Lead the end-to-end global payroll operating model for employees, contractors, EORs/PEOs and local providers; manage operational equity administration in Shareworks; implement HRIS integrations and automation; strengthen payroll controls and compliance; establish processing calendars, metrics and dashboards; collaborate with HR Tech, IT and Finance. Role is remote (Montréal/Québec) within HTS (Hopper).

NVIDIA is hiring a Forward Deployed Architect to lead technical engagements with AI-native organizations, cloud providers, and ISVs—designing strategies for distributed training, large-scale inference, MLOps, and enabling new NVIDIA hardware. Requires a technical leader with hands-on troubleshooting and prototyping ability, a strong foundation in AI infrastructure technologies (especially Linux), and 12+ years of technical experience or 5+ years of specialist-level experience.

Tide is hiring a Head of Machine Learning for the Ongoing Monitoring team in Hyderabad. The role requires defining risk engineering strategy, building and scaling risk infrastructure and ML-driven fraud/compliance systems, developing self-service tooling, and leading a team. Requires 12+ years engineering experience with senior leadership experience and domain expertise in fraud, AML/KYC, adversarial AI, cloud-native systems and production risk-scoring environments.

Twilio is hiring a Tech Lead (P6) in Emerging Technologies to lead hands-on prototyping and build production-ready AI/ML and full-stack solutions for the Twilio Forward incubation lab. The role requires 10+ years of full‑stack software development experience, proficiency in languages such as JavaScript, Python, Java or Go, experience with front-end (React/Angular) and/or back-end (Node.js/Springboot) frameworks, and familiarity with SQL/NoSQL, cloud platforms (AWS/Azure), and LLM/AI-assisted development practices.

Individual Contributor role responsible for designing and deploying production ML models for fraud/risk detection, addressing highly imbalanced data, detecting and mitigating data/concept drift, and building GenAI/Agentic AI workflows. Requires 10+ years' experience in data science/ML with strong classical ML expertise and production experience on cloud platforms (AWS or GCP).

Role on the Ads ML Platform team to design and build scalable data infrastructure, feature management, and training-set generation systems (batch and real-time). Responsibilities include feature governance (lineage, validation, drift detection, monitoring), integration with ML workflows, and operational excellence. Requires 3+ years in data infrastructure/platform or ML infrastructure and experience with distributed data/compute systems such as Spark, Kafka, Airflow, Kubernetes, BigQuery, or similar.

Hiring a Machine Learning Systems Engineer for Reddit's Ads ML Platform to design and build large-scale feature and training-set infrastructure, develop batch and real-time feature frameworks, implement feature governance (lineage, validation, drift detection, anomaly monitoring, reproducibility, versioning), and improve observability, reliability and cost. Requires 3+ years in data/platform or ML infrastructure and experience with distributed systems such as Spark, Flink, Kafka, Ray, Airflow, Kubernetes or BigQuery.

Senior engineering role owning the technical evolution of Acquia DAM's AI capabilities: architect and implement stateful, multi-agent (agentic) workflows, lead AI observability and prompt/version management, integrate DAM with Acquia's AI ecosystem, and mentor engineers. Requires 8+ years software engineering experience and 3+ years production experience with AI agents plus hands-on experience with LangGraph, Temporal, Pydantic, LangFuse, and cloud AI deployment.

Senior individual contributor role to architect and deliver agentic AI features for Acquia DAM. Responsibilities include building stateful multi-agent workflows (LangGraph, Temporal, Pydantic), owning AI observability via LangFuse, setting AI engineering standards (RAG, prompt management, tool-calling), evaluating LLM/provider tooling, and mentoring engineers. Requires 8+ years software engineering experience (3+ years producing AI agents), deep Python skills, hands-on experience with LangGraph, Temporal, Pydantic, LangFuse, agent harness frameworks (LangChain or similar), and cloud AI deployments (AWS/Azure/GCP). Salary range stated: $180,000–$200,000 USD.

Lead technical design and deployment of AI/ML solutions for NVIDIA GeForce NOW, including real-time diagnostics, LLM chatbot, forecasting, constraint-optimized capacity allocation, anomaly detection, retention modeling and large-scale production analytics. Requires extensive software and data science experience and hands-on experience with tools such as Python, Spark, Databricks, MLflow, Kubernetes and related MLOps technologies.

Role to build and operate ML platform tooling including offline experimentation systems, training orchestration, experiment tracking, model registries, metadata/lineage, automated promotion/rollback workflows, and an agentic AI execution platform. Requires experience in large-scale distributed systems and production ML infrastructure (5+ years), with hands-on experience in ML platform tooling and orchestration technologies.

Reddit seeks an Engineering Manager for a new Ads ML Efficiency function to hire and lead ML/systems engineers, define technical roadmap for training and inference optimization, build profiling/benchmarking/load-testing/observability tooling, and partner with ranking and platform teams to reduce training time, latency, and serving cost. Preferred experience includes ML engineering close to models, GPU training/serving, PyTorch and distributed training, and production ads or recommender systems.

The Forward Deployed Engineer will work directly with enterprise clients to map and redesign workflows, establish operating models and governance, and deliver working AI-powered prototypes (using LLM APIs and low-/no-code tools). Requires 5+ years in management consulting or equivalent enterprise technology roles and experience driving organizational change, prototyping, and adoption strategies.

Hiring a Forward Deployed Engineer to map and redesign creative workflows, build and integrate AI-powered pipelines (LLMs, prompt engineering, agents) into DAM/CMS and marketing stacks, establish governance, and enable adoption. Role requires 5–10 years in creative technology, content operations, production engineering, or applied AI; 100% remote; compensation described as paid in USD.

Hands-on role configuring and implementing creative AI platforms, integrating AI workflows with DAMs, CMSs, marketing technology stacks, and project management systems. Requires 5+ years in creative technology, production engineering, content operations, or applied AI, and experience delivering production-ready solutions, proofs of concept, governance frameworks, and driving adoption within client teams.

Embedded, hands-on role that maps end-to-end creative workflows, redesigns processes, and implements AI-powered pipelines integrated with DAMs, CMSs, and marketing technology stacks. Responsible for building proofs of concept and production-ready solutions, establishing governance, enabling adoption, and tracking operational metrics. 100% remote role supporting engagements across Latin America and U.S. clients.

Software Engineering Manager for AI/ML Platforms (based in Georgia, virtual) responsible for delivery, people leadership, vendor collaboration, and driving adoption of AI/ML solutions. Minimum 7 years' experience; skills include Java/Python, MLOps, GCP (BigQuery, Vertex AI, Dataflow, Dataproc, Pub/Sub), Airflow, distributed systems, SQL, CI/CD and production monitoring. Posted salary range: $140,000–$240,000 (annual, specified for certain U.S. locations).

Build and optimize real-time data pipelines and backend services, develop and deploy ML models for fraud detection, produce production-ready features, and collaborate with platform/backend engineers. Technologies and practices mentioned include PyTorch, Scikit-learn, Go, SQL, Docker, Kubernetes, CI/CD, observability, and work with device/behavioral data and privacy/security requirements. Role is remote and open to candidates in UK, Germany, Ireland, Spain, Poland, Bulgaria, or Lithuania.

Design and build systems and tooling to improve ML training and inference efficiency, optimize distributed training, resource utilization, and model serving; build benchmarking and monitoring, partner with ML researchers and product teams, and drive platform scalability and cost efficiency. Requires 5+ years software engineering experience, strong Python, and proficiency in at least one systems language; experience with distributed systems and ML infrastructure (PyTorch Distributed, Ray, TensorFlow, Spark) and GPU performance analysis.

Join Reddit's ML Efficiency team to design and build systems, tooling, and optimizations that improve training and inference efficiency, GPU and resource utilization, distributed training infrastructure, and model serving. Requires 5+ years software engineering experience, strong Python skills, systems-language proficiency (Go/C++/Rust/Java preferred), experience with distributed systems and ML infrastructure, and expertise in performance engineering and profiling.