AI-powered, software-defined underwater sensing, communication, networking, and analytics technology.If you are a Field Operations Engineer with experience, please read on!What You Will Be Doing:Lead day-to-day field ope…
Engineering & IT · Boston, MA
Machine Learning Engineer jobs in Boston, MA
Machine learning engineers take models from notebook to production — building training pipelines, serving infrastructure, and monitoring so ML systems keep working after launch.
320
Open roles today
$55–$141/hr
Typical pay range
$189k
Median, full-time
6
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01
Open machine learning engineer roles
12 shown of 320 · sorted by freshness
$71 - $80 per hour
DescriptionKforce has a client in need of a Senior Data Quality & Snowflake Migration Engineer in Boston, MA.Responsibilities:* Design and implement data quality and validation processes for large-scale cloud data migrat…
$87.7k - $139.7k
OverviewJLL is seeking an experienced Senior High Voltage Electrical Engineer to lead the design and engineering of critical electrical... ...detail ensuring technical accuracy and code compliance. Continuous learning mi…
$165k - $185k
investment management firm in Boston, is investing heavily in its next-generation cloud data platform and is looking for a Senior Data Engineer to help drive that transformation.This is an opportunity to join a lean, hig…
$80.46k - $142.72k
ANA United StatesWork Type: On-siteDate Posted: 2026-08-21Arcadis is the world's leading company delivering sustainable design, engineering, and consultancy solutions for natural and built assets.We are more than 34,000…
We are looking for a skilled Data Engineer to join a 100% remote contract to hire position. This role focuses on developing and maintaining data warehouse integration processes, working closely with technical teams and b…
$197.3k - $225.1k
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…
We are seeking an experienced Snowflake Data Engineer with strong expertise in Snowflake, Python, SQL, Snowpark, and ELT pipeline development. The ideal candidate will have a strong background in data engineering, data w…
$144k - $192k
Mission Summary: We are looking for a Machine Learning Systems Engineer to join our ML Acceleration team. In this role, you will be responsible for the core systems that enable our researchers to train frontier models at…
$100k - $120k
What you’ll need to succeed as a Machine Learning Engineer at XPO Minimum qualifications: ~ Bachelor's degree in Computer Science, Engineering, or related field, or equivalent related work or military experience ~1 year…
$161k - $246k
Description Job Description Overview: The ASUS Robotics & AI Center is seeking a Senior Machine Learning Engineer to join our global research and development team. This role centers on leading the design and delivery of…
$197.3k - $225.1k
Overview Lead Machine Learning Engineer (Finance Tech - AI Enablement) At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructur…
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What machine learning engineers earn in Boston
Hourly first — that's how the offer arrives
| Experience | Hourly | Annual, full-time |
|---|---|---|
| Entry level | $55–$77 | $115k–$160k |
| Mid level | $77–$105 | $160k–$218k |
| Senior | $101–$141 | $211k–$294k |
Adjusted for the Boston 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.
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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
02
Senior ML Engineer
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Staff ML Engineer
04
ML Platform Lead
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