Sr. Civil Engineer Sr. Civil Engineer For over four decades, Schmidt Associates has built our entire approach on the idea of Servant Leadership. As servant leaders, our design team seeks to deeply understand our clients…
Engineering & IT · Indianapolis, IN
Machine Learning Engineer jobs in Indianapolis, IN
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
152
Open roles today
$40–$103/hr
Typical pay range
$137k
Median, full-time
3
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Open machine learning engineer roles
11 shown of 152 · sorted by freshness
This role will apply principles and techniques of mechanical engineering for the design and development of mechanical equipment and sub... ...mechanical loads, materials of construction, component selection, machine layo…
Our projects span the U.S., offering opportunities to learn, lead, and advance. This travel-based role requires flexibility and relocation. Field Engineers typically work on projects for about two years before moving to…
Food/FDA compliance space and have enjoyed steady growth for the last several years. Responsibilities As a Plumbing Systems Engineer, your key responsibility will be to serve as a vital team member associated with provid…
become part of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction... ...00 firm that had revenue of $16.1 billion in fiscal year 2025. Learn more at aeco…
to achieve Our Purpose: Patient Focus, Integrity, Innovation, Impact and Empathy. Learn more about Life at Olympus: . Job Description The Field Service Engineer (FSE) services and maintains all product lines for Olympus…
intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest... ...edge AI research and production systems at the intersection of machine learning, com…
$18 per hour
Job Title: Shop Maintenance Engineer Job Description The Shop Maintenance Engineer builds, maintains, troubleshoots, and repairs... ...company, the global leader in workforce and business solutions. To learn more, visit:…
enable a better, safer and more interconnected world. Job Description We're looking for an experienced Field Service Engineer to join our team in Indianapolis, United States. In this role, you will deliver expert technic…
Operating Engineer (Building Maintenance & HVAC) Contract-to-Hire Opportunity Schedule: Monday–Friday, 8:00 AM – 4:00 PM We are seeking an experienced Operating Engineer to support the operation, maintenance, inspection,…
the role of Developer-Information Services Senior. This position is full time, remote. Role Responsibilities: The Senior Data Engineer is critical in shaping the strategic data framework of the organization. This role de…
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What machine learning engineers earn in Indianapolis
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 Indianapolis 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
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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.
03
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
02
Senior ML Engineer
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Staff ML Engineer
04
ML Platform Lead
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