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Machine Learning Engineer Jobs in Norwalk, CT (NOW HIRING)

Virtu Financial

Machine Learning Engineer

New York, NY · On-site

$200K - $300K/yr

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 ...

Root Access Inc

Machine Learning Engineer

New York, NY · On-site

Our team is a passionate mix of engineers across electrical, firmware, software, and machine learning. Core Responsibilities * Architect Physics Foundation Models: Design and train deep learning ...

PROPRIUS

Senior Machine Learning Engineer

New York, NY · On-site

$114K - $157K/yr

Sr. Machine Learning Engineer Location: New York, NY Sponsorship: Yes Relocation: Yes Industry: Machine Learning A leading provider of AI is looking for a Sr. ML Engineer. Our client is an industry ...

Capital One

Sr. Lead Machine Learning Engineer

New York, NY · On-site +1

$112K - $147K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale.

NYULMC

Machine Learning Engineer

New York, NY · On-site

As a ML Engineer, you will support the implementation of diverse Generative AI and Machine Learning initiatives across the health system. You will be responsible for driving specific projects forward ...

Autodesk

Senior Machine Learning Engineer

New York, NY · On-site +1

$114K - $157K/yr

Position Overview As a Senior Machine Learning Engineer, you will play a key role in designing, developing, and evolving machine learning systems that support conversational AI, search, multi-agent ...

Teleskope

Machine Learning Engineer

New York, NY · On-site

$160K - $210K/yr

About the role We are seeking a Machine Learning Engineer to strengthen our element classification system - working closely with data scientists and data annotators to ship and improve entity ...

TransRe

Machine Learning Engineer

New York, NY · On-site

$145K - $170K/yr

Constructing machine learning models including data collection, normalization, and standardization, data pipeline construction, model selection and hyperparameter tuning, working ml systems that can ...

TransRe

Machine Learning Engineer

New York, NY · Hybrid

$145K - $170K/yr

Constructing machine learning models including data collection, normalization, and standardization, data pipeline construction, model selection and hyperparameter tuning, working ml systems that can ...

RunSybil

Machine Learning Engineer

New York, NY · On-site

About this Role We are seeking talented engineers intent on changing the security industry. If you ... Understanding of both modern and classic machine learning techniques * Equally comfortable with ...

Attentive

Senior Machine Learning Engineer

New York, NY · On-site

$244K - $320K/yr

About the Role Our Machine Learning Engineering team powers personalized experiences for hundreds of millions of customers across thousands of brands. As a Senior Machine Learning Engineer, you will ...

WireScreen

Machine Learning Engineer

New York, NY · On-site

$150K - $195K/yr

As a Machine Learning Engineer at WireScreen, you will be working across our data systems to unlock the source of truth behind one of the world's economic powerhouses: China. This role is critical to ...

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Norwalk, CT

Machine Learning Engineer information

See Norwalk, CT salary details

$31.7K

$129.5K

$194.6K

How much do machine learning engineer jobs pay per year?

As of Jul 15, 2026, the average yearly pay for machine learning engineer in Norwalk, CT is $129,513.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,100.00 and $155,900.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What cities near Norwalk, CT are hiring for Machine Learning Engineer jobs? Cities near Norwalk, CT with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Norwalk, CT as of July 2026, with employment types broken down into 89% Full Time, 8% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $129,513 per year, or $62.3 per hour.
Machine Learning Engineer

Machine Learning Engineer

Virtu Financial

New York, NY • On-site

$200K - $300K/yr

Full-time

Re-posted 5 days ago

Job description

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 researchers. This is a unique opportunity to work at the intersection of machine learning and systematic trading - building tools that directly determine how fast our researchers can move, and how effectively our GPU cluster translates into research output.
In this role, you will be responsible for the development of our ML research platform: the systems that manage data and compute, track experiments, and enable researchers to go from idea to result as efficiently as possible. You will work closely with quants and engineers alike and will play a central role in shaping how ML is done at the firm as we scale our capabilities. We mostly use Python, C++ and Java with a variety of open-source tools along with proprietary solutions.
THE ROLE
  • Design and build experiment tracking, job orchestration, and reproducibility infrastructure so researchers can iterate quickly, compare runs reliably, and recover from failures without losing work
  • Create tools for all stages of the simulation lifecycle including historical back-tests and production monitoring. Add new features to our simulators
  • Own visibility into GPU cluster utilization - track allocation, surface bottlenecks, and ensure our compute investment is being used effectively
  • Diagnose and resolve performance issues across training pipelines: data loading throughput, storage I/O, GPU utilization, and inter-node communication in distributed training runs
  • Build and maintain data pipelines that move financial data from storage into training workflows efficiently, with strong guarantees on correctness and versioning
  • Develop feature storage and retrieval patterns that support fast, reproducible access to training data at scale
  • Work directly with researchers to understand friction in their workflows, and build solutions that reduce it - from tooling improvements to infrastructure changes
  • Collaborate with existing infrastructure engineers on capacity planning, cloud/on-prem tradeoffs, and tooling decisions - this is a collaborative environment, not a siloed one
  • Stay current with developments in ML infrastructure tooling and bring relevant ideas and tools into our stack where they create genuine value

THE CANDIDATE
  • 5+ years of experience in ML engineering, research infrastructure, or HPC environments
  • Strong Python engineering skills - you write clean, maintainable, well-tested code that other engineers want to build on. Exposure to C++ in a performance-sensitive context is a plus
  • Experience building or operating distributed training infrastructure, with working knowledge of how collective communication libraries (NCCL, Horovod, or similar) behave at scale
  • Practical experience with experiment tracking systems and strong opinions about what good research infrastructure looks like
  • Comfort working across the Linux systems stack - storage, networking, job scheduling - enough to follow a problem wherever it leads
  • Excellent communication skills and the ability to work closely with researchers and engineers across disciplines
  • Intellectually curious and self-driven - you proactively identify problems worth solving, not just problems you've been asked to solve

DESIRED, BUT NOT REQUIRED
  • Experience with on-prem compute environments and job orchestration tools such as Slurm
  • Familiarity with GPU profiling tools (NSight Systems, PyTorch Profiler) and hands-on experience optimizing GPU memory or compute utilization
  • Experience with columnar data formats and high-performance data processing tools such as Parquet, Arrow, and Polars
  • Familiarity with workflow orchestration tools (Prefect, Dagster, or similar)
  • Prior experience in environments with high-stakes, time-series data at scale. Open to Quantitative Finance, Algorithmic Trading, and Other
  • Experience contributing to or extending open-source ML frameworks or infrastructure tooling

Salary Range: $200,000 - $300,000 (salary range is exclusive of bonuses, benefits or other categories of compensation)
Virtu Financial is an equal opportunity employer, committed to a diverse and inclusive workplace, welcoming you for who you are and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

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