NVIDIA seeks a Developer Advocate focused on reinforcement learning and LLM post-training pipelines to build demos, write developer-facing content, collaborate on product launches, and engage developers via events and online channels. Requires 5+ years relevant experience (including 3 years engineering), experience with RL libraries and AI frameworks such as PyTorch, JAX, and NeMo-RL. Salary ranges provided for different levels (USD 152,000–241,500 for Level 3; USD 184,000–287,500 for Level 4).

BetterUp is hiring an AI / Data Engineer (P3) to design and maintain scalable data pipelines and warehouse architecture (Snowflake), develop dbt models and SQL/Python transformations, and build/deploy AI solutions (lead scoring, forecasting, segmentation) integrated with GTM systems (Salesforce, HubSpot). The role is hybrid (minimum two days per week in a US hub) and requires 3+ years of relevant experience. Base salary range listed: $133,000–$185,000 per year.

Dave is hiring a Lead Machine Learning Scientist to define ML strategy for marketing and growth, build and deploy production models (propensity, churn, LTV, cross-sell), optimize marketing spend and incentives, and partner with Marketing, Product, and Finance. Requires 7+ years in ML/data science, strong Python/SQL skills, production ML experience, and familiarity with marketing KPIs and experimentation.

Applied agent engineering role focused on building tool-using AI systems (agents) into existing workflows. Responsibilities include designing and implementing agent infrastructure (MCP servers, stateful sub-agents, skills), integrating with systems like Slack, Jira, Salesforce, Okta, Workday, Google Workspace and AWS, setting safety and access standards, instrumentation, and collaborating with HR, Sales, Customer Success, and other functions. Requires 7+ years of engineering experience and experience shipping production systems at scale.

Senior Applied Scientist role focused on credit risk: develop and productionize ML, statistical, causal, and optimization models; evaluate new data sources; build backtesting, validation, and monitoring frameworks; and translate ambiguous business problems into measurable modeling work. Requires a quantitative bachelor's degree (or higher) and 5+ years industry experience (or 3+ years with a PhD), strong Python and SQL skills, and experience with ML libraries and production systems.

Udacity (an Accenture company) seeks external part-time AI Technical Mentors to provide 1:1 sessions, technical deep-dives, and group Q&A. Mandatory skills include Python, API integration, core AI/ML concepts, familiarity with LLMs and generative AI, prompt engineering, ML libraries (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow), exposure to frameworks like LangChain and LlamaIndex, and knowledge of RAG, embeddings, and vector databases.

Responsible for designing, training, deploying, and monitoring ML models, developing time-series forecasts and dashboards, maintaining high-quality datasets, and collaborating with cross-functional teams. Requires ~4–5 years experience, strong Python and SQL skills, experience with ML workflows, and familiarity with BI tools and AI toolkits.

Lead open-ended and applied AI-security research: discover failure modes, build evaluation harnesses and prototypes, develop adversarial and defensive techniques, and translate findings into practical mitigations and guidance for engineering and security teams. Requires 12+ years of relevant experience and hands‑on expertise with ML tooling and security domains.

Work on the open-source Triton compiler to design and implement compiler technology (using MLIR and Triton's Python DSL), optimize high-level kernels to efficient low-level GPU code, and hand-tune critical paths (inline PTX). Collaborate with hardware architects and the CUDA compiler team; experience tuning BLAS/deep-learning kernels, numerics/linear algebra, and ML compilers (TVM/MLIR) is a plus. Base salary range: 184,000 USD - 287,500 USD per year.

Customer-facing technical role partnering with strategic AI-native customers, vendors, and internal teams to architect, deploy, benchmark, and optimize production inference and agentic systems. Responsibilities include GPU and runtime performance tuning, building deployment frameworks and automation, validating platform capabilities under real-world workloads, and enabling platform adoption; role requires travel up to 30%.

Hands-on technical leader responsible for architecting and building control and data plane implementations for a multi-tenant, high-scale machine learning platform. Work includes developing Ray and Spark solutions for LLM training and inference, improving operational excellence, collaborating cross-functionally, mentoring talent, and contributing to platform architecture. Basic qualifications include a relevant degree plus 8–10+ years of experience and strong programming experience in Python, Go, Scala, or Java.

Lead the definition, experimentation, and productization of agentic SDLC capabilities, designing reference architectures, safety guardrails, observability, and evaluation frameworks. Partner across Architecture, Product, Infrastructure, and Data/ML teams to drive internal adoption and scale reliable, multi-tenant solutions to customers while mentoring senior engineers.

Contractor mentor role supporting learners in Udacity's School of Data. Requires 5+ years of relevant technical experience and proficiency in Python and/or SQL, data analysis and modeling, ETL and data pipelines, data visualization tools (e.g., Pandas, NumPy, Tableau/Power BI), and familiarity with databases and data warehousing. Responsibilities include 1:1 mentoring, technical deep-dive presentations, and group Q&A sessions.

Senior engineering role on the Elasticsearch Search team responsible for designing and implementing new vector search features, improving existing vector similarity functionality (HNSW, IVF, etc.), fixing bugs, and collaborating with a globally distributed team and the open source community. Requires strong Java, algorithms, data structures, concurrency, and distributed systems skills, plus experience with search platforms and vector databases.

Role focuses on building performance testing frameworks, automated CI/CD pipelines, benchmarking systems, and dashboards to track and analyze GPU and compiler performance across models and hardware. Requires 5+ years software engineering experience, strong Python (C++ a plus), familiarity with deep learning frameworks (PyTorch/TensorFlow/JAX/TensorRT), GPU/hardware-aware performance analysis, profiling, and debugging across software and hardware layers. Base salary range listed: 152,000 USD–241,500 USD.

The role owns vector stores for RAG, the feature store for training and inference, and graph data structures modeling applicants/products/decisions. Responsibilities include data discovery and profiling, building AI-ready datasets with lineage and freshness SLAs, evaluating retrieval/feature quality, and partnering with ML engineers and governance teams. Requires 4+ years of relevant experience and strong skills in Python, SQL, Databricks, vector & graph technologies, and embedding/LLM tooling.

NVIDIA seeks a Senior DL Algorithms Engineer to enable and optimize open LLM/Omni models (e.g., Nemotron, Cosmos) on NVIDIA's accelerated inference software stack. Responsibilities include contributing to open-source frameworks (TRT-LLM, vLLM, SGLang, FlashInfer), profiling and removing bottlenecks across the inference stack, benchmarking, and co-designing with partner teams. Requires 3+ years' experience, strong deep learning and inference background, GPU performance optimization, PyTorch or equivalent, and understanding of computer/GPU architecture.

Lead technical development of scalable recommender, search, personalization, and conversational AI systems. Collaborate with software, ML, and MLOps engineers and product/clinical leads. Requires 5+ years ML engineering experience, 3+ years with modern NLP tools, strong Python skills, experience with embedding/vector search, recommender systems, LLM orchestration/RAG, and familiarity with PyTorch/TensorFlow/scikit-learn/spaCy. US-based candidates only; hybrid for Bay Area residents.

Role responsible for building and improving security-focused ML models (guardrails, classifiers, anomaly detection) across Reddit's GenAI workflows. Owns full ML lifecycle including data ETL, feature engineering, training, evaluation, deployment, monitoring, and retraining. Requires 5+ years of production ML experience and hands-on experience with frameworks such as PyTorch or TensorFlow; remote (United States) position.

Role focuses on research, implementation, and validation of model and algorithm improvements for human-centric video generation and world models. Responsibilities include prototyping spatial multimodal and neural-rendering approaches, improving training and inference efficiency, defining training objectives for sim-to-real/real-to-sim generalization, building domain-specific benchmarks, and translating research into production-grade checkpoints and integrations. Requires PhD or equivalent, 8+ years applied research/industry experience, 3+ years designing/training generative models, and advanced proficiency in Python, PyTorch, C++, and CUDA.