$160k - $250k
Role Description Senior Machine Learning Engineer – Recommendation Systems Remote – United States Salary: $160,000 – $250,000 + Equity Please note: Candidates must be fluent in both Mandarin and English. Revive Recruitme…
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.
11,560
Open roles today
$43–$111/hr
Typical pay range
$148k
Median, full-time
9
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01
Open machine learning engineer roles
12 shown of 11,560 · sorted by freshness
$266k - $372.4k
the-art models at scale, driving architectural decisions, and ensuring robust monitoring and smooth product integration across the engineering organization. ~Collaborate across disciplines and with ML, Product, Infrastru…
$165k - $185k
Role Description Allocate is looking to add an AI / Extraction Engineer to the team! There's a lot for us to build, and we need a... ...Essential Responsibilities and Duties ~Build, train, and improve machine-learning mo…
$170k - $190k
Role Description As the Senior Machine Learning Engineer, you are an independent owner of critical Machine Learning (ML) subsystems in production. You take ambiguous problems, design practical solutions, and ship systems…
$150k - $170k
Role Description As the Staff Machine Learning Engineer, you own the execution layer of intelligence. You translate research direction into reliable, scalable, production-grade Machine Learning (ML) systems. This role si…
$170k - $200k
Role Description As a Principal Machine Learning Engineer, you are a deep technical authority responsible for designing and evolving the most critical ML systems in the company. This is a hands-on, high-impact role focus…
Role Description As a Machine Learning Engineering Fellow at Indeed, your technical leadership will play an integral role in accelerating improvements to our core search and recommendations platforms. You will have the o…
$165k - $190k
Intelligence systems. This is a hands-on role perfect for an ambitious engineer who is passionate about taking raw data through the entire... ...workflows Qualifications ~5+ years of experience in machine learning, with…
Role Description We are looking for a Senior Machine Learning Engineer to own search and recommendation systems for Sekai’s consumer product. This role sits at the center of content discovery, user engagement, and conten…
$130k - $200k
Role Description We are seeking a Machine Learning Engineer (3–5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and intelligence ap…
$500 per month
Role Description The Machine Forward Deployed Learning Engineer position requires a mix of software development, LLM Ops, and SecDevOps practices, resulting in an exciting, fast-paced engineering role. This role requires…
Role Description We are looking for a Machine Learning Engineer to join our team and help build next-generation AI-powered workplace assistants. In this role, you will work with modern Generative AI technologies, agentic…
02
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.
03
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
01
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.
05
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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