Hire an AI
Machine Learning Engineer
Find an ML Engineer who trains models that actually work in production.
Machine Learning Engineers design, train, and optimize predictive models. They sit between data scientists who explore and software engineers who ship — translating research into reliable, scalable systems. The best ones are as comfortable in a training loop as they are deploying a model to serve millions of requests.
- ML Engineers with production deployment and model optimization experience
- Candidates screened for practical depth across the full ML lifecycle
- We screen for practical depth, not just certifications
- Contract, contract-to-hire, or full-time — your choice
- US and Canada talent. Shortlist in 5–10 business days.
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About CRM Force
Boutique means something different when the person who built the company has done the work.
Saad Ahmed spent 20 years as a CRM consultant, solution architect, and implementation lead — building marketing automation and CRM systems for clients from start-ups to publicly traded enterprises. He founded CRM Force to bring practitioner-led vetting to a space dominated by generalist agencies.
Every candidate goes through a two-step internal qualification process — reviewed by two separate evaluators before they reach you. We confirm experience, certifications, technical depth, and fit. You receive a shortlist, not a resume dump.
CRM consultants, architects, developers, and admins. Marketing automation specialists. RevOps leaders, business analysts, and project managers. If it touches the CRM or marketing technology stack, we place it. Contract, contract-to-hire, and direct hire — across all seniority levels.
We Recruit for You
Three reasons companies choose us over a traditional recruiter — and what makes the difference in practice.
Vetted by Someone Who Has Done the Work
Saad Ahmed, founder of CRM Force, spent 20 years as a CRM Consultant and Solution Architect — building HubSpot implementations, designing data models, and running client engagements himself. When he reviews a candidate for your role, he is not reading a job description. He is evaluating whether someone can do work he has done himself. That distinction matters.
No Resume Dumps. A Shortlist You Can Act On.
You receive 3 to 5 pre-vetted candidates, each with our written assessment — why we believe they fit your role, where they are strong, and what to probe in the interview. Our internal process puts every candidate through two separate evaluation stages before they reach you. You only meet people who have cleared the bar.
Three Engagement Models. One Standard.
Contract for short and long-term engagements. Contract-to-hire when you want to try before you commit. Direct placement for permanent roles. Every model includes the same sourcing, multi-stage screening, skills assessment, and reference validation. The delivery vehicle changes — the standard does not.
Our Recruitment Process
Four steps from brief to shortlist — no surprises, no padding, no wasted interviews.
We Learn Your Requirements
A focused role briefing call — not a generic intake form. We ask about portal size, Hub configuration, seniority level, technical depth required, team structure, and what a genuinely great candidate looks like for your specific situation. The brief matters more than most clients expect.
We Search Our Network
We work our pre-built database of CRM and HubSpot professionals alongside targeted outreach to passive candidates who are not actively applying. The majority of the people we present are not on job boards — they are available to the right opportunity, if the right person asks.
You Receive a Shortlist
Typically 3 to 5 pre-vetted candidates, each with our written assessment — not just a resume. We tell you why we believe each person is right for your specific role, where they are strong, and what you should probe in the interview. You decide who moves forward.
We Support You to Offer
We coordinate interviews, facilitate debriefs, provide market-rate compensation benchmarks, and support you through the offer stage. The search does not end when we send the shortlist — it ends when you have the right person in the role.
Looking for a Machine Learning Engineer?
We place CRM and Marketing Automation professionals across the US and Canada.
Industries We Serve
Machine Learning Engineers are in demand wherever data can be used to build competitive advantage. We work with organizations of all sizes — from growth-stage startups to publicly traded enterprises — across all major industries.
Not sure what you need? We can help you define the role.
One conversation is usually enough. No commitment required.
Signs You Need a Machine Learning Engineer
A few things we hear from clients before they reach out.
- Your data scientists have models that never reach production
- Model accuracy is good in testing but degrades in live traffic
- Your training pipelines are manual, slow, or not reproducible
- You need a recommendation, ranking, or classification system built properly
- You are scaling from one model to dozens and need infrastructure discipline
What to Look For in an ML Engineer
Strong ML Engineers combine statistical intuition with the engineering discipline to productionize it.
Red Flags to Watch For
- Has only used AutoML tools without understanding the underlying algorithms
- Cannot describe how they handle class imbalance or data leakage
- No experience with model monitoring or retraining triggers
- Unfamiliar with the cost/latency trade-offs of different model architectures
Whatever your sector, we know CRM talent.
Tell us about the role and we will get back to you within one business day.
What a Machine Learning Engineer Does
ML Engineers own everything from data preparation to trained, monitored models in production.
Model Training
Designs and runs training experiments, managing hyperparameters, datasets, and compute resources efficiently.
Feature Engineering
Builds robust feature pipelines that extract predictive signal from raw data and survive production traffic patterns.
Model Evaluation
Validates models beyond accuracy — including business metrics, fairness checks, and offline/online alignment.
MLOps Automation
Automates training, evaluation, and deployment pipelines so models ship faster and reliably.
Production Monitoring
Monitors model performance, data drift, and prediction quality in live traffic with automated alerts.
Model Optimization
Applies quantization, pruning, distillation, and batching to reduce inference cost without sacrificing accuracy.
ML Engineering Across Domains
ML Engineers apply their craft across different problem types. Here are the most common domains we place for.
Recommendation Systems
Personalization and ranking engines for product, content, and search.
- Collaborative filtering
- Two-tower models
- Real-time re-ranking
- Cold start strategies
NLP & Text Models
Text classification, extraction, and generation systems.
- Fine-tuning transformers
- Named entity recognition
- Sentiment and intent
- Text embeddings
Computer Vision
Image and video analysis for classification, detection, and generation.
- CNN architectures
- Object detection (YOLO, DETR)
- OCR and document AI
- Video analytics
Forecasting & Tabular
Predictive models on structured data for business outcomes.
- Time series forecasting
- Gradient boosting (XGBoost, LightGBM)
- Survival analysis
- Anomaly detection
Frequently Asked Questions
- What is the difference between a Machine Learning Engineer and a Data Scientist?
- Data Scientists explore data, build exploratory models, and derive insights. Machine Learning Engineers take those models and make them production-ready — with pipelines, versioning, deployment, and monitoring. ML Engineers write more software engineering code; Data Scientists write more analytical code.
- How long does it take to hire a Machine Learning Engineer?
- CRM Force typically provides a shortlist within 5 to 10 business days. For specialized roles — such as reinforcement learning or large-scale distributed training — expect slightly longer.
- Do you place ML Engineers on contract or full-time?
- Both. Contract placements are common for specific project phases (e.g. building a new model from scratch). Full-time is preferred for companies building a permanent ML team.
- What seniority levels do you place for ML Engineers?
- We place across all levels — from mid-level ML Engineers with 3 to 5 years of experience, to Staff and Principal engineers leading ML platform initiatives.
Tell Us What You’re Looking For
Share the role, your timeline, and any must-haves. No commitment required.