$250.8k - $286.2k
years of experience in constructing, enhancing, and optimizing machine learning systems ~ At least 2 years of experience leading teams in... ...Masters or doctoral degree in computer science, electrical engineering, math…
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.
867
Open roles today
$57–$146/hr
Typical pay range
$195k
Median, full-time
6
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01
Open machine learning engineer roles
12 shown of 867 · sorted by freshness
$245.9k - $280.6k
needs Collaborate with cross-functional teams, including engineers, research scientists, technical program managers, and product... ...of your solutions Technologies: AI Java Machine Learning Mobile Python React React Na…
$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…
$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…
$124k - $186k
for our audiences and our employees – and aim to leave a positive mark on culture. Overview As a member of the Applied Machine Learning Group, you’ll help build a world-class streaming experience within the team. Your mi…
TL;DR – We’re building humanity’s defense layer for the AI age and are looking for an exceptional machine learning engineer to build the AI decision system that turns raw signals into trusted operational decisions across…
of sustained growth and superior returns, as we deliver rare value and impact across our businesses. The Role As a Senior ML Engineer for AWS and Real-Time Inference, you'll own the fast path: ingesting live trading data…
$175k - $275k
Overview A leading global alternative investment firm is seeking a Machine Learning Engineer, Knowledge Graph Intelligence to join its New York office. The firm is looking for bright, motivated, and collaborative people…
$148.7k - $199.4k
Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data... ...responsible for building robust data pipelines and advanced machine learning pla…
our unparalleled access to a wide range of publicly available data sources. Role/Responsibilities: We are seeking a Machine Learning Engineer to join the High Frequency Trading Technology team. This role will apply the l…
of the great threats AI presents: mass-manufactured social engineering. Countless scams, deepfakes, and other social engineering attacks... ...threats. What We're Looking For We’re looking for a machine learning engineer…
important work of your career.. About the team Stripe's Applied ML organization is excited to offer new grad PhD machine learning engineering positions for 2026. This is an exceptional opportunity to contribute to critic…
02
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.
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
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Senior ML Engineer
03
Staff ML Engineer
04
ML Platform Lead
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