Top Machine Learning Engineer Jobs

Develop advanced personalization and recommendation systems for digital grocery shopping. Build, deploy, monitor, and maintain machine learning pipelines and microservice APIs supporting production inference. Apply natural language processing, generative AI, large language models, agentic AI, recommendation models, deep learning, transformers, and classical machine learning techniques to real-world business problems. Evaluate and tune models, communicate technical results, and contribute to scalable AI solutions.

Develop machine-learning surrogate models and optimization workflows for Formula 1 ERS design. Build neural networks emulating thermal, electrical, and degradation behavior; process large-scale telemetry and simulation datasets; implement genetic algorithm and Bayesian optimization loops; perform sensitivity and uncertainty analysis; integrate ML workflows with engineering simulation tools; and validate performance against real duty cycles under FIA constraints.

Lead development and optimization of the compiler stack for ML systems, collaborate across teams, integrate and deploy products, map ML operations to hardware, and drive compiler infrastructure innovation and performance debugging.

Design, build, and optimize personalized recommendation systems for Fortnite Discover, including candidate generation, ranking, reranking, user representations, and impression allocation. Develop deep learning retrieval models, run A/B experiments, establish evaluation frameworks, and deploy models to production. Collaborate on content quality and ranking signals while making infrastructure decisions across batch, near-real-time, and streaming architectures.

Design, build, and optimize personalized recommendation systems for Fortnite’s Discover experience. Develop retrieval, ranking, reranking, user representation, and candidate-generation models using deep learning. Lead A/B experimentation, evaluation frameworks, production deployment, and ML infrastructure decisions across batch, near-real-time, and streaming systems. Collaborate on content quality and ranking signals while balancing relevance, diversity, fair exposure, cold-start challenges, and creator incentives.

Develop simulation technology for physical AI and robotics, integrating learning environments with engineering-grade simulators for dynamics, structures, fluids, and thermal applications. Build workflows combining simulation and AI-driven learning to accelerate robotics deployment. Preferred expertise includes machine learning, reinforcement learning, vision-language models, and vision-language-action models.

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, test, evaluate, deploy, and maintain AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Process structured and unstructured data, build ETL pipelines, conduct experiments, measure performance, deploy models in cloud environments, document findings, brief stakeholders, educate staff, and provide AI/ML subject matter expertise.

Lead the architecture, implementation, and operation of scalable AI/ML infrastructure. Build CI/CD pipelines for model development and deployment, manage data ingestion, serving, monitoring, security, and performance, and collaborate with AI researchers and software engineers. Mentor junior engineers and establish best practices for AI/ML platform development, automation, and cloud infrastructure.

Build and operate production machine learning systems for recommendations, ranking, personalization, contextual bandits, and agent-based recommendations. Responsibilities include model development, feature and data pipelines, evaluation, deployment, monitoring, experimentation, and reliability improvements. The role partners with engineering, data science, product, marketing, and sales teams to translate business problems into measurable ML outcomes.

Design, build, and maintain batch and streaming data pipelines on GCP (BigQuery, dbt, Airflow, Pub/Sub). Productionize ML workflows (training, inference, CI/CD) using Cloud Run, GKE, and Vertex AI. Support real-time inference, RAG/LLM assistants, graph-based recommendations, and model monitoring. Collaborate with cross-functional teams to deliver scalable, reliable data products and observability for downstream ML and analytics.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Design, build, and operate large-scale ML systems across data, training, evaluation, inference, and deployment. Lead GPU-optimized training and inference pipelines, ensure reliability, performance, safety, and seamless integration into products. Own production deployments, monitoring, and cross-functional best practices to drive measurable model improvements and efficient research-to-production cycles.

Lead design and build of a global AI/ML deployment platform: architect control plane, multi-region deployments, APIs, autoscaling, and integrations. Own technical direction, engage clients, drive governance of AI-assisted development, and ensure resilient, secure production-grade infrastructure.

Build and own Clera’s AI/ML intelligence layer, including recommendation systems, ranking algorithms, and LLM-powered candidate-role matching. Develop production-ready features, data pipelines, and services while collaborating cross-functionally and shipping frequently. The role requires hands-on experience with the listed development tools, TypeScript, React, startup environments, and a willingness to work six days per week in a San Francisco hacker house.

Own and advance production search, recommendation, retrieval, and ranking systems for a consumer social and interactive entertainment platform. Build candidate generation, embedding retrieval, two-tower models, ranking features, user and content representations, and online serving systems. Lead experimentation and evaluation to improve discovery, engagement, retention, personalization, cold-start performance, and content distribution while partnering with product, data, and engineering teams.

Build and optimize Snap’s machine learning infrastructure, including scalable training, evaluation, inference, feature serving, data management, vector search, and model deployment systems. Improve reliability, performance, and cost efficiency for large-scale ML workloads while collaborating with ML engineers. The role requires strong programming, distributed systems, big data, cloud infrastructure, and production machine learning experience.

Architects, develops, deploys, and operates large-scale machine learning systems that optimize merchant delivery zones and marketplace performance. Responsibilities include model training, feature engineering, ML infrastructure, real-time inference, experimentation, monitoring, and automated retraining. The role partners with Product, Data Science, Operations, and Infrastructure teams while leading technical initiatives and mentoring engineers.

Build and operate scalable machine learning platforms supporting drug discovery. Responsibilities include model deployment, monitoring, retraining, reliability, inference optimization, GPU resource management, infrastructure automation, CI/CD, observability, performance tuning, and production integration. Collaborate with research scientists, data scientists, AI engineers, and infrastructure teams to deliver reliable, reproducible AI capabilities.

Own the complete machine learning model lifecycle, from experimentation and validation through deployment, monitoring, retraining, rollback, and retirement. Build reusable training, evaluation, deployment, orchestration, and CI/CD infrastructure. Establish model promotion standards, versioning, lineage, monitoring, alerting, and incident-response processes. Partner with data scientists to preserve scientific validity, improve reliability and efficiency, support delayed-label use cases, mentor teammates, and define MLOps priorities.

Develop and deploy advanced machine learning solutions for pricing, promotions, demand forecasting, and optimization. Own the end-to-end ML lifecycle from data exploration and model design through deployment, monitoring, and maintenance. Collaborate cross-functionally to improve revenue, profitability, and business efficiency using machine learning, statistical modeling, forecasting, generative AI, and production ML infrastructure.

Own the infrastructure powering language-model training and serving from experimentation through production. Optimize GPU performance, latency, throughput, memory, and cost; improve batching, caching, compression, and decoding; support inference for evaluation, synthetic data, and reinforcement learning; and build reliable model release and observability workflows. Partner with founders and researchers to allocate GPU capacity and deploy models in customer environments.

Drive applied machine learning research on language models from research questions through experimentation and deployment. Responsibilities include creating datasets, reward functions, evaluation suites, automated checkers, and training experiments; analyzing failures through ablation and held-out testing; and shipping successful models or publishable results. The role involves close collaboration with leadership, infrastructure engineers, researchers, and clients, requiring strong Python and PyTorch skills, analytical judgment, and experience adapting pretrained language models.

Design and implement production agentic AI systems, RAG pipelines, intelligent document processing services, embedding and vector retrieval workflows, and multi-agent extraction systems. Build FastAPI and Java/Spring Boot services, databases, evaluation harnesses, and governance controls. Own major AI platform components, collaborate with architecture and business teams, and mentor mid-level engineers while driving technical excellence and production quality.

Build and own the recruiting marketplace’s AI/ML intelligence layer, including recommendation, ranking, retrieval, and LLM-powered candidate-role matching. Ship production features rapidly, architect data pipelines and model services, and translate founder product goals into deployed systems. The role requires applied machine learning, recommender systems, production LLM API experience, and strong Python or TypeScript skills. Work is fully on-site in San Francisco six days per week.