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# Technical Lead, Machine Learning

- **Company:** Spectraforce
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Training Data, Python (Programming Language), Machine Learning, Software Deployment, Pytorch, Backend, Production Code, Machine Learning Operations, Data Pipelines
- **Published:** July 24, 2026
- **Apply:** http://leoforce.us/Careers/Spectraforce/JobDetails.html?jobid=53fd2d3c-f755-4396-9b53-a7815e735704&OrgId=1&UserId=2346

## About the Role

* You have built or shipped real ML systems used by people, not just demos.
 * You are comfortable working with large models and understanding their failure modes.
 * You write strong, production-grade code and care about system correctness.
 * You are self-directed, pragmatic, and take full ownership of outcomes.
 * You communicate clearly and collaborate well in small, high-trust teams.

## Description

As Technical Lead, Machine Learning, you own the execution layer of Company's intelligence. You translate research direction into reliable, scalable, production-grade ML systems.
This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real-world constraints.

What You'll Do

 * Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment.
 * Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation.
 * Architect and operate scalable inference systems, balancing latency, cost, and reliability.
 * Design and maintain data systems for high-quality synthetic and real-world training data.
 * Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership.
 * Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies.
 * Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products.
 * Make pragmatic trade-offs and ship improvements quickly, learning from real usage.
 * Work under real production constraints: latency, cost, reliability, and safety

Outcomes

 * Research and models reliably translate into production-ready solutions with clear performance and quality targets.
 * ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.
 * Production issues are detected, debugged, and resolved quickly, minimizing user impact.
 * Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction.
 * Iterations on models and systems are measurable, safe, and improve user experience over time.

Tech Stack

 * Python
 * PyTorch / JAX
 * GPU-based training and inference system


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