Machine Learning Engineer

• Serve as founding engineer for RF machine learning at Datacept
• Take responsibility for the cognitive core of electronic warfare systems
• Design and train models from raw I/Q recordings for detection, classification, and emitter understanding
• Research and implement machine-learning methods from other domains to improve data pipelines
• Define evaluation approaches, including test sets, metrics, and real-world scenarios involving domain shift and interference
• Develop compact, deployable expert models for edge hardware
• Continuously test pipelines in real-world conditions and use feedback to improve them
• Integrate new data sources into the data platform
• Set the direction for RF machine learning and make architectural decisions
• Work directly with the founders and build the team as the company grows
• Stay connected to hardware, founders, customers, field sites, and deployments

AI Founding Engineer – RF Machine Learning, SIGINT

• Develop production-grade data and ML pipelines
• Automate data preparation and model training
• Build and operate model serving, including monitoring and alerting
• Integrate ML models into business applications
• Monitor ML models and detect model drift
• Further develop the technology stack and DevOps/MLOps processes
• Build AI solutions such as conversational systems, semantic search, RAG pipelines, and agents for public-sector clients
• Operate AI systems in production under stringent data protection and operational security requirements

Machine Learning Engineer – MLOps Engineer

• Own model pipelines end to end, from partner data landing through data preparation, training, evaluation and release of benchmarked model weights for ADMET and toxicity endpoints
• Build schema and data-contract validation, actionable-error validators, and QC/profiling reports usable without access to partners' raw data
• Make federated runs reproducible and auditable through versioned configurations, pinned data snapshots and model provenance
• Build leakage-safe splitting, held-out benchmarks and honest performance reporting
• Turn prototypes and research code into tested, modular systems and hand them to engineering for scaling into Foundry
• Translate scientific requirements into pipeline behavior with the science team
• Surface data or modeling risks early to partners and internal teams

Senior ML Engineer – ADMET, Toxicity Networks

• Design and maintain NLP pipelines that extract, classify, and enrich structured knowledge from unstructured web content
• Build and operate agentic workflows that automate data collection, validation, and maintenance with minimal human-in-the-loop overhead
• Collaborate with product and data engineering teams to ship ML components that improve data quality and reliability
• Evaluate and integrate frontier models where they reduce complexity or improve coverage, prioritizing production stability
• Own systems through their full lifecycle, from design and deployment to monitoring and iteration

• Develop and optimize forecasting and pricing models, as well as data-driven decision logic
• Work with time series, demand signals, and heterogeneous data sources
• Define features and labels carefully to prevent data leakage
• Evaluate models using backtests, robust metrics, and segmentation analyses
• Support holdout experiments and A/B testing logic
• Monitor offline versus online performance
• Establish best practices for backtesting, reproducibility, and versioning
• Build dashboards and reports that make model and business KPIs transparent
• Improve reproducible workflows, versioned pipelines, and meaningful tests
• Automate recurring analyses and evaluation runs
• Collaborate closely with Product and Engineering
• Help scale the next generation of automated revenue management for happyhotel’s hotel technology platform

Senior Machine Learning Engineer

• Develop and optimize forecasting and pricing models and data-driven decision logics
• Work with time series, demand signals, and heterogeneous data sources
• Define features and labels carefully to prevent data leakage
• Evaluate models through backtesting, robust metrics, and segmentation
• Support holdouts and A/B testing logic
• Balance offline and online performance
• Raise standards for backtesting, reproducibility, and versioning
• Enhance dashboards and reports to make model and business KPIs transparent
• Drive reproducible workflows with versioning, clear pipelines, and meaningful tests
• Automate recurring analyses and evaluation runs
• Collaborate closely with Product and Engineering
• Deliver measurable, data-driven improvements in the Pricing & Revenue context