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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - Core AI R&D - **Company:** Paradigm - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Cloud Computing, Query Languages, Graph Database, Integer Programming, Python (Programming Language), Neo4j, Azure Machine Learning, Software Deployment, Software Engineering, SPARQL, Reinforcement Learning, Chatbots, Pytorch, Large Language Models, Multi-Agent Systems, Kepler, Kaggle, Knowledge Representation, AISTATS, Machine Learning Operations, Unsupervised Learning - **Published:** September 13, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pjrc0v1ewy ## About the Role * Hybrid neuro-symbolic approaches * Knowledge-augmented LLMs * Cross-domain transfer using shared KG embeddings Core Requirements * 5+ years of applied ML/AI experience * Research-grade depth in at least one of the three specializations above * Strong Python and modern deep-learning expertise; PyTorch preferred * Hands-on experience building/adapting models using real-world data * Proven research-to-production capability * Strong product understanding alongside research expertise * Production-level AI/ML deployment experience * Experience working closely with Product and R&D teams * Strong software engineering practices * Cloud / managed ML platform experience * Experience building and shipping real AI/ML systems Particularly Valuable Background Experience in predictive maintenance, condition monitoring, vibration analysis, industrial sensor data, high-frequency telemetry, RUL or industrial AI will be highly valued. Candidates with relevant condition-monitoring research or publications are particularly encouraged to apply. ## Description * Time-Series Foundation Models (TSFMs): TimesFM, Chronos, Moirai, Lag-Llama, TimeGPT, MOMENT, TOTO, Granite TimeSeries, UniTS * Self-supervised pretraining for time series * Transformer architectures: PatchTST, iTransformer, Crossformer * State-space models: S4, S5, Mamba * Near-real-time, continual and online learning * In-context learning for time series * Drift detection and adaptation * Long-horizon forecasting * Anomaly detection on sensor data Relevant domain experience: * Industrial / IoT time-series * Sensor data at scale * High-frequency telemetry * Vibration analysis * Condition monitoring * Remaining Useful Life (RUL) Preferred: * Time-series research/publications at NeurIPS, ICML, ICLR, KDD, AAAI or AISTATS * Condition-monitoring research or publications * Physics-informed ML (PINN/PIML) * Diffusion models for time series * Causal inference for root-cause analysis (RCA) Specialization 2 - Reinforcement Learning & Prescriptive Maintenance Strong signals: * Frameworks: Ray RLlib, Stable Baselines3, CleanRL, Tianshou, Gymnasium / OpenAI Gym * Algorithms: PPO, SAC, DQN, A2C/A3C, DDPG, TD3 * Offline RL: CQL, IQL, BC * RLHF, DPO and preference learning * Multi-objective / constrained RL * Inverse RL / imitation learning * Multi-agent RL * Bandit algorithms * Hierarchical RL Relevant adjacent experience: * Operations research * Mixed-integer programming * Scheduling and resource allocation * Robotics control * Sim-to-real transfer * Prescriptive analytics Strong preference for: * Production RL deployments - not research/papers alone * Maintenance * Scheduling * Supply chain * Energy * Industrial applications Specialization 3 - Knowledge Graphs, GNN & Multi-modal Contextualization Graphs / Knowledge Representation: * Graph databases: Neo4j, TigerGraph, ArangoDB, JanusGraph, Amazon Neptune * Query languages: Cypher, SPARQL, Gremlin * Semantic technologies: RDF, OWL, ontologies, SHACL * GNN frameworks: PyTorch Geometric (PyG), DGL, Spektral * Architectures: GCN, GraphSAGE, GAT, GIN, PNA, R-GCN * KG embeddings: TransE, ComplEx, RotatE, DistMult, KEPLER Multimodal + RAG: * CLIP, BLIP, LLaVA, Flamingo, ImageBind * Multi-modal RAG * Document understanding: Donut, LayoutLM * Vector databases: Weaviate, Pinecone, Qdrant, Chroma, pgvector, Milvus * RAG frameworks: LlamaIndex, LangChain, Haystack, This will likely not be the right fit for candidates whose experience is primarily: * NLP/chatbots without meaningful time-series, RL or graph depth * BI, analytics or dashboard development without advanced modeling * Notebook/Kaggle projects without production deployment * Consulting without ownership of a shipped ML system * Pure academic research without industry/applied collaboration * Generalist ML without research-grade depth in at least one of the three core specializations Location & Engagement This is a remote international opportunity, with geographic restrictions. * Candidates must be located outside India and Asia * Selected locations across Europe, the Middle East, Africa and other eligible regions can be considered * Candidates must be located in a region where international/USD payment and employment arrangements can be supported * Candidates in active war/conflict locations cannot be considered * Belarus is excluded * Some overlap with India working hours is preferred, but is not mandatory for exceptional candidates * No visa sponsorship * Full-time engagement through PEO/EOR via Deel If your background combines research-grade AI expertise, product thinking and real-world production deployment, I'd love to connect. ## Related Videos - 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