Senior Machine Learning Engineer

Wiser Solutions•Ontario, CA

22h•Remote

About The Position

Wiser Solutions is hiring a Senior Machine Learning Engineer to build its customer-facing AI platform and deliver initial features to customers. The company processes well over 100 million product and pricing records each day from retailers and marketplaces worldwide. Key challenges include product matching, classification, and attribute extraction, using an appropriate mix of trained models, embeddings, retrieval, and large language models while balancing accuracy and cost per record. The role owns ML systems end to end, from identifying a customer problem and testing an approach through production deployment and ongoing improvement. The engineer is expected to use AI tools, move quickly, validate ideas with data, and build practical prototypes. The job is available remotely; when performed outside a remote setting, it operates in a professional office environment.

Requirements

At least 7 years of experience in ML engineering, applied data science, or AI engineering.
Track record of shipping ML or AI systems to production and owning them after launch, including explaining architecture, tradeoffs, and evidence of success.
Hands-on experience training and evaluating models, including feature engineering, experiment design, metric selection, handling class imbalance and noisy labels, and determining when a model is ready 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 using AWS for ML workloads, including SageMaker, Bedrock, Lambda, ECS/EKS, or S3.
Strong Python skills and sound engineering practices, including version control, testing, CI/CD, and code review.
Ability and inclination to build quickly to test ideas.
Daily use of AI coding assistants such as Cursor, Claude Code, GitHub Copilot, or similar, and curiosity about emerging AI tools.

Nice To Haves

Experience with LLM fine-tuning and understanding when to fine-tune, prompt engineer, or train a smaller task-specific model.
Experience reducing inference costs at high volume through distillation, quantization, caching, or tiered model routing.
Background in retail, e-commerce, or product data, including catalog matching, taxonomy mapping, or attribute extraction.
Experience with entity resolution or similarity search at scale.
Comfort working with data pipelines and orchestration tools such as Airflow; helpful but not required.

Responsibilities

Take customer problems from idea to working prototype, use data to validate or reject approaches, and move successful solutions into production.
Train, fine-tune, and evaluate models for matching, classification, and extraction, selecting an approach that meets quality and cost requirements.
Design and ship RAG systems, embedding-based retrieval, and LLM-powered features on AWS.
Own the ML lifecycle, including experiment tracking, model versioning, deployment, monitoring, and retraining.
Build evaluation methods such as labeled golden sets, offline precision and recall metrics, LLM-as-judge where appropriate, and shadow or A/B testing before rollout.
Monitor production models for drift, output quality, latency, and cost per prediction, and address issues found.