Project Details: Job Title: Machine Learning Engineer Job ID: 12279 No. of Position: 2 Duration: 12 Months Location: Remote PST time zone Overview: Seeking an experienced AI Engineers...
Engineering & IT · Remote
Remote Machine Learning Engineer jobs
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
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$43–$111/hr
Typical pay range
$148k
Median, full-time
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Open machine learning engineer roles
6 shown of 3,942 · sorted by freshness
$90 per hour
Machine Learning Engineer Expert is a remote review track for evaluating AI outputs across machine learning engineer specialist operations workflows. Reviewers grade workflow correctness, policy adherence, and stakeholde…
$225k - $275k
entertainment. Founded in 2012 and headquartered in Redwood City, CA, Liftoff has a diverse, global presence. As a Staff Machine Learning Engineer at Liftoff, you will: - Develop and maintain machine learning models that…
Machine Learning Engineer About the role Deeter Analytics is a privately held investment research and trading firm managing its own capital across public markets. After years of discretionary success, we think we have so…
investors access information. We combine artificial intelligence, machine learning, and real-time data pipelines to surface insights before they... .... We're seeking a highly motivated AI / Machine Learning Engineer who…
Job Responsibilities: Design and implement deep learning models using TensorFlow, PyTorch, and transformer architectures. Fine-tune pre-trained models for domain-specific tasks involving text, image, or audio datasets. O…
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What machine learning engineers earn remotely
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $43–$60 | $90k–$125k |
| Mid level | $60–$82 | $125k–$170k |
| Senior | $79–$111 | $165k–$230k |
National ranges — remote pay usually tracks the employer's base market.
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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.
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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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