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abra R&D is looking for a Senior Embedded Engineer! abra R&D is looking for an Embedded Engineer to help build the first real-time database purpose-built for AI agents at scale. Designed around time-series and unstructured data, it leverages a custom storage format optimized for append-heavy, real-time workloads. Joining the team means working on a high-performance execution engine built with vectorized execution, SIMD, and cache-efficient memory layouts, enabling extreme low-latency and high-throughput performance at scale. The system is engineered to fully utilize modern hardware, with deep optimization across CPU cache layers (L1 / L2 / L3), pushing the limits of real-time data processing. What You’ll Do
* Design and implement core components of the database engine (query engine, execution engine, storage engine)
* Build vectorized execution pipelines optimized for SIMD
* Design and evolve a time-series optimized storage engine (custom on-disk + in-memory formats)
* Work on unstructured / event-driven data representations and efficient indexing/querying strategies
* Own memory layout, compression, and data defragmentation
* Develop cache-aware / cache-efficient data structures with deep understanding of CPU cache behavior
* Implement distributed data primitives: sharding, partitioning, replication, and data locality
* Profile and optimize performance at the CPU level (cache misses, branch prediction, memory bandwidth)

abra R&D is looking for a Data Scientist! abra R&D is looking for a Data Scientist who will take part in building the next?generation Database built from scratch for AI Agents agentic analytics platform that enables data?driven decision making on large?scale, real?time data. The product focuses on extracting insights, understanding patterns, and supporting decisions through advanced analytics, experimentation, and statistical modeling. The Data Scientist role focuses on analysis, modeling, experimentation, and insights , rather than low?level algorithm or infrastructure development. What you’ll do:
* Analyze large?scale structured and unstructured datasets to extract actionable insights
* Build and evaluate statistical and machine learning models to support analytics and decision systems
* Apply causal inference methodologies to understand relationships in data and measure impact
* Develop time?series models for forecasting, trend analysis, and anomaly detection
* Define statistical methodologies used across analytics and decision?making processes
* Design and analyze experiments (A/B tests, uplift modeling, evaluation frameworks)
* Create data?driven feedback loops to improve product decisions
* Collaborate with AI and engineering teams to translate analytical insights into product features
* Communicate findings clearly to technical and non?technical stakeholders