Must be located in the United States.
This position is remote for now, but candidates must be in Pacific Time zone.
Unfortunately we are not able to sponsor work visa for now
Open 5 monthsWe first saw this posting 155 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
Status in our records
Live
First seen by us
Jun 2, 2026155 days ago
Recorded sightings
65
Last seen by us
Nov 4, 2026
Jun 2, 2026today
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Pay
$166K–$240K is typical
$166K$240K
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Time since we first saw it
Day 155
669
Up longer than most. Similar jobs usually come down between day 6 and day 69.
From 4,339 similar postings that came down. Coming down is not always a hire.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
Python
PyTorch
TensorFlow
Where the posting names them
- Proven track record leading small ML teams or pods in fast-moving, product-driven environments.
- Strong hands-on expertise in Python and modern deep learning frameworks like PyTorch or TensorFlow.
- Experience designing and deploying production-grade models for prediction, ranking, recommendation, or personalization.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Are you excited to build what’s next in AI-driven growth?
We’re on a mission to reinvent how brands maximize ROAS and conversions across major ad networks - Google, Meta, TikTok, Snap, and beyond. If you live at the intersection of machine learning, performance marketing, and real-world impact, this is your playground. Are you interested in living on edge? This is your calling.
What You’ll Own
Lead the ML function end-to-end: from model research and architecture to experimentation, deployment, and scale.
Build next-gen conversion models that fuse CRM, engagement, and structured & unstructured behavioral data to drive smarter conversion semantics. Drive high speed and efficient feedback loop with Ad Networks
Fine-tune and specialize LLMs to power our AI Sales Agents that optimizes business outcome - reasoning, persona alignment and conversion alignment over longer time horizon with seamless CRM integration
Collaborate cross-functionally with product, data, and engineering teams to bring bleeding-edge ML ideas to production.
Mentor and grow a small, world-class team of ML and data engineers.
Why Does This Role Rocks?
You’ll be building core IP - models that directly drive hundreds of millions in ad spend for our customers. We move fast, ship often, and love people who obsess over signal, scale, and impact. If you’ve ever dreamed of owning ML systems that directly move ROI curves, this is it.
What You’ll Work On
Predictive Modeling at Scale: Build and deploy advanced sequence models and transformer-driven architectures for lead scoring, conversion prediction, and conversion value forecasting — models that actually move the ROAS needle.
Model Tuning & Optimization: Own the full lifecycle — from training and hyper parameter tuning to real-time performance monitoring and continuous RoAS improvement across ad platforms.
Opportunity Intelligence: Develop Opportunity Score Models that identify cross-sell and upsell potential, driving long-term customer value (LTV) through smarter activation.
Foundation Model Customization: Fine-tune and extend LLMs with RAG pipelines, optimizing for sales conversion goals and CRM-integrated conversational intelligence.
ML Infrastructure & Automation: Design robust systems for continuous training, monitoring, and rapid experimentation across multimodal datasets (text, voice, and behavioral signals).
Frontier Research & Innovation: Push the limits of representation learning, transformer-based sequence modeling, multimodal embeddings, and sales conversation understanding — bringing the latest breakthroughs straight into production.
What We’re Looking For
We’re looking for a hands-on technical leader who loves building ML systems that drive real business impact. You should be equally comfortable designing models, leading experiments, and shipping production code.
Core Qualifications
8+ years of experience in Machine Learning, Applied AI, or related fields.
Proven track record leading small ML teams or pods in fast-moving, product-driven environments.
Strong hands-on expertise in Python and modern deep learning frameworks like PyTorch or TensorFlow.
Experience designing and deploying production-grade models for prediction, ranking, recommendation, or personalization.
Deep understanding of LLM fine-tuning techniques — including SFT, LoRA, and PEFT — and best practices in prompt engineering.
Solid grasp of data-centric AI workflows, feature engineering, and CRM / engagement data modeling.
Comfortable operating across the full spectrum — from rapid research prototypes to high-scale production systems.
Bonus Points For
Experience with transformer-based sequence models or multimodal architectures.
Familiarity with ad tech, marketing optimization, or conversion modeling.
Background in MLOps, experimentation platforms, or continuous training pipelines.
Background in Ad-tech, Sales-tech, Mar-tech, or CRM data ecosystems.
Eligibility:
Must be located in the United States.
This position is remote for now, but candidates must be in Pacific Time zone.
Unfortunately we are not able to sponsor work visa for now
Benefits:
Competitive Compensation
Startup Equity
Health, Dental and Vision Insurance
Career Growth Opportunities
Equal Opportunity & E-Verify Employer
Scowtt is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status. We are committed to providing a workplace free of discrimination and harassment.
This employer participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. For more information, please visit www.e-verify.gov.
Selected passages from the saved posting. Check the full description for conditions and exceptions.
Pay
No pay amount identified in the saved description.
Location & working pattern
Seattle Metropolitan Area
Eligibility:
- Must be located in the United States.
- This position is remote for now, but candidates must be in Pacific Time zone.
- Unfortunately we are not able to sponsor work visa for now
Work authorization
- This position is remote for now, but candidates must be in Pacific Time zone.
- Unfortunately we are not able to sponsor work visa for now
Benefits:
More source context
Scowtt is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status. We are committed to providing a workplace free of discrimination and harassment.
This employer participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. For more information, please visit www.e-verify.gov.