Top Machine Learning Engineer Jobs

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

Design, build, and operationalize production-grade NLP and GenAI models (summarization, semantic search, classification, prediction). Develop scalable data pipelines and distributed systems, deploy models with cloud-native tooling, mentor engineers, collaborate cross-functionally, and ensure model monitoring, evaluation, and responsible-AI practices to deliver business-aligned insights.

Lead architecture and development of distributed, large-scale ML systems for Merlin Bird ID and related platforms. Build training pipelines, production deployments, APIs, and front-end integrations. Mentor engineers, set technical direction, ensure scalability, performance, reliability, and collaborate with engineers, scientists, and domain experts to deliver global conservation technology.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.

Optimize large-scale ML training and inference workloads by profiling bottlenecks, implementing efficiency improvements (parallelism, mixed precision, compilation), building observability and benchmarking tools, and collaborating with research and platform teams to scale models and increase training throughput.

Own end-to-end CTR/CVR prediction models in a real-time bidding system (sub-100ms): modeling, feature pipelines, offline and online evaluation, A/B testing, experiment debugging, and post-launch ops. Hunt for new predictive signals, build tooling for experiment debugging and signal discovery, collaborate with data and pipeline teams, and set technical direction for NEXT's ML stack.

The Staff Software Engineer will build and scale machine learning systems for personalization, collaborating with teams to enhance project quality and performance.

Build and operate cloud-native, large-scale data pipelines and scalable services for the Mist platform. Develop AI-driven routing intelligence to detect, troubleshoot, predict, and mitigate network issues in multi-tenant data centers, collaborating across teams and leveraging AI-assisted development tools.

Develop, maintain, and enhance software systems focusing on data pipelines, machine learning analytics, and cloud technologies while integrating models within defense systems.

Design, implement, deploy, and monitor production-grade ML and computer vision models (CNNs, transformers, vision-language). Evaluate models, analyze data, improve robustness, support MLOps/CI-CD, and mentor junior engineers within an Agile team.

Design, develop, and deploy AI/ML solutions for wired and wireless signaling data. Implement and benchmark neural networks, optimize algorithm performance, support Agile software development, collaborate cross-functionally, and mentor junior staff. Work requires eligibility for TS/SCI with polygraph and a STEM bachelor's degree.

Design, build, and deploy high-throughput ML and LLM-powered personalization and agentic systems at consumer scale. Process petabyte-scale data, optimize real-time inference, implement resilient fallback paths, and balance latency, cost, and model quality while collaborating across engineering and product teams.

Design, develop, and deploy scalable machine learning models and data pipelines for production. Collaborate with data scientists, engineers, and stakeholders, optimize model performance and scalability, automate deployments, monitor production systems, and evaluate emerging ML and cloud technologies to drive business outcomes.

The Principal AI/ML Engineer will lead the design, implementation, and deployment of AI systems, mentor junior engineers, and ensure model performance and reliability in production environments.

Hands-on ML internship working on real production ML projects with mentor guidance. Build and optimize ML models and workflows using a large GPU cluster for noisy financial data; collaborate with engineers and attend ML-specific training and technical interviews.

Lead design, implementation, and deployment of scalable AI/ML systems for aerospace and defense. Develop RAG, embedding/retrieval pipelines, transformers, GANs, RL and MPC-aligned models. Use GPU/HPC for real-time decisioning, mentor engineers, ensure safety, validation, regulatory and cybersecurity compliance, and support integration and prototyping.

Design, build, test, deploy, and operate end-to-end AI/ML pipelines for mission systems. Automate workflows, ensure monitoring and observability, integrate models into production across hybrid cloud/on-prem environments, and collaborate with cross-functional teams while meeting security and infrastructure requirements.

As a Machine Learning Engineer in Computer Vision, you'll develop AI solutions using advanced algorithms, focusing on image classification, segmentation, and video processing tools.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

Design, develop, deploy, and maintain AI/ML models and pipelines. Collaborate with cross-functional teams, implement MLOps and CI/CD, monitor model performance, investigate incidents, ensure compliance with security/privacy/ethical requirements, and document model architectures and procedures.

Design, develop, train, validate, and deploy AI/ML products for life-sciences customers. Advise leadership on technical and commercial feasibility, collaborate with cross-functional and commercial teams, and lead projects in RL, LLM agent harnesses, fine-tuning, governance, and model evaluation.

Lead a small ML engineering pod to turn research into production: design and operationalize training pipelines and low-latency inference services for computer vision, document intelligence, and predictive models. Drive R&D execution, benchmarking, prototyping, migrations from frontier models to fine-tuned/open-source solutions, and mentor early-career engineers while aligning research with product timelines.

Early-career ML engineer builds data pipelines and trains scientific ML models (surrogate and physics-informed), develops visualization/dashboard tools, runs experiments, and hardens prototypes for production while collaborating across engineering teams.

Design, train, test, deploy, and maintain machine learning models for defense applications. Collaborate with data engineers, data scientists, architects, and product owners to implement AI use cases, guide clients on ML algorithms and tools, and drive mission-focused solutions.

Develop and deploy ML-based perception algorithms for robotics, optimize pipelines, and collaborate across disciplines for integration.