Lead Machine Learning Engineer

Yahara Software•Madison, WI

3d•Hybrid

About The Position

Lead Machine Learning Engineer Know your way around a model from first dataset to final evaluation? Come lead a team building machine learning that solves real problems for life sciences and scientific instrumentation clients. About Yahara: We help scientific organizations solve hard problems and move their industries forward, from life sciences startups and CROs to the teams building lab software and scientific instruments. It's work we're proud of, and we're excited about the successes still ahead. Join a team recognized as one of Madison Magazine's Best Places to Work, where innovation thrives and collaboration drives success. We don't just build software. We empower people and transform industries. Important Notes about this Position: This is a hybrid position based out of our office in Madison, WI. We are unable to provide sponsorship. The Opportunity: You’ll lead the technical side of client projects where machine learning is the heart of the solution. Some days that means training a model from scratch. Other days it means picking the right existing model and making it earn its keep. You’ll set the technical direction, grow the engineers around you, and work alongside our DevOps team to get models running in production. Our Approach: We build on strong engineering fundamentals: solid methodology, rigorous testing, and sound architecture. We use AI tools like Claude and Codex where they speed up delivery, while keeping critical thinking and engineering discipline at the center. We own the quality, accuracy, and understanding of the code we ship.

Requirements

6+ years in software engineering, data science, or ML engineering.
Hands-on experience building and training ML models that shipped.
Time leading technical work for a team, formally or informally.
A broad ML toolkit rather than a single narrow specialty.
Comfort making sound calls when the data or requirements are fuzzy.
Honesty about what a model can and can’t do.
Genuine interest in helping other engineers grow.
Ability to explain technical tradeoffs to non-technical stakeholders.

Nice To Haves

Experience with life sciences organizations, lab systems, or regulated scientific software.
Computer vision, NLP, or time-series modeling.
MLOps practices like experiment tracking and model versioning.
Consulting or client-facing project experience.
Cloud ML certifications (Azure or AWS).

Responsibilities

Design, train, and evaluate models for classification, forecasting, anomaly detection, and more.
Choose the right approach for the problem, from classical ML to deep learning.
Decide when a pretrained model beats a custom one, and explain why.
Shape messy client data into features a model can actually learn from.
Define how models get measured, so everyone knows what “good” looks like.
Partner with DevOps to deploy, monitor, and retrain models in production.
Own technical delivery for your project team: quality, pace, and direction.
Mentor the engineers on your team through code reviews and one-on-one problem solving.
Help engineers work through hard problems instead of solving them for them.
Present results, tradeoffs, and risks to clients in plain language.
Join pre-sales conversations with solution outlines and rough estimates.
Document key decisions in ADRs so the next team knows why, not just what.

Benefits

20+ days of PTO accruable in the first year!
Comprehensive health insurance (Medical, Dental, Vision) with HMO and PPO options
Health Savings Account (HSA) with annual employer contributions
401(k) with guaranteed company match (Traditional and Roth options)
100% company-paid short-term and long-term disability
100% company-paid life insurance with option to increase coverage
100% company-paid identity theft protection
On-site gym with basketball court
Hybrid/remote schedule with home office stipend
Fresh fruit, healthy snacks, and beverages provided daily
Bonus certification program (Microsoft, AWS, PMP, IIBA, etc.)
Employee Assistance Program (counseling, legal, financial services)
Monthly and Quarterly Recognition Awards with spot bonuses
Company-supported community outreach and volunteer opportunities
Employee-run committee involvement opportunities
Collaborative culture founded on realized values and incredible people