Additionalinformation

Additional Information

Base pay is one part of our total compensation package. Pay is established on an individual basis after considering multiple factors such as relevant experience, education, and other qualifications. In addition, we take into account geographical differentials and make sure pay is equitable with our current staff. For this position, our hiring range for base annual pay is estimated to be CAD $160-200k at the time of this posting Performance-based discretionary bonuses and variable pay plans are available for some positions. If you require accommodation to complete any part of the application process or need an alternative manner to apply, please contact us at [email protected] or call (855) 469-4737. Work Environment

This job is available remotely. When not remote, this job operates in a professional office environment. This role routinely uses standard office equipment such as computers and phones.

Physical Demands

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. The employee is required to sit for prolonged periods of time; balance; and stoop, kneel, crouch or crawl. The employee must occasionally lift and/or move up to 25 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception, and ability to adjust focus. The employee is required to operate essential office equipment, such as computer/laptop, phone, keyboard, mouse, and other office equipment on a regular basis.

EEO Statement

Wiser Solutions, LLC is an Equal Opportunity Employer and prohibits Discrimination, Harassment, and Retaliation of any kind. Wiser Solutions, LLC is committed to the principle of equal employment opportunity for all employees and applicants, providing a work environment free of discrimination, harassment, and retaliation. All employment decisions at Wiser Solutions, LLC are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, sex, national origin, family or parental status, disability, genetics, age, sexual orientation, veteran status, or any other status protected by the state, federal, or local law. Wiser Solutions, LLC will not tolerate discrimination, harassment, or retaliation based on any of these characteristics. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Companydescription

Company Description

Wiser Solutions is a competitive intelligence platform that brands, retailers, and marketplace sellers use to win more sales online. Our products cover price intelligence, market intelligence, and MAP intelligence. They give customers real-time visibility into pricing, product availability, promotions, and reseller behavior across the digital shelf, along with the tools to act on it. Brands and retailers around the world rely on Wiser.

Jobdescription

Job Description

Wiser Solutions is hiring a Senior ML Engineer to help build our customer-facing AI platform and get the first features into customers' hands.

Our data runs at real scale: well over 100 million product and pricing records a day from retailers and marketplaces worldwide. The problems are concrete ones like product matching, classification, and attribute extraction. The right answer is often a mix of trained models, embeddings, retrieval, and LLMs, and cost per record matters as much as accuracy.

We want someone who uses AI first, moves fast, and would rather show a working prototype than a slide deck. You'll own ML systems end to end, from idea to experiment to production, and keep improving them once they're live. You don't need to be a data engineer. You need to be the person who turns a customer problem into a model that works.

What You Will Do

• Take customer problems from idea to working prototype quickly, prove or kill an approach with data, and move what works into production

• Train, fine-tune, and evaluate models for matching, classification, and extraction problems. This covers classical ML, embedding models, and LLMs, and you pick the simplest approach that meets the quality and cost bar

• Design and ship RAG systems, embedding-based retrieval, and LLM-powered features on AWS

• Own the full ML lifecycle: experiment tracking, model versioning, deployment, monitoring, and retraining

• Build evaluation that tells us whether something works: labeled golden sets, offline metrics (precision, recall), LLM-as-judge where it fits, and shadow or A/B testing before rollout

• Monitor models in production for drift, output quality, latency, and cost per prediction, and fix what you find

Qualifications

• 7+ years in ML engineering, applied data science, or AI engineering

• A track record of shipping ML or AI systems to production and owning them after launch. You can walk us through the architecture, the tradeoffs, and the metrics that showed it worked

• Hands-on experience training and evaluating models: feature engineering, experiment design, choosing metrics, handling class imbalance and noisy labels, and knowing when a model is good enough to ship

• Hands-on experience building production applications with LLMs, including RAG, embeddings, and vector databases

• Working knowledge of MLOps practices and tooling, such as MLflow, SageMaker Pipelines, Weights & Biases, or similar

• Experience with AWS for ML workloads (SageMaker, Bedrock, Lambda, ECS/EKS, S3)

• Strong Python and solid engineering habits: version control, testing, CI/CD, and code review

• A habit of building quickly to learn: you'd rather test an idea in two days than debate it for two weeks

• Daily use of AI coding assistants (Cursor, Claude Code, GitHub Copilot, or similar) and curiosity about new AI tools as they come out

Preferred:

• Experience with LLM fine-tuning, and a clear sense of when to fine-tune versus prompt engineer versus train a smaller task-specific model

• Experience keeping inference cost down at high volume through distillation, quantization, caching, or tiered model routing

• Background in retail, e-commerce, or product data (catalog matching, taxonomy mapping, attribute extraction)

• Experience with entity resolution or similarity search at scale

• Comfort working with data pipelines and orchestration tools like Airflow (helpful, not required)