Top Machine Learning Engineer Jobs

As a ML Engineer at Apiphany, you will develop advanced machine learning models, focusing on engineering data, AI systems, and real-world performance.

Design and implement 3D machine learning models, process reality capture data, and integrate AI solutions in production environments for robotics applications.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

The Machine Learning Engineer role at Wayve focuses on leading initiatives in model-based autonomous driving, involving the development and delivery of ML-driven behaviors, collaboration across teams, and mentoring other engineers.

The Machine Learning Engineer will conduct data analysis, develop ML applications, manage data transformations, and support API and ETL implementations.

Optimize large-scale AI training and inference systems for throughput, latency, and cost. Responsibilities include GPU and kernel optimization, distributed training, model compression, LLM serving, compiler optimization, data pipeline tuning, benchmarking, hardware evaluation, and performance regression analysis. The engineer will collaborate across ML and platform teams, document optimization practices, evaluate emerging technologies, and mentor junior engineers.

Design and maintain infrastructure for large-scale multimodal training and evaluation. Collaborate with researchers to build reliable distributed data systems.

The Staff Machine Learning Engineer will design and implement ML systems, build scalable architectures, collaborate with data teams, and drive technical strategy for AI applications.

The Staff Data Architect will design complex data solutions, lead data architecture initiatives, and oversee data governance while collaborating with various teams to meet business objectives.

The Staff Machine Learning Engineer will optimize the inference layer for voice applications, focusing on model serving and real-time audio processing. Responsibilities include driving performance architecture, collaborating with model partners, and developing evaluation frameworks for STT and TTS models.

Design, build, and deploy machine learning systems for controlling quantum processors. Develop reinforcement learning and Bayesian methods for QEC calibration, and work cross-functionally with product and R&D teams.

Lead end-to-end machine learning solutions for League of Legends, from problem framing to production. Build personalization, recommender, matchmaking, and in-game ML systems; design evaluations and experiments; integrate models with game and service engineering; create reusable ML patterns; mentor engineers and improve ML observability, monitoring, and launch practices.

The Senior ML Engineer will architect data pipelines, develop classification algorithms, manage data integration, and mentor team members in a cross-functional setting.

The Machine Learning Engineer will build and scale ML infrastructure, drive model performance, and ensure reliability while collaborating with various teams to optimize feature engineering pipelines and champion best practices.

Build and scale production machine learning systems for personalization and marketing optimization. Design end-to-end AI, reinforcement learning, recommendation, propensity, uplift, and lifetime-value pipelines; implement CI/CD, model versioning, monitoring, and orchestration; and lead agentic AI workflow automation. Collaborate with data science, engineering, marketing, and operations teams to productionize solutions. Provide technical leadership, mentor engineers and data scientists, and influence ML and data strategy.

Own the full lifecycle of production classification and detection models, including transformer fine-tuning, entity detection, behavioral risk scoring, training, serving, versioning, evaluation, drift detection, and retraining. Build SageMaker-based ML systems and define output contracts supporting audit and policy enforcement. Lead the evaluation harness and mentor a co-op while operating as the company’s sole ML engineer.

Designs and operationalizes scalable machine learning platforms, pipelines, infrastructure, and production models. Develops MLOps and LLMOps solutions, including generative AI, prompt engineering, RAG, LLM fine-tuning, and computer vision applications. Collaborates with data scientists, engineers, architects, and cross-functional teams to optimize models and integrate them into enterprise systems. Applies hybrid ML and rule-based systems in regulated insurance environments, mentors colleagues, and supports Agile delivery.

Designs, builds, and operates reliable, high-performance inference platforms for large machine learning models in production. Responsibilities include request routing, batching, caching, autoscaling, GPU utilization, observability, performance engineering, capacity planning, incident response, and optimization of latency, throughput, cost, and quality across diverse workloads.

Develop and deploy AI/ML applications for semiconductor test operations. Responsibilities include building LLM-based tools for data analysis, technical documentation processing, and code generation; implementing RAG systems in testing pipelines; collaborating cross-functionally to convert customer requirements into prototypes; and providing weekly progress reports.

This role leads the ML Acceleration team, focusing on enhancing ML model training efficiency and team management, while driving technical strategy and collaboration.

Develop, deploy, and support secure, scalable production software and AI/ML solutions. Collaborate with product, UX, and engineering to deliver features, create tests and automation, instrument monitoring and dashboards, and participate in agile processes while continuously learning and improving team practices.

Build and operate production machine learning systems focused on multilingual NLP, including entity linking, classification, event extraction, deduplication, and narrative analysis. Develop GPU-accelerated workflows, fine-tune transformer models, and create LLM features such as RAG pipelines and agents on AWS Bedrock. Establish evaluation frameworks, integrate ML services into Django and event-driven platforms, lead technical architecture discussions, and mentor team members.

Lead and build an ML Acceleration team to define strategy and implement scalable solutions to speed ML training. Hands-on role combining technical design (distributed training, hardware and model optimization) with people management, cross-functional collaboration, impact measurement, and continuous performance improvements for production autonomous vehicle ML systems.

Lead development of real-time machine learning and computer vision algorithms for ultrasound imaging in Intuitive’s robotic-assisted biopsy system. Responsibilities include detection, segmentation, classification, temporal tracking, artifact mitigation, image fusion, model optimization, and deployment into high-performance C++ medical-device software. The role also supports system integration, testing, formal design documentation, and patent applications while collaborating with clinical and engineering teams under Good Machine Learning Practice principles.

Productionize and optimize ML systems for AI reasoning over structured data. Responsibilities include improving training and inference performance, porting workloads from CPU to GPU, building training and deployment infrastructure, developing experimentation and observability tools, and implementing new model architectures and fine-tuning methods using Python and PyTorch. The role requires strong ML research, systems engineering, software architecture, optimization, and experimental design skills.