Machine Learning Engineer

Merik Solutions; United States; On-site; USD 150,000 - 190,000; Full time

Job summary

Merik Solutions is seeking a senior ML engineer to own the full model lifecycle from data preparation to deployment and monitoring. You will deliver reliable ML systems for forecasting, classification, and recommendation use cases, emphasizing MLOps as much as model accuracy.

The role requires hands-on experience with Python, SQL, and cloud ML platforms, plus the ability to communicate trade-offs to non-technical stakeholders. Travel and remote-work details not specified.

Qualifications

• 4+ years in ML engineering or applied data science.
• Strong Python and SQL with production ML experience.
• Experience with at least one major cloud ML stack (SageMaker, Vertex AI, or Azure ML).
• Solid understanding of feature engineering, evaluation, and experiment design.
• Authorization to work in the United States.

Responsibilities

• Train, evaluate, and deploy ML models for forecasting, classification, and recommendation use cases.
• Build reproducible training pipelines and feature stores.
• Set up model monitoring, drift detection, and automated retraining.
• Work with client data teams to productionize prototypes.
• Present model behavior and trade-offs to non-technical stakeholders.

Skills

Python

SQL

ML engineering

Production ML

Experiment design

Tools

SageMaker

Vertex AI

Azure ML

Job description

United States Full-time $150,000 - $190,000 Posted Jul 2, 2026

Work with our US-based team and enterprise clients in finance, healthcare, and retail to take ML models from notebook to production.

You will own the full model lifecycle: data preparation, training, deployment, monitoring, and iteration.

This role suits engineers who care as much about reliability and MLOps as about model accuracy.

What your days will look like

• Train, evaluate, and deploy ML models for forecasting, classification, and recommendation use cases.
• Build reproducible training pipelines and feature stores.
• Set up model monitoring, drift detection, and automated retraining.
• Work with client data teams to productionize prototypes.
• Present model behavior and trade-offs to non-technical stakeholders.

You might be a great fit if you...

• 4+ years in ML engineering or applied data science.
• Strong Python and SQL with production ML experience.
• Experience with at least one major cloud ML stack (SageMaker, Vertex AI, or Azure ML).
• Solid understanding of feature engineering, evaluation, and experiment design.
• Authorization to work in the United States.

Bonus points (but not required!)

• Experience in regulated industries (finance or healthcare).
• Familiarity with streaming inference and real-time features.
• Publications, talks, or open source work in ML.

Tools you'll work with

We respect your time and want this to feel like a real conversation, not an interrogation. Here's what to expect:

1

Let's chat

A relaxed 30-minute call where we get to know each other. We'll share what we're building and hear about what excites you

2

A 60-minute session to share stories about projects you've worked on. Ask our engineers anything — seriously, anything

3

Build something together

We'll tackle a real problem as a team. No gotchas, no trick questions — just a chance to see how we work together

4

Meet your future teammates

Hang out with the folks you'd be working with day-to-day. See if we're the right crew for you