AI/ML & Algorithm Engineer

Salute Inc.•United States, US,

1d•$0 - $200,000

About The Position

We are seeking an AI/ML and Algorithm Engineer to design, build, and protect the core intelligence layer of our enterprise platform. This role owns the development of multi-agent AI systems, novel scoring and optimization algorithms, large language model applications, and closed-loop autonomous workflows that operate at scale across complex physical environments. You will translate cutting-edge AI research into production systems, identify novel algorithmic approaches worthy of patent protection, and establish the technical foundation for how the organization uses AI to drive measurable operational outcomes. This is a high-impact, technically deep role that sits at the frontier of enterprise AI deployment.

Requirements

8+ years in AI/ML engineering or research, with at least 3 years building and operating production AI systems at a senior IC level.
Deep expertise in large language models and agentic frameworks (LangChain, LangGraph, AutoGen, CrewAI, or equivalent); strong understanding of prompt engineering and model behavior.
Proven ability to develop novel algorithmic approaches - not just apply existing frameworks - and document them rigorously for IP purposes.
Strong ML fundamentals: optimization theory, probabilistic modeling, statistical learning, reinforcement learning, and deep learning architectures.
Proficiency in Python and at least one deep learning framework (PyTorch or JAX); familiarity with ML infrastructure (MLflow, Weights & Biases, Ray, or equivalent).
Experience with vector databases, embeddings, semantic search, and knowledge graph architectures as components of production AI systems.
Demonstrated ability to deploy and maintain AI systems in production: monitoring, versioning, rollback strategies, and performance degradation detection.

Nice To Haves

Named inventor on AI, ML, or algorithmic patents.
Experience with autonomous AI systems, robotic process automation, or AI applied to physical infrastructure, industrial operations, or IoT environments.
Familiarity with RLHF, Constitutional AI, or other alignment and fine-tuning methodologies.
Experience with digital twin architectures, simulation environments, or AI-driven 3D modeling tools in operational contexts.
Background in algorithm design for scheduling, resource allocation, combinatorial optimization, or operational research.
Experience building AI systems that process structured operational data: time-series sensor readings, maintenance logs, energy consumption data, or workforce telemetry.
Familiarity with federated learning frameworks or privacy-preserving ML techniques that enable model training across distributed, domain-owned data sources without centralizing sensitive operational data.
Experience integrating AI agents with federated data access layers - query routing across multiple source systems, semantic data discovery, and context-aware retrieval from heterogeneous operational datastores.
Graduate degree (MS or PhD) in Computer Science, Machine Learning, Mathematics, or a related quantitative field.

Responsibilities

Design and implement production multi-agent AI systems: agent coordination, tool use, context and memory management, structured output, and human-in-the-loop ratification loops.
Develop novel algorithms for scoring, ranking, prioritization, and optimization - including multi-dimensional impact scoring systems that rank ideas or proposals by value, feasibility, revenue potential, and strategic alignment.
Build LLM-powered workflows: retrieval-augmented generation (RAG), prompt pipelines, fine-tuning, structured extraction, and domain-adapted language models for enterprise contexts.
Architect closed-loop AI systems where agent observations feed structured proposals for human review and ratification, then feed back into continuous improvement cycles.
Identify novel algorithmic and AI system designs that represent patentable inventions; author invention disclosures and collaborate with patent counsel through prosecution.
Lead evaluation and benchmarking of AI model performance in production: accuracy, latency, cost, reliability, and domain-specific accuracy metrics.
Drive build-vs-buy-vs-partner decisions for AI components; design integration patterns for third-party foundation models, AI APIs, and specialized ML services.
Establish AI governance practices: model cards, explainability tooling, bias evaluation, auditability, and responsible deployment standards.
Design AI and agent systems that operate across decentralized, federated data domains - enabling intelligent query routing, context assembly, and inference across disparate operational data sources without requiring centralized data pipelines; architect retrieval and grounding strategies that respect data sovereignty and domain ownership as the organization transitions from siloed data stores to a federated intelligence layer.