Job Overview
Location
Remote
Job Type
Full-time
Category
Machine Learning Engineer
Date Posted
January 5, 2026
Full Job Description
đź“‹ Description
- • Own the end-to-end lifecycle of AI/ML systems that power Asteri’s Work Intelligence & Orchestration Platform, from ideation and data collection to model training, deployment, monitoring, and continuous improvement in real enterprise environments.
- • Architect and implement production-grade retrieval-augmented generation (RAG) pipelines, policy-driven agents, and workflow orchestration engines that operate at the scale of Fortune-500 data volumes while meeting strict latency, cost, and compliance constraints.
- • Collaborate daily with backend, platform, and frontend engineers to integrate LLM-based capabilities—such as semantic search, summarization, and autonomous task agents—into the core product, ensuring seamless user experiences and rock-solid reliability.
- • Design and run rigorous offline and online evaluation suites for every AI component, establishing SLOs for accuracy, latency, throughput, and cost; surface actionable insights that guide iterative model tuning and system-level optimizations.
- • Build and maintain robust CI/CD, testing, versioning, and rollback strategies specifically tailored for AI artifacts (models, prompts, embeddings, datasets), reducing deployment risk and accelerating iteration cycles.
- • Continuously monitor production AI behavior using real-time telemetry, anomaly detection, and human-in-the-loop feedback loops; proactively detect drift, bias, or degradation and implement fixes before customers are impacted.
- • Translate cutting-edge research in generative AI, retrieval, and agentic systems into pragmatic product features, balancing innovation with enterprise-grade stability, security, and auditability.
- • Partner with product managers, UX researchers, and customer success teams to deeply understand enterprise workflows, then translate complex business requirements into elegant, scalable AI solutions that deliver measurable ROI.
- • Champion engineering excellence across the team by conducting design reviews, writing clear technical specs, and mentoring peers on best practices for building and operating AI at scale.
- • Stay ahead of the rapidly evolving AI landscape—evaluating new foundation models, tooling, and optimization techniques—and make pragmatic build-vs-buy decisions that keep Asteri at the forefront of applied AI.
🎯 Requirements
- • Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience.
- • 5+ years of professional experience spanning both software engineering and applied AI/ML development.
- • Strong proficiency in Python and a proven track record of shipping production software systems.
- • Demonstrated experience deploying and operating ML or LLM-based systems in cloud environments (AWS, GCP, or Azure).
- • Nice-to-have: hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or Hugging Face, plus deep familiarity with retrieval-augmented generation (RAG) optimization techniques.
🏖️ Benefits
- • Competitive compensation package with equity, reflecting the high impact and ownership of this role.
- • Fully remote-first culture with flexible hours and a stipend for home-office setup and co-working memberships.
- • Direct ownership of critical AI systems that shape how AI is safely and effectively deployed alongside humans at enterprise scale.
Skills & Technologies
About Asteri AI, Inc.
Asteri delivers intelligent work orchestration solutions for enterprises, particularly within the life sciences industry, empowering them to understand and optimize task-level operations. Their proprietary Work Ontology Knowledge Graph provides a unified, living model of how work actually happens, enabling the detection of patterns, duplication, and opportunities for AI augmentation. Asteri then orchestrates execution across both human teams and AI agents, ensuring human judgment remains central while continuously adapting to evolving workflows. This platform is designed to help large organizations, including F500 enterprises, manage complex operations, reduce repetitive tasks, and operationalize AI impact, facilitating seamless collaboration across diverse teams by providing clarity into work execution at scale.
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