Note: The job is a remote job and is open to candidates in USA. Infinite Uptime is hiring a Senior Data Scientist – Core AI R&D for its AI/ML Data Scientist track focused on new product development in AI. The role will translate state-of-the-art AI research into customer-facing product capabilities and take advanced research from prototype through production deployment while collaborating with Product, R&D, Applied Product and Platform Engineering teams.
Responsibilities
- The role will work closely with Product, R&D, Applied Product and Platform Engineering teams to translate state-of-the-art AI research into customer-facing product capabilities
- Take advanced AI research from research → prototype → production deployment
- Building/adapting models using real-world data
- Building and shipping real AI/ML systems
Skills
- • 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
- • Industrial / IoT time-series
- • Sensor data at scale
- • High-frequency telemetry
- • Vibration analysis
- • Condition monitoring
- • Remaining Useful Life (RUL)
- • 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
- • Operations research
- • Mixed-integer programming
- • Scheduling and resource allocation
- • Robotics control
- • Sim-to-real transfer
- • Prescriptive analytics
- • 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
- • 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
- • DEXPI
- • ISO 15926
- • P&ID parsing
- • OPC UA
- • MIMOSA
- • Equipment ontologies
- • 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
- • 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
- • No visa sponsorship
- • 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)
- • Production RL deployments – not research/papers alone
- • Maintenance
- • Scheduling
- • Supply chain
- • Energy
- • Industrial applications
- • Hybrid neuro-symbolic approaches
- • Knowledge-augmented LLMs
- • Cross-domain transfer using shared KG embeddings
- 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
- • Some overlap with India working hours is preferred , but is not mandatory for exceptional candidates
Benefits
- Remote international opportunity, with geographic restrictions.
- Full-time engagement through PEO/EOR via Deel.
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