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Overnight Machine Learning Quant Jobs in Boston, MA

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Overnight Machine Learning Quant information

See Boston, MA salary details

$57K

$129.5K

$213.5K

How much do overnight machine learning quant jobs pay per year?

As of Sep 6, 2026, the average yearly pay for overnight machine learning quant in Boston, MA is $129,461.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,300.00 and $165,700.00 per year, depending on experience, location, and employer.

What is the difference between Overnight Machine Learning Quant vs Quantitative Researcher?

AspectOvernight Machine Learning QuantQuantitative Researcher
CredentialsAdvanced degrees in CS, Math, or Stats; programming skillsSimilar; advanced degrees often required
Work EnvironmentFinancial firms, hedge funds, trading desks; fast-paced, data-drivenFinancial institutions, research labs; analytical, research-focused
Industry UsageHigh-frequency trading, algorithmic strategiesMarket analysis, model development
Work HoursOvernight shifts aligned with trading hoursStandard business hours, flexible in some cases

While both roles involve quantitative analysis and programming, Overnight Machine Learning Quants focus on developing models for overnight trading strategies, often working overnight shifts. Quantitative Researchers typically conduct broader market research and model development during regular hours. The roles overlap in skills but differ mainly in work hours and specific application areas.

What are the most commonly searched types of Machine Learning Quant jobs in Boston, MA?

The most popular types of Machine Learning Quant jobs in Boston, MA are:

What cities near Boston, MA are hiring for Overnight Machine Learning Quant jobs?

Cities near Boston, MA with the most Overnight Machine Learning Quant job openings:

Overnight Machine Learning Quant jobs near you

AI / Machine Learning Engineer with Security Clearance

John Galt Staffing

Lexington, MA • On-site

Other

Posted 10 days ago

Job description

The Artificial Intelligence Technology and Systems Group at MIT Lincoln Laboratory has over 50 years of experience developing revolutionary technologies for critical national missions. Group 52 AI Technology & Systems specializes in machine learning (ML) algorithms, technologies, and systems that extract and analyze information from multimedia data—including speech, text, images, and video. In recent years, we have significantly expanded our mission to include developing impactful ML solutions for cybersecurity in collaboration with the Nation’s top cyber organizations. As a recognized leader in both basic and applied AI/ML, our group is shaping emerging AI fields and driving the Laboratory’s efforts in AI Assurance across the Department of Defense and the Intelligence Community. We also integrate expertise in multimedia, cyber, and AI assurance to develop cutting-edge technologies for Operations in the Information Environment (OIE). A hallmark of our work is a focus on operational relevance: we design and evaluate AI/ML systems using realistic datasets and metrics, and we partner directly with intelligence analysts and cyber operators to ensure rapid transition of our technologies into real-world, mission-critical systems. Required Skills:
Knowledge of artificial intelligence ideally with applications on multimedia, cyber security or adversarial machine learning / AI security experience.
Applied AI/ML knowledge – solid graduate-level coursework or equivalent work experience applying machine-learning theory, deep learning, NLP, computer-vision, graph analytics, or adversarial/AI-assurance techniques.
Proficient Python programming – comfortable writing clean, modular, and testable code; expert-level use of deep-learning frameworks (PyTorch, TensorFlow, etc.), data-science stacks (NumPy, pandas, SciPy, etc.), and hands-on experience with agentic/LLM-oriented toolkits (e.g., MCP).
Operating in a rapid research-to-prototype pipeline – demonstrated ability to take a cutting-edge research concept, design an experimental plan, implement a functional prototype, and iterate based on quantitative evaluation. Experience with benchmark datasets and realistic metrics (e.g. latency, accuracy, robustness) is a plus.
Software-engineering best practices – strong Git workflow (branching, pull-requests, code reviews), continuous-integration testing, environment reproducibility (e.g., conda, virtualenv). Ability to document code, write reproducible experiment notebooks, and maintain versioned releases. Desired Skills:
Ability to read, evaluate, and implement state-of-the-art AI research papers.
Problem-solving & analytical mindset – can decompose novel, ill-defined problems, propose multiple solution paths, and select the most promising approach based on empirical evidence.
Detail-oriented, able to multi-task, with the ability to work autonomously with minimal supervision.
Effective written and oral communication skills in technical environments, technical reports, internal wiki pages; delivers clear demo presentations and briefings to both technical audience and senior mission stakeholders.

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