Top Machine Learning Engineer Jobs

Train and evaluate computer vision models for embedded deployment, build efficient C++ inference pipelines, and optimize latency, memory, power, and sustained performance. Integrate models with embedded Linux, test on representative hardware and data, and investigate errors across preprocessing, inference, and system integration. Lead candidates will set architecture and evaluation standards while guiding model and software development.

Train and fine-tune diffusion models, adapters, and pipelines for character-based sticker generation. Build provenance-aware training data pipelines and evaluation systems measuring character fidelity and output quality. Partner with brand teams to secure character approvals, ensure reliable outputs from spoken requests, and collaborate with serving and trust-and-safety engineers to launch safe products for children.

Build and adapt AI models for customer-specific scientific workflows. Lead post-training using SFT and reinforcement learning, design evaluation systems, analyze model failures, and drive iterative quality improvements. Partner with AI researchers and software engineers to integrate reliable model capabilities into production workflows. The role requires strong Python and PyTorch skills, deep familiarity with language, multimodal, or agentic models, and the ability to translate customer needs into technical solutions.

Lead applied AI model post-training, evaluation, and production ML system development. Adapt models to customer-specific scientific workflows using SFT and reinforcement learning, build evaluation and monitoring systems, debug complex model behavior, collaborate with research and software teams, and mentor engineers. The role requires deep expertise in Python, PyTorch, LLMs, multimodal or agentic systems, and shipping reliable machine learning capabilities.

Lead a three-person computer vision team developing, training, and deploying custom YOLO object-detection models for live full-motion video. Architect secure video-training pipelines, establish annotation and dataset standards, address class imbalance and challenging environments, and optimize inference using CUDA, TensorRT, and DeepStream. Integrate computer vision capabilities with broader applications, guide technical direction and Agile delivery, and mentor mid-level engineers.

Lead full-stack development and technical strategy for Copilot Discover: design, build, and operate scalable ML/deep-learning models and prompt strategies; implement responsive TypeScript/React/NextJS/Web Components front-ends; develop REST APIs and data layers; ensure performance, scalability, and reliability; collaborate across UX, backend, and ML teams; participate in code reviews and architecture decisions.

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.

Machine Learning Engineer Interns will develop, deploy, and operate production-scale ML models and pipelines. They will lead an end-to-end research-to-production project, apply modern ML techniques to blockchain and crypto use cases, collaborate with senior engineers and product teams, and present findings to stakeholders. The role requires doctoral-level machine learning research, model development experience with PyTorch or TensorFlow, production-quality Python skills, and software engineering fundamentals.

Leads architecture and hands-on implementation of scalable AI Defense systems that detect, validate, and stop threats to AI applications, agents, and foundation models. Owns high-performance platform services, LLM inference and model-hosting strategy, secure enterprise deployments, and production delivery. Partners with Product, Threat Research, and Operations while mentoring engineers and guiding technical strategy across teams.

Develop and optimize large language models for clinical documentation, reasoning, coding, and structured clinical outputs. Responsibilities include fine-tuning and post-training open-weight models, building evaluation frameworks and datasets, reducing inference cost and latency, serving models at scale, and moving research into production. The role collaborates with clinicians and engineering teams to deliver reliable AI in regulated healthcare environments.

Build ML and data systems for climate policy analysis, renewable energy optimization, and scalable climate interventions. Responsibilities include developing LLM and NLP pipelines, knowledge graphs, predictive and recommendation models, causal analyses, evaluation frameworks, data visualizations, and reproducible MLOps pipelines. The role also maintains cloud data infrastructure, APIs, model deployment workflows, monitoring, and governance while collaborating with policy experts and community stakeholders.

Design and implement scalable offline and online machine learning infrastructure for search and recommendation. Own data logging, processing, quality, model training, batch and real-time pipelines, and online inference services handling billions of records and petabytes of data. Partner with data scientists, analysts, product managers, and engineering teams while researching technical solutions and mentoring engineers.

Sets technical vision for Pinterest’s ML and media infrastructure, leading distributed training, fine-tuning, evaluation, inference, GPU optimization, content ingestion, and signal-processing architectures. Partners with AI/ML, Ads, Content, Shopping, and GenAI teams to productionize reliable, scalable platforms, drive architecture decisions and migrations, improve operational standards, and mentor senior engineers.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Develops and leads production machine learning and image processing solutions for neurovascular OCT and angiographic medical imaging. Responsibilities span algorithm research, prototyping, software architecture, implementation, testing, validation, deployment, and maintenance within regulated medical device systems. The role provides technical mentorship, contributes to ML frameworks and infrastructure, collaborates cross-functionally, performs code reviews, and supports quality, documentation, and compliance initiatives.

Own the platform for deploying, serving, monitoring, and retraining fraud, risk, and payments models in production. Build real-time inference systems, reproducible feature and ML pipelines, model rollout workflows, drift monitoring, third-party model integrations, and merchant-facing risk tooling. Ensure low latency, reliability, explainability, training-serving consistency, and safe model operations for payment authorization systems.

Build reinforcement learning environments from proprietary datasets, develop and deploy production AI agents, and help define technical strategy. Serve as the first engineering hire, collaborate with leadership, and eventually lead the growing engineering team. The role requires strong reinforcement learning expertise, production AI agent experience, AI-assisted coding proficiency, and willingness to work full-time on-site in the Bay Area.

Build and scale AI/ML solutions for healthcare revenue cycle automation. The role owns AI outcomes, identifies high-value use cases, shapes technical and product roadmaps, converts prototypes into scalable production systems, and architects infrastructure for training, testing, monitoring, security, and deployment. The engineer will apply ML, NLP, and LLM approaches to claims and workflow optimization while balancing technical tradeoffs, business value, cost, and operational efficiency.

Lead the design, optimization, and production deployment of high-performance LLM inference systems using vLLM, CUDA, Triton, C++, and Python. Improve GPU utilization, latency, throughput, scalability, and cost through kernel optimization, quantization, batching, memory management, distributed inference, and profiling. Contribute to upstream open-source projects, establish engineering standards, review performance-critical code, and guide research-driven serving improvements.

Build and deploy production machine learning systems, primarily recommendation and personalization models. Own projects across problem definition, data pipelines, model training, deployment, monitoring, and optimization. Develop scalable low-latency inference infrastructure, run experiments and A/B tests, and apply ML to fraud detection, abuse prevention, and content moderation. Collaborate with engineering, product, and data science teams in a fast-paced, in-person startup environment.

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

Design and build a production-grade AI memory and retrieval engine for personalized, privacy-preserving LLM experiences. Responsibilities include developing multi-tier storage and hybrid retrieval pipelines, memory extraction and classification, adaptive updates, evaluation frameworks, and reliability safeguards. The role researches advanced memory architectures, supports agentic workflows and tool integrations, collaborates with product management, and owns production operations. Strong software engineering, Python, NLP/ML, vector database, LLM orchestration, privacy, and RAG expertise are required.

Build and improve AI recruiting agents using modern LLMs, Python backend services, APIs, prompts, RAG, workflows, and agent orchestration. Evaluate model performance, experiment with AI technologies, collaborate with product and engineering teams, and deploy scalable production systems. The role requires Python, LLM and AI API experience, familiarity with agent frameworks and FastAPI, and cloud platform knowledge. Production AI product experience, model evaluation, tool calling, prompt engineering, and open-source contributions are preferred.

Design, build, and maintain secure on‑prem and cloud infrastructure and CI/CD pipelines for Kubernetes-based services. Automate deployments, manage production/staging/dev environments, monitor logs and system health, enforce security controls, and support accreditation (A&A) and operational readiness. Troubleshoot incidents, perform root cause analysis, maintain documentation, and collaborate with Agile teams to drive DevSecOps best practices.

Designs and manages AWS cloud infrastructure and Amazon EKS platforms using GitLab CI/CD and Infrastructure as Code. Builds ETL workflows and scalable data pipelines for analytics and AI workloads, supports MLOps/AIOps and FinOps capabilities, troubleshoots Linux systems, and improves platform performance, security, scalability, and availability.