Machine Learning Engineer (NLP/Multi Agent Systems)

About us: At Spin, recruitment is about more than filling roles; it’s about creating real human connections. We partner with leading U.S. companies to help them hire exceptional talent through a true direct-hire process, ensuring candidates join our clients’ teams directly while we support them throughout the journey.

About our partner: Join a team that operates at the intersection of AI and modern marketing, building a smarter, more agile operating model for go-to-market teams. With a growing portfolio of companies, they empower organizations to make better decisions earlier, connect audience intelligence to execution, and move faster with greater clarity.

As the company continues to scale, they are building a dynamic, always-on content engine that powers the full brand narrative—spanning executive voice, social, web, PR, events, sales enablement, investor communications, and portfolio storytelling.

About the role: We're seeking a Machine Learning Engineer with deep expertise in natural language processing and modern transformer architectures, who is ready to work with new technologies and research in a forward-thinking organization that's always pushing boundaries. Here, you will take complete end-to-end ownership of projects across the model lifecycle from research to production. You'll be joining a rapidly growing team working at the forefront of new technology, solving the challenges that impact the company’s products, and ultimately, delivering amazing user experiences.

What You'll Do

Model Development:
Train, fine-tune, and evaluate transformer-based language models for production use cases.
Data Pipelines:
Build datasets and labeling pipelines; own data quality, curation, and preprocessing.
Evaluation:
Run rigorous offline benchmarks, build evaluation harnesses, and define quality metrics.
Production Handoff:
Partner with applied engineers to move models into production and monitor post-launch performance.
Research:
Stay current with NLP research and prototype new techniques relevant to our product.
Performance Optimization:
Profile and optimize training and inference workloads on GPU infrastructure.
Collaboration:
Work closely with applied engineers, MLOps, product managers, and fellow ML engineers to deliver end-to-end solutions.

What You Bring

MS/PhD in CS, ML, computational linguistics, or equivalent practical experience.
Core ML:
Hands-on experience with PyTorch and Hugging Face Transformers.
Strong understanding of modern LLM architectures and training techniques.
Experience with distributed training and GPU workloads.
Track record of shipping ML models that ran in production.
Data:
Experience building and managing training datasets at scale.
Familiarity with data labeling, annotation, and quality processes.
Tooling:
Comfort with experiment tracking tools (Weights & Biases, MLflow).
Strong Python skills and familiarity with the modern Python data stack.
Version Control:
Proficient with Git for code and model management.
Other:
Excellent problem-solving, communication, and teamwork skills.
Ability to communicate research findings to technical and non-technical audiences.
Bonus:
Publications at NLP/ML venues (ACL, EMNLP, NeurIPS, ICLR).
Experience with RLHF, DPO, or other alignment techniques.
Familiarity with vector databases and retrieval systems.

Additional Skills:

Knowledge of cloud ML services (SageMaker, Vertex AI) is advantageous.
Understanding of responsible AI and model evaluation best practices.

Salary & Benefits

Location: Remote in Canada
Salary: Competitive Salary
This is a full-time, long-term position.
The position is immediately available.
Work from home.
Monday through Friday - EST.
Additional perks and benefits.

Ready to apply?

We'd love to hear your story. The next step is a short application where you can share more about your background and upload your resume. Take your time — we read every response carefully.

ML Engineer – NLP/Multi Agent Systems