Senior Data Scientist - Core AI R&D

• 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.

• 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.

Our Client Infinite Uptime is hiring a Senior Data Scientist - Core AI R&D as part of its AI/ML Data Scientist track, focused specifically on New Product Development in AI.

This is not a generic AI/ML/Data Science or GenAI role.

The ideal candidate will bring research-grade technical depth combined with product understanding and production-level deployment experience. We are specifically looking for candidates who can take advanced AI research from research * prototype * production deployment, rather than research and publication alone.

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.

Candidates should demonstrate deep expertise in at least one of the following three specializations