Hire Machine Learning Engineers Who Deploy, Not Just Train
The hard part of ML was never fitting a model — it's everything around it: feature pipelines, deployment, monitoring, retraining. Hire machine learning engineers through Aizecs and you get people who own that full lifecycle, from classical models and deep learning through LLM fine-tuning and the MLOps that keeps it all honest in production. Named profiles reach you within 48 hours.
48h
Matched ML profiles
5+ yrs
Median production ML experience
$28/hr
Mid-level rates from
4+ hrs
US Eastern overlap daily
Transparent Pricing
Published Rates for Machine Learning Engineers
ML engineering sits 20–30% above generalist rates everywhere talent is priced honestly. Here's our full seniority ladder against what the alternatives actually charge.
| Role / Seniority | Aizecs (India) | Toptal / Turing | Upwork Freelancers | US In-House |
|---|---|---|---|---|
| Mid-Level ML Engineer (3–5 yrs) | $28–$50/hr | $90–$130/hr | $40–$70/hr | $150–$190/hr |
| Senior ML Engineer (5–8 yrs) | $45–$80/hr | $110–$160/hr | $50–$90/hr | $180–$230/hr |
| Staff ML Engineer / Lead (8+ yrs) | $55–$85/hr | $130–$180+/hr | $60–$100/hr | $200–$250/hr |
| Data Engineer (ML pipelines) | $38–$65/hr | $90–$150/hr | $30–$60/hr | $130–$180/hr |
Dedicated full-time engagement rates, 2025–26 benchmarks. Upwork figures are freelance asking rates with vetting left entirely to you. US in-house includes salary, benefits, and overhead — the fully loaded number.
How It Works
The Route to a Working ML Engineer, Compressed
Recruiting ML talent in the US takes a median of four to six months. Our version of the same funnel takes about two weeks.
Scope the ML problem together
Thirty minutes on your use case — recommendations, forecasting, fine-tuning, MLOps debt — plus data maturity and stack. If your data isn't ready for ML yet, we'll tell you that instead of placing someone.
Get named profiles in 48 hours
Two to four engineers with directly relevant shipped systems, identified by name with verifiable histories. Run your own technical loop — modeling depth, deployment scars, data intuition.
Start with a paid trial week
Your pick joins the sprint and works real problems for a week at standard rate. If it's not landing, you stop there — no further cost, and we source a replacement free.
Extend monthly on your terms
Post-trial, the engagement is month-to-month. Add an engineer as the ML roadmap widens or step down with 30 days' notice once models are stable.
Roles We Staff
Where Our ML Engineers Specialize
Whether you searched 'hire ML engineer' or need a deep learning specialist, these six specializations cover the machine learning work product companies actually commission.
Recommender System Engineers
Collaborative filtering through two-tower and embedding-based retrieval — personalization that measurably moves conversion and retention, with A/B rigor built in.
Forecasting & Time-Series Engineers
Demand planning, churn prediction, and anomaly detection using gradient boosting and deep sequence models — validated against baselines before anyone celebrates.
Deep Learning Engineers
PyTorch specialists training and optimizing neural networks for vision, audio, and multimodal tasks — including distillation and quantization for affordable serving.
LLM Fine-Tuning Engineers
Dataset curation, LoRA and full fine-tunes on open-weight models, and eval-driven comparison against Claude/GPT API baselines so you only fine-tune when it wins.
MLOps Engineers
Feature stores, experiment tracking, model registries, CI/CD for models, and drift monitoring — the infrastructure that turns one-off models into a repeatable capability.
ML Data Engineers
The pipelines beneath the models: dbt, Airflow, and Spark workflows producing training data your models can trust, with lineage and quality checks throughout.
Why Aizecs
Six Reasons Teams Trust Aizecs With ML Hiring
The ML hiring market is full of strong resumes and weak deployment records. Our whole model is designed to filter for the second thing and de-risk the first.
Deployment-tested vetting
Candidates must walk through a model they took to production — the pipeline, the monitoring, the retraining story. Fewer than 1 in 20 make it.
No bait-and-switch
Named talent only. The engineer whose model retraining strategy you dissected in the interview is the one who shows up Monday.
Risk capped at one week
The paid trial week means a wrong match costs you seven days of rate — and we replace the engineer at zero additional cost.
Live collaboration hours
Guaranteed 4+ hours of overlap with US Eastern and near-full EU/UK coverage, so experiment reviews and modeling discussions happen in real time.
Modern ML toolchain fluency
PyTorch, scikit-learn, XGBoost, MLflow, and cloud ML platforms — plus AI-assisted development workflows that speed up the unglamorous 80% of ML work.
Your models, your IP
Full IP assignment and NDA under a US-enforceable MSA. Training data, model weights, and code all live in your infrastructure, owned by you.
Engagement Models
Pick the Engagement That Fits Your ML Maturity
First model, growing portfolio, or an ML platform overhaul — the structure flexes, and everything stays month-to-month.
Solo ML engineer
One senior engineer embedded with your data or product team, owning a model from prototype through deployment and monitoring.
Best for: A first ML use case or one high-value model
ML delivery pod
An ML engineer, an MLOps engineer, and a data engineer operating as one unit — covering the full path from raw data to monitored production model.
Best for: Teams shipping several models without ML infrastructure
Long-term ML capacity
Standing engineers who maintain and extend your model portfolio — retraining cycles, new use cases, and platform improvements as ongoing work.
Best for: Products where ML drives core metrics
Compare Your Options
The ML Hiring Landscape, Honestly Priced
Four realistic ways to add machine learning engineers — with the costs and failure modes each one carries.
| Aizecs | Toptal / Turing | Upwork | US In-House | |
|---|---|---|---|---|
| Senior ML engineer rate | $45–$80/hr | $110–$160/hr | $50–$90/hr | $180–$230/hr loaded |
| Deployment record verified | Yes, system walkthroughs | Algorithmic screens | No | Your interviews |
| Time until modeling starts | ~2 weeks | 2–4 weeks | Days, unscreened | 4–6 months |
| If the hire fails | Trial week only, free swap | Case-by-case refunds | You absorb it | Months of cost + backfill |
| Term structure | Month-to-month, 30-day exit | Monthly + platform fees | Per project | Permanent + benefits |
FAQ
Common Questions
Where do companies hire machine learning engineers with production experience?
The realistic options are premium marketplaces ($110–$160/hr for seniors), freelance platforms (cheap but unvetted), US recruiting (4–6 months), or a specialist agency like Aizecs. We deliver named, deployment-tested ML engineers at $45–$80/hr senior rates with profiles in 48 hours and a paid trial week before commitment.
How do you screen when you hire machine learning engineers into your network?
Every candidate presents a model they personally deployed — data pipeline, serving architecture, monitoring, and what broke after launch. We probe retraining strategy and metric honesty, not just algorithm trivia. Acceptance runs under 1 in 20, and median experience is 5+ years.
Do your engineers cover deep learning as well as classical ML?
Both, deliberately. Deep learning engineers handle PyTorch training, vision, and sequence models; classical ML specialists cover gradient boosting, forecasting, and recommenders — which still win on tabular business data most of the time. We match the specialization to your problem, not the hype cycle.
Should we fine-tune a model or just use LLM APIs?
Our default advice: baseline with Claude or GPT APIs first, because it's faster and often good enough. Fine-tuning earns its cost when you have domain-specific data, strict latency or unit-economics targets, or self-hosting requirements. Our fine-tuning engineers run that comparison with evals before recommending either path.
What ML stack do your engineers typically work in?
PyTorch, scikit-learn, and XGBoost for modeling; MLflow and Weights & Biases for experiment tracking; Airflow and dbt for pipelines; deployment on AWS SageMaker, GCP Vertex, or containerized serving. They adapt to your existing stack rather than importing a preferred one.
How does pricing work for ML engineers?
Mid-level ML engineers are $28–$50/hr, senior $45–$80/hr, staff/lead $55–$85/hr — reflecting the standard 20–30% specialist premium. Billing is hourly for dedicated full-time engineers, month-to-month after the trial week, with no placement fees or annual minimums.
Who owns the trained models and training data?
You do — models, weights, datasets, feature pipelines, and code are all assigned to you under a US-enforceable MSA with NDA. Engineers train and deploy within your cloud accounts and repositories, so no artifact ever exists outside your control.
Stop Interviewing Resumes. Trial a Real ML Engineer.
One call to scope the problem, named ML profiles within 48 hours, and your own interviews before anyone starts.
Then a paid trial week on real work — the cheapest ML hiring decision you'll ever validate.
30 minutes, no strings. If your data isn't ready for machine learning yet, we'll tell you what to fix first — free.