$69k - $109k
Salary: $69,000 - 109,000 per year Requirements: High school diploma or equivalent is required. An associates or bachelors degree in Computer Science, Computer Information Systems, or a related discipline is preferred. A…
Engineering & IT · Albuquerque, NM
Machine Learning Engineer jobs in Albuquerque, NM
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
63
Open roles today
$40–$103/hr
Typical pay range
$137k
Median, full-time
5
Fresh in this list
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01
Open machine learning engineer roles
11 shown of 63 · sorted by freshness
$180k - $225k
on your skills and experience — talk with your recruiter to learn more. Base pay range $180,000.00/yr - $225,000.00/yr... ...Compensation Job Details We are seeking an experienced Machine Learning Engineer to design, dev…
$70k - $105k
solve challenges and celebrate success! Job Summary JCS Solutions LLC is seeking a highly skilled Senior Data/Visualization Engineer specializing in data visualization to join our team. The primary focus of this role is…
$27 - $32 per hour
MedTech is seeking Mechanical Field Service Engineers to support a nationwide medical device... ...hydraulic assemblies on dialysis machines. Complete required rinse, flush, testing... ...outcomes and population health w…
Qualifications: College or University education in Electrical, Electronic, or Computer disciplines Passion for technology and learning new software and hardware products Hands-on experience with IP Networking, server har…
build with our clients, and the positive impact we make in the community. Our team of intellectually curious and highly motivated engineers, scientists and business professionals engages with our clients to solve complex…
$53k - $106k
provide a work home for those ready to lead in the field. As a Field Engineer, you deliver durable solutions by applying sound technical... ...Dependent Care Spending Account Tuition Assistance To learn more about our be…
$89 per hour
ongoing initiatives to reduce energy use, waste generation, and greenhouse gas emissions. As a Facilities Chemical/Gas Systems Engineer, you will play a key role in ensuring the reliability, efficiency, and safety of Int…
of the boldest and most ambitious space missions PANEL PROGRAM ENGINEER II/SENIOR Based out of Rocket Lab's site in Albuquerque, New... ...of State and/or the U.S. Department of Commerce, as applicable. Learn more about…
end of complex technical projects in a role that blends sales, engineering insight, and customer strategy. This opportunity is ideal for... ...or incorrect proposal or scope of work. ~Capture lessons learned from complet…
Verus Research is searching for a Machine Learning Engineer to perform research & development, conception, and implementation of advanced concepts in artificial intelligence, machine learning, autonomous systems, and mob…
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What machine learning engineers earn in Albuquerque
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $40–$56 | $84k–$116k |
| Mid level | $56–$76 | $116k–$158k |
| Senior | $74–$103 | $153k–$214k |
Adjusted for the Albuquerque market from national ranges.
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What employers ask for
The skills these listings keep naming
Python and software engineeringPyTorch or TensorFlowML fundamentals and evaluationModel serving and APIsMLOps (tracking, registries, CI)Docker and KubernetesData pipelines and feature storesLLM fine-tuning and RAG (a plus)Monitoring and drift detection
04
Interview questions worth rehearsing
With the thing the interviewer is actually listening for
Walk me through taking a model from prototype to production.
Cover data pipelines, training reproducibility, serving, and monitoring. Emphasize that the model is a small part of the system — that framing is the job.
How do you monitor a model in production?
Discuss input drift, prediction distributions, delayed labels, and business metrics — plus what triggers retraining. Mention that silent degradation is the default failure mode.
How would you reduce inference latency or cost for a large model?
Options include distillation, quantization, batching, caching, and smaller models. Frame it as measuring first, then choosing the cheapest acceptable quality tradeoff.
Tell me about a time a model failed in production. What happened?
A real story about skew, drift, or a data bug — with detection and prevention — is far more convincing than claiming smooth deployments.
How do you evaluate a model beyond accuracy?
Talk about the metric matching the business cost of errors, slicing by segment, and offline-online gaps. Naming a case where accuracy misled is a strong touch.
When would you fine-tune an LLM versus use retrieval or prompting?
Start cheap: prompting, then RAG for knowledge, fine-tuning for behavior and format. Cost and maintenance burden should drive the answer.
How do you make training reproducible?
Version code, data, and config; track experiments; pin environments. This is engineering discipline applied to ML, which is exactly the role.
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Resume tips that move the needle
For machine learning engineers specifically — generic advice costs you here
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Center bullets on production systems: models served, request volume, latency, and the business metric they moved.
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Show software engineering credentials explicitly — testing, CI, code review — since MLE hiring filters hardest on engineering rigor.
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Name the MLOps tooling you have run (MLflow, SageMaker, Kubeflow, vector databases) as these are common screens.
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Include LLM work with specifics — fine-tuning, RAG, evaluation — if you have it; vague 'GenAI experience' claims read poorly.
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Distinguish your role on shared projects: built the serving layer, owned the pipeline, or trained the model.
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Where this role goes
Typical progression
01
ML Engineer
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Senior ML Engineer
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Staff ML Engineer
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ML Platform Lead
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