Top Hybrid Machine Learning Engineer Jobs

Design, develop, and deploy advanced ML models for smart buildings and industrial automation. Integrate models into BMS/ICS/SCADA/PLC environments, implement real‑time and batch inference, collaborate with data engineering and MLOps for pipelines and deployment, optimize inference performance, lead PoCs, mentor junior engineers, and ensure cybersecurity and industrial safety compliance.

The Staff Machine Learning Engineer will design and implement ML systems, build scalable architectures, collaborate with data teams, and drive technical strategy for AI applications.

The Senior ML Engineer will architect data pipelines, develop classification algorithms, manage data integration, and mentor team members in a cross-functional setting.

The Senior Architect will lead client projects focusing on AI/ML solutions, including model design, implementation, and deployment across various platforms, while mentoring junior team members.

Design and build end-to-end user features and multi-step AI workflows. Architect reliable, private, high-performance systems and infrastructure. Fine-tune and evaluate LLMs, mitigate hallucinations, and translate research into production. Drive product-focused engineering decisions and benchmarks to improve data and model performance.

Design, build, and tune hybrid retrieval and RAG systems combining Elasticsearch, vector/embedding search, and re-ranking models. Improve relevance using query analytics, behavioral signals, experimentation, and evaluation metrics to enhance search quality and Generative AI responses. Collaborate with AI engineers, platform teams, and product stakeholders.

Build and scale Astronomers context layer for data: design retrieval and semantic search systems, experiment with LLMs and embeddings, turn research into product features, and influence architecture for AI-driven data tools used across organizations.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Own AI product strategy and roadmap across clinician- and member-facing surfaces. Partner with engineering, applied ML, design, and clinical teams to ship, scale, and measure LLM/ML-powered features; set safety and quality guardrails, run evals, make model build-vs-buy decisions, and translate ML capability into clinical workflow outcomes.

Embed with clinical and operations teams to identify high-impact problems and build production AI systems. Design and deploy LLM/RAG-driven tools, data pipelines, APIs, models, and user-facing applications using messy clinical and claims data. Iterate quickly based on real-world feedback and operational needs.