Senior Machine Learning Software Engineer

Senior Machine Learning Software Engineer

About Kilsar

Kilsar is reimagining the future of maintenance, training, and sustainment through applied artificial intelligence. Our mission is to modernize the way technicians, including aircraft maintainers and industrial operators, access and apply complex technical knowledge.

We develop AI-powered software that helps organizations extract structured insights from technical documentation, preserve institutional expertise, and deliver real-time diagnostic guidance in environments where performance, reliability, and security are critical.

Our technology is actively used across aviation, defense, and industrial sectors. It is built to function reliably in hangars, classrooms, and even in fully disconnected field environments. Designed for low-bandwidth, secure, and classified settings, our AI systems are engineered for real-world use where cloud access is not guaranteed.

Role Overview

We are seeking an experienced Machine Learning Engineer to lead the design and implementation of production-ready AI systems focused on document intelligence, agentic workflows, and secure deployment.

This role will involve building systems that transform unstructured technical content into structured and actionable data models such as modules and procedural steps. You will be responsible for developing AI agents capable of multi-step reasoning, integrating rule-based systems with large language models, and orchestrating decision flows that call the appropriate models, tools, or fallback logic. You should be comfortable designing pipelines that operate without access to third-party APIs and instead run fully on-premise or in isolated environments.

You will collaborate closely with backend and data engineers to translate complex and often ambiguous product requirements into working prototypes and scalable workflows. These solutions must meet high standards of performance, reliability, and maintainability.

Key Responsibilities

• Build and deploy retrieval-augmented generation (RAG) pipelines that ground general-purpose models in proprietary documents
• Design agentic workflows using LangChain, LlamaIndex, or similar tools to support multi-step reasoning and tool orchestration
• Engineer hybrid inference strategies that combine lightweight task-specific models, rules, and LLM components based on context and confidence
• Fine-tune LLMs using domain-specific data when appropriate, while using prompt engineering and safety guardrails to handle ambiguous user input
• Translate high-level product goals (e.g., "turn this folder of technical documents into a searchable training module") into structured experiment plans
• Develop data ingestion pipelines to handle scanned PDFs, forms, structured logs, and other industrial data types
• Integrate ML pipelines with backend systems (Django/PostgreSQL) and Snowflake to support end-to-end deployment
• Create observability layers including model latency, drift detection, and human-in-the-loop feedback systems
• Ensure all ML systems meet performance and security constraints for deployment in air-gapped and field environments

Minimum Qualifications

• 5+ years of experience in machine learning, with a strong track record of owning and delivering ML systems end to end
• Hands-on experience with LLM agents or similar systems using LangChain, LlamaIndex, or custom-built orchestration logic
• Demonstrated experience building production-ready RAG pipelines and/or embedding-based semantic search systems
• Familiarity with prompt design strategies including few-shot, chain-of-thought, and fallback logic under safety constraints
• Strong grounding in ML fundamentals, including supervised learning, tree-based models, and linear models
• Proficiency in Python and libraries like PyTorch, Hugging Face Transformers, and Scikit-learn
• Experience with feature engineering for sparse or noisy data
• Proficient in deploying containerized ML systems (Docker/Kubernetes) with CI/CD pipelines and basic observability practices
• Strong experience with ETL tools and orchestration frameworks such as Airflow, Dagster, or Luigi
• Comfort working with data in less-than-perfect form, and a mindset that 80% of the job is cleaning up someone else’s CSV
• U.S. Citizenship and ability to obtain a security clearance (Secret or TS/SCI eligible)

Preferred Qualifications

• Experience deploying models in air-gapped or disconnected environments
• Familiarity with OCR and layout-aware document understanding techniques
• Experience using or building vector databases and custom embedding strategies
• Ability to balance model-driven and rule-based approaches when designing ML systems
• Exposure to aviation, defense, or industrial workflows where high assurance and compliance are critical
• Experience designing feedback loops and evaluation workflows to continuously improve model performance in production

Why Join Kilsar

• Join a high speed, high impact startup
• Solve real-world problems that impact operational readiness and workforce transformation
• Influence the design and deployment of secure AI systems in complex environments
• Collaborate with a deeply technical, agile team working at the intersection of ML, systems, and product
• Enjoy remote flexibility while maintaining strong collaboration within East Coast working hours
• Competitive compensation package: base salary of $120,000 to $180,000 plus equity, commensurate with experience

Job Type: Full-time

Pay: $120,000.00 - $180,000.00 per year

Benefits:

• Health insurance
• Unlimited Paid Time Off
• Bonus opportunities

Work Location: Remote