Senior Machine Learning Engineer

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.

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

• 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, Sage Maker Pipelines, Weights & Biases, or similar

• Experience with AWS for ML workloads (Sage Maker, 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, Git Hub 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…