate with data engineers, data scientists, and product owners to implement ML, deep learning, computer vision, and generative AI use cases while advising clients on algorithms, tools, and deployment.

The ML Engineer will design, develop, and implement machine learning models, manage ML pipelines, integrate APIs, and collaborate with teams to deploy solutions.

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

Design, optimize, and deploy ML models for resource-constrained edge devices. Focus on model compression, quantization, pruning, hardware-aware optimization, and production deployment to mobile and embedded platforms. Collaborate cross-functionally to ensure performance, profiling, privacy, and security for on-device inference.

Build and improve state-of-the-art agent systems: design RL/RLHF learning pipelines, create custom environments, set up robust evaluation, train smaller task-specific models, leverage open-source text and image models, and help shape technical and hiring strategy alongside founders.

As an ML research engineer, you will develop systems using language models, curate datasets, set evaluation metrics, and scale semantic search processes.

Build and scale LLM-based, agentic customer-engagement systems: architect, deploy, and monitor production ML features; finetune models; create evaluation frameworks, feedback loops and HITL workflows; run A/B tests; optimize latency, cost, and reliability; and mentor the team.

Design, implement, deploy, and maintain LLM-based AI systems for workflow automation, process and entity extraction, dialog/agent functionality, and text generation. Work includes research, model evaluation, production deployment, monitoring, statistical analysis, and collaboration with engineering and product teams using cloud infrastructure (AWS/GCP).

Lead architecture and maintenance of end-to-end traffic classification ML systems for adversarial domains. Own model lifecycle from offline evaluation to millisecond-edge deployment, build certified offline-to-online data pipelines, establish rigorous evaluation and leakage-prevention, optimize inference under strict latency/cost budgets, integrate scoring with mitigation workflows, and provide cross-functional leadership and mentorship.

Lead development and productionization of LLM quantization and deployment pipelines (PTQ, QAT, mixed-precision, INT8/FP4), build robust export/calibration/benchmarking/validation tooling, collaborate with research, field-testing, simulation and vehicle software teams, curate evaluation datasets and metrics, analyze numerical errors and performance trade-offs, and develop orchestration workflows for on-vehicle inference and model sign-off.

Design and scale data pipelines and automated closed-loop systems for continuous improvement of perception models. Deploy active learning, curate and prune large datasets, develop evaluation metrics and ground-truth‑free performance measures, and build robust mining, training, and evaluation pipelines for rare, safety-critical scenarios while collaborating with research and operations teams.

The Data Scientist/ML Engineer designs and deploys Machine Learning algorithms, assists federal customers, and contributes to new feature design.

Seeking a Machine Learning Engineer to apply AI technologies in high frequency trading, focusing on automation and collaboration with AI research.

As a Generative AI & Machine Learning Engineer, you'll manage AI/ML product development, support model lifecycle, and ensure solutions meet customer needs, fostering collaboration and innovation.

Develop and deploy 2D traffic sign detection models for autonomous driving. Responsibilities include scenario analysis, dataset curation, model training and evaluation, optimization and quantization, building evaluation pipelines, long-tail data mining, and validating performance on edge/onboard platforms while collaborating across data, infrastructure, and deployment teams.

Lead design, training, and deployment of custom deep-learning models for Radar and EO/IR semantic segmentation, detection, and temporal tracking. Build multi-stage pipelines, train on large proprietary datasets, optimize C++ inference for resource-constrained ARM64/CPU edges, advance sensor-fusion with AIS and cartography, integrate ML outputs into multi-target tracking/SLAM, and contribute to MLOps for continuous training and evaluation.

Own end-to-end matching and ranking systems for out-of-home advertising: design candidate generation and re-ranking models, build and manage data warehouse and pipelines, implement evaluation and monitoring, publish low-latency ranking APIs, and maintain model observability and retraining workflows.

You will design and develop machine learning systems for an AI-driven dating platform, focusing on personalized matching algorithms and natural language interactions.

Design, train, and deploy reinforcement learning systems for high-impact decision problems. Build and evaluate RL policies at scale, handle reward modeling and simulation, and productionize stable, safe RL solutions.

Build systems for Mercor's hiring engine, including pipelines for indexing, sourcing, scoring, and conversion. Work with ML and backend technologies.

As a Machine Learning Engineer, you will design and train perception models for robotics, focusing on 3D perception systems. You will tackle complex physical challenges, optimize models for embedded hardware, and collaborate with cross-functional teams.

Develop and scale learning models for behavior cloning and reinforcement learning. Build data pipelines and evaluate model performance for integration into autonomous systems.

The role involves building and deploying AI-native data solutions, focusing on LLM and agent development, automation workflows, and data analysis, while leveraging cloud technologies.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.