Hire a Fractional Machine Learning Engineer
Pre-screened Machine Learning Engineers who have taken models from Jupyter to monitored production endpoints, matched to your data stack and use case in about 3 days.
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When to hire a Fractional Machine Learning Engineer
Bring in a Machine Learning Engineer when your models work in a notebook but not in your product, or when nobody on the team owns them after launch.
Models stuck in notebooks
Your data scientists have promising prototypes, but nobody can package, containerize, and serve them behind a reliable API.
Accuracy is drifting in production
Predictions have degraded since launch and you have no monitoring, retraining schedule, or alerting on input data shifts.
Data scientists doing infrastructure
Your analysts and scientists spend their weeks fighting Kubernetes, feature stores, and CI instead of improving models.
An LLM feature with no evaluation plan
Leadership wants a generative AI feature shipped, but you have no retrieval design, eval harness, or guardrails for cost and hallucinations.
GPU and inference costs keep climbing
Training runs and endpoints are overprovisioned, and nobody has profiled batch sizes, quantization, or autoscaling.
Your full-time ML search has stalled
The role has been open for months and the roadmap items that depend on it keep slipping.
The benefits of hiring a Fractional Machine Learning Engineer
A fractional Machine Learning Engineer gets your models trained, deployed, and monitored without waiting months on a full-time search or paying a staff-level salary for work that needs part-time ownership.
Put your models into production on a retainer
Senior ML engineering for feature pipelines, model serving, and retraining jobs, paid as a monthly retainer instead of a staff salary, equity, and benefits.
Start fixing model drift this sprint
Candidates arrive pre-screened, so your engineer can be reviewing your training data and inference logs in days rather than after a 4 to 6 month hiring cycle.
Hire for PyTorch, MLOps, and LLM depth
Match with engineers who have shipped recommendation systems, forecasting models, fraud detection, or RAG pipelines on stacks like SageMaker, Vertex AI, Databricks, and MLflow.
How to hire a Fractional Machine Learning Engineer
No more juggling job boards and sorting through pools of unqualified applicants. Hiring a Fractional Machine Learning Engineer is easy with Go Fractional.
Intro Call
Answer a few quick questions, then book a free 30-minute intro call with our team to review your goals, needs, and team dynamics.
Meet Experts
Our AI finds Fractional Machine Learning Engineer candidates whose past clients and projects match your brief, which you won’t find on LinkedIn. A dedicated executive recruiter verifies each one’s experience, fit, and genuine interest, and you meet the ones who fit in about 3 days.
Start Work
Book time with candidates right from your shortlist, choose your operator, and sign a simple month-to-month agreement. We move as fast as you do.
Engagements that fit how you work
Every Go Fractional engagement is structured for senior leadership without full-time overhead. Start fractional, scale up, or convert to full-time when you’re ready.
Fractional Machine Learning Engineer
- Senior leadership on a part-time schedule
- Monthly retainer, no recruiting fees
- Scale hours up or down as needs change
Contract-to-Hire
Trial a Fractional Machine Learning Engineer on a fractional engagement before converting them to a full-time hire.
- Keep the expert's learnings or convert to full-time
- Try the fit before you commit, no hiring risk
- Faster speed to market, conserving time and resources
Interim Machine Learning Engineer
Full-time coverage for a defined stretch: a leadership gap, a transition, or a critical project.
Fractional Machine Learning Engineer rates average $189/hr across our live benchmarks. Pricing scales with hours and scope.
Machine Learning Engineer Hourly RatesFractional vs full-time vs agency
Three ways to get Machine Learning Engineer leadership, and where each one wins.
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| Factor | Fractional Machine Learning Engineer | Full-time ML hire | AI agency / dev shop |
|---|---|---|---|
| Time to hire | ~3 days to match | 3-6 months | 2-6 weeks |
| Engagement | Monthly retainer | Salary + equity + benefits | Project fee with markup |
| Model ownership | Embedded with your team, documents and hands off | Full long-term ownership | Often leaves with the contract |
| Data access and governance | Works inside your cloud and access controls | Works inside your cloud and access controls | Often needs data exported to their environment |
| Best for | Shipping and stabilizing models, setting MLOps standards | Large ML roadmap with steady headcount budget | One-off prototype or proof of concept |
We’ve helped hundreds of organizations Go Fractional
Twelve years: the average experience across a network of handpicked executives ready to make an impact on day one.
“Hiring a fractional Head of Data Science has been transformative for our company. At just 50% of the cost of a full-time data scientist, we secured a seasoned data leader with over 12 years of experience in top startups. This strategic move has significantly enhanced our data strategy and execution. The expert leadership has instilled confidence in our data capabilities, driving measurable results for our business.”
“It was literally three days after he started with us fractionally, our CEO was calling me and asking if we could get a full-time offer out.”
“It's a can't-lose scenario. You either get the learnings from the subject matter expert, or you get the full-time capability. It provided a ton of flexibility, much faster speed to market, and conservation of time and resources.”
Frequently asked questions
Here are a few questions that folks just like you have had during their search.
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