$147.6k - $274k
worldwide.The OpportunityWithin AI for Drug Discovery, the Software Engineering team builds and operates software platforms that put... ...impact across drug discovery. We are seeking a very talented Machine Learning Eng…
Engineering & IT · New York, NY
Machine Learning Engineer jobs in New York, NY
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
604
Open roles today
$57–$146/hr
Typical pay range
$195k
Median, full-time
5
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Open machine learning engineer roles
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and ML inference. Turn advances in information retrieval and machine learning into production systems through rigorous evaluation,... ...multiple teams, simplify fragmented systems, mentor senior engineers, and align tec…
$197.3k - $245.6k
Requirements: Bachelors degree or higher in Computer Science, Machine Learning, or a related quantitative field such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering. At least 4 years…
$215.2k - $245.6k
We require a bachelors degree or higher in Computer Science, Machine Learning, or a related quantitative discipline such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering ~ We expect a…
$124k - $186k
both for our audiences and our employees – and aim to leave a positive mark on culture. Overview We are seeking a Machine Learning Engineer to join the PlutoTV pod. You will focus on Channel, Guide, and Schedule personal…
$229.9k - $262.4k
Overview Machine Learning Engineer 5 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery en…
$215.2k - $245.6k
Overview Machine Learning Engineer 4 Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery en…
mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise... ...Remote, Full-time Role Overview We’re hiring a Senior Machine Learning Engineer to arch…
$228.96k - $315.36k
The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging... ...support hundreds of customers. As a Senior Machine Learning Engineer, you…
$228.96k - $315.36k
intelligence products. We identify the best opportunities to use machine learning in Plaid products, prove out those opportunities, and... ...systems. About the Role As a Senior Machine Learning Engineer on Embedded Insi…
$201.3k - $352.3k
It all started when engineer Fred Luddy wrote code that automated a tedious task for... ...experiences, and a culture of continuous learning. This is a zero-to-one incubation. We... ...Computer Science, Artificial Intell…
$200k - $300k
Virtu’s Research Technology team is looking for an experienced Machine Learning Engineer to join a small group of technologists whose primary function is building the infrastructure that powers our quantitative researche…
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What machine learning engineers earn in New York
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $57–$79 | $119k–$165k |
| Mid level | $79–$108 | $165k–$224k |
| Senior | $105–$146 | $218k–$304k |
Adjusted for the New York 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
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.
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
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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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