Senior AI/ML Engineer, Applied Machine Learning

Trase SystemsRemote - Seattle, WA or Remote (USA)$175k – $225k+ Equity3w ago

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Requirements

ML Systems Expertise: Proven experience in developing, optimizing, and deploying ML systems in production environments.
ML Systems Expertise:
Model Training and Pipeline Mastery: Strong background in building and managing end-to-end training pipelines for ML models.
Model Training and Pipeline Mastery:
LLM Fine-Tuning: Extensive knowledge and hands-on experience in fine-tuning large language models for specific use cases and optimizing them for targeted outcomes.
LLM Fine-Tuning:
Framework Proficiency: Skilled in ML frameworks such as TensorFlow, PyTorch, or similar tools used in ML model development.
Framework Proficiency:
Programming Skills: Proficient in Python with a focus on writing efficient, clean, and maintainable code for ML applications.
Clear Communicator: Ability to distill complex ML concepts for both technical and non-technical audiences.
Clear Communicator:
Educational Background: Bachelor’s or Master’s degree in Machine Learning, Computer Science, Data Engineering, or a related field.
Educational Background:
Impactful ML Solutions: A track record of delivering and implementing machine learning solutions that have successfully driven value in real-world applications.
Impactful ML Solutions:
Active Secret or Top Secret Clearance
Active Secret
Top Secret Clearance

Architect, Build, and Optimize ML Systems: Develop and deploy robust ML models that deliver high-impact results for real-world applications.
Architect, Build, and Optimize ML Systems:
Training Pipeline Development: Design and implement efficient, scalable pipelines to train and retrain ML models, ensuring they meet business needs.
Training Pipeline Development:
Fine-Tuning Large Language Models (LLMs): Continuously fine-tune LLMs to align with specific enterprise requirements, enhancing accuracy, relevance, and performance.
Fine-Tuning Large Language Models (LLMs):
Feedback Systems Design: Implement and refine feedback loops to iteratively improve the effectiveness of ML models over time.
Feedback Systems Design:
Cross-Functional Collaboration: Work closely with product and business teams to understand and translate requirements into ML solutions that provide tangible outcomes.
Cross-Functional Collaboration:
Stay Current with ML Advancements: Keep up with the latest in ML research and best practices, applying insights to our ML infrastructure to ensure it remains at the cutting edge.
Stay Current with ML Advancements:
Mentorship and Knowledge Sharing: Guide and mentor junior team members, fostering a culture of continuous improvement and technical growth.
Mentorship and Knowledge Sharing:
Technical Communication: Clearly and effectively communicate ML methodologies, results, and insights to non-technical stakeholders.
Technical Communication:

100% employer-paid, comprehensive health care including medical, dental, and vision for you and your family.
Paid maternity and paternity for 14 weeks at employees' normal pay.
Unlimited PTO, with management approval.
Opportunities for professional development and continued learning with educational reimbursements.
Optional 401K, FSA, and equity incentives available.
Mental health benefits through TARA Mind.
If you want to be on the cutting edge of technology, building AI solutions for the future, and are up for a challenge, let’s talk!
Salary Range: $175,000-$225,000. This represents the typical salary range for this position based on experience, skills, and other factors.