Senior engineer role owning end-to-end abuse detection and response: design and implement LLM guardrails, build AI-powered classifiers and automated enforcement, integrate/tune security scanners in CI, and analyze abuse patterns using BigQuery/Hex. Requires 8+ years in security engineering/anti-abuse/fraud detection and experience with Python/TypeScript, SQL at scale, ML/LLM classifiers, and familiarity with common attack vectors.

Lead the strategy and build foundational user models and systems—including embeddings, feature pipelines, storage/retrieval, and low-latency serving—partnering with product and infra teams to drive adoption across Feeds, Search, Notifications, and Ads. Requires 10+ years building production ML systems, deep expertise in user modeling and representation learning, and experience applying LLMs/foundation models to improve personalization at scale.

Sayari seeks a Staff Applied Scientist to own the "Judgment Layer": design and implement specialized judge models, scoring pipelines, domain-specific evaluation frameworks, and annotation infrastructure; research MoE routing, ensemble calibration, and evaluation techniques for agentic LLM/Graph RAG systems. Requires 10+ years in ML with deep expertise in neural networks, LLM evaluation, statistics, and production deployment of evaluation services.

Lead a team of ML engineers owning notifications relevance (retrieval + ranking), guide design and deployment of large-scale recommender systems, optimize budgeting and ranking, run experiments/QA, and collaborate with product and cross-functional stakeholders. Requires experience managing engineering teams and building large-scale ML systems.

Lead the technical strategy, architecture, and implementation of Reddit's next-generation ML Indexing & Retrieval platform, covering lexical and vector indexing, low-latency retrieval, batch and stream processing, and integration with ML applications. Requires 10+ years software engineering experience (3+ years technical leadership), strong experience with distributed systems, containerized cloud-native architectures (Kubernetes, AWS/GCP), and technologies such as Go/Java/Python, Flink/Airflow/Spark, and search/vector engines (Milvus, Vespa, Elasticsearch).

Zapier is hiring an Applied AI Engineer for its AI Platform team to build shared LLM/ML platform capabilities (LLM proxy, observability, MLOps) used across product teams. The role expects 4+ years of software engineering experience, at least 1 year of LLM/ML Ops or adjacent platform work, and hands-on work in TypeScript and Python to improve reliability, performance, safety, and cost of production AI systems.

NVIDIA is hiring a Senior AI Compiler Engineer in applied research to develop machine-learning-driven compiler solutions and low-level GPU code generation. Responsibilities include designing AI-based compilation technology, building SFT and RL training pipelines, prompt engineering for compilers, dataset preparation from compiler traces, benchmarking, and integrating learned policies into production toolchains. Requires MS/PhD or equivalent, 5+ years building AI/ML systems, strong Python and C++ skills, CUDA experience, reinforcement learning and fine-tuning experience. Locations include US (Austin, Redmond, Santa Clara) with remote options. Base salary range: 152,000–241,500 USD/year.

General Assembly seeks a subject matter expert to validate competencies, technical accuracy, and async learning assets for an MLOps/AI Platform Engineer pathway targeted at customer-facing professionals. Requires 7+ years in software or data engineering, 3+ years in MLOps/ML platform production roles, strong experience with ML pipelines, deployment, monitoring, governance, Python, CI/CD, and Azure/Azure ML. Engagement is ~15–20 hours/week for 6–7 weeks, paid $60–$75/hour, remote from approved countries.

Lead and execute a research roadmap in quantum information science focused on quantum optimization, quantum machine learning, and quantum algorithms; collaborate with quantum hardware vendors and internal partners; publish and represent Capital One in the research community. Requires deep quantum computing expertise, experience with quantum programming and simulation, research track record (publications), and leadership/mentoring experience. Salary range provided (USD, annual); employer may consider visa sponsorship.

NVIDIA is hiring a Senior Solutions Architect to design and deploy agentic AI solutions for retail customers, focusing on multi-agent coordination, RAG retrieval pipelines, and GPU-accelerated inference. Requires BS/MS/PhD (or equivalent), 8+ years in deep learning/machine learning/distributed AI, and strong experience with Python, C/C++, Linux, PyTorch or TensorFlow. Listed base salary ranges (USD, annual) are 184,000–287,500 (Level 4) and 224,000–356,500 (Level 5); applications accepted through April 25, 2026.

Hands-on role building AI-driven analytics on top of the company data warehouse (primarily BigQuery). Responsibilities include designing AI interactions with warehouse data, working with LLMs and AI agents, ensuring metric correctness, improving data governance and access controls, and automating analytical workflows and validation.

Twilio is hiring an L4/Staff Machine Learning Engineer for the Trust Intelligence Platform (remote - US). The role involves architecting scalable data pipelines and feature stores, building reproducible ML training/evaluation/inference workflows, integrating event streams, and operating MLOps and cloud infrastructure. Requires a B.S. (or equivalent), 4–8 years of experience building/operating data or ML systems, and proficiency with Python, SQL, cloud platforms, containerization, and orchestration tools.

Join Reddit's Search Recommendations & Relevance team to advance search and recommendation products by improving retrieval and ranking, designing search features, building and scaling indexes and pipelines, and integrating/deploying LLMs. Requires 10+ years' industry experience with large-scale search/recommendation systems, strong ML model development and deployment skills (PyTorch/TensorFlow), and proficiency in Python and Golang.

Responsible for designing and implementing automated training pipelines, ETL and feature engineering, and deploying real-time ML pricing solutions. Role involves monitoring production systems for latency, drift and training-serving skew, running champion-challenger tests, and partnering with data science, engineering and product teams to deliver scalable pricing models. Ideal candidate has 5+ years' experience with production ML systems, proficiency in Python, Scala and SQL, and experience with distributed data processing.

Design, build, and evolve machine learning systems that power Hopper's pricing engine: implement automated training pipelines, ETL and feature engineering, deploy real-time ML pricing solutions, monitor production for latency/drift/skew, and run champion-challenger tests. Requires ~5+ years experience and proficiency with Python, Scala, SQL, distributed data processing and production ML systems.

Work on the Fintech Foundation team designing, building, and operating machine learning systems that power Hopper's pricing engine. Responsibilities include creating automated training pipelines, ETL and feature engineering, deploying real-time ML pricing solutions, monitoring production models, and running champion-challenger tests. Ideal candidates have 5+ years' experience with production ML systems, expertise in Python/Scala/SQL, and familiarity with distributed data processing and ML model lifecycle.

Join Hopper's Fintech Foundation team to design, build, and evolve production ML systems and pipelines that power real-time pricing and fintech products. Responsibilities include ETL and feature engineering, automated training pipelines, real-time model deployment, monitoring for latency and drift, and running champion-challenger tests in collaboration with data science, engineering, and product stakeholders.

Design, build, and maintain automated training pipelines, ETL and feature engineering, and real-time ML pricing solutions. Own model-to-production lifecycle, monitor production for latency/drift, run champion-challenger tests, and collaborate with data science, engineering, and product teams to deliver scalable pricing solutions. Requires ~5+ years experience with production ML systems, proficiency in Python, Scala, and SQL, and experience with distributed data processing and software architecture.

Design, build, and evolve machine learning systems and automated training/ETL pipelines for Hopper's pricing engine; deploy and monitor real-time ML pricing solutions in production and run champion-challenger tests. Role expects 5+ years experience with production ML systems, proficiency in Python, Scala, SQL, distributed data processing, and domain knowledge in pricing or demand forecasting.

Join Hopper's Fintech Foundation as a senior software/ML engineer responsible for designing and deploying production ML pricing solutions. Responsibilities include building automated training pipelines, ETL and feature engineering, real-time model deployment, and production monitoring for latency, drift and training-serving skew. Requires 5+ years in production ML or large-scale pricing platforms, strong Python/Scala/SQL experience, and proficiency in French and English. Role is remote in Canada (Montréal listed).