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Scientific Machine Learning Jobs in Boston, MA (NOW HIRING)

Nanite Inc.

Machine Learning Engineer

Boston, MA · On-site

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

Nanite Inc.

Machine Learning Engineer

Boston, MA

Nanite is a disruptive Machine Learning/AI therapeutics company focused on revolutionizing drug ... Design and implement complex data engineering processes to support innovative data science modeling

C the Signs

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133.10K - $175.50K/yr

Position Summary The Machine Learning Engineer will be responsible for the end-to-end development ... Work closely with data scientists, clinicians, and software engineers to understand requirements ...

C the Signs

Senior Machine Learning Engineer

Boston, MA · Remote

$125.40K - $165.30K/yr

Position Summary The Machine Learning Engineer will be responsible for the end-to-end development ... Work closely with data scientists, clinicians, and software engineers to understand requirements ...

C the Signs

Senior Machine Learning Engineer

Boston, MA · On-site +1

$133.10K - $175.50K/yr

Position Summary The Machine Learning Engineer will be responsible for the end-to-end development ... Work closely with data scientists, clinicians, and software engineers to understand requirements ...

STR

Lead Research Scientist - Machine Learning

Woburn, MA

$174K - $220K/yr

... machine learning, statistics, estimation theory, and information theory algorithms for signals ... scientific field such as applied math, physics, electrical engineering, computer science, or data ...

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$15

$34

$56

How much do scientific machine learning jobs pay per hour?

As of May 29, 2026, the average hourly pay for scientific machine learning in Boston, MA is $34.20, according to ZipRecruiter salary data. Most workers in this role earn between $20.91 and $43.61 per hour, depending on experience, location, and employer.

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

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in Scientific Machine Learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

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

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Scientific Machine Learning jobs in Boston, MA? For Scientific Machine Learning jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Scientific Machine Learning jobs in Boston, MA look for? The top searched job categories for Scientific Machine Learning jobs in Boston, MA are:
What cities near Boston, MA are hiring for Scientific Machine Learning jobs? Cities near Boston, MA with the most Scientific Machine Learning job openings:
Scientific Machine Learning jobs near you

Machine Learning Scientist I/II, Scientific Reasoning

Lila Sciences

Cambridge, MA • On-site

Other

Posted 2 days ago

Job description

Your Impact at LILA

As a Machine Learning Scientist focused on Scientific Reasoning, you will help pioneer the next generation of AI systems capable of reasoning like a scientist. You'll design novel frameworks that push the boundaries of LLM-based reasoning methods - while also implementing scalable frameworks that integrate with Lila's platforms. This role bridges deep theoretical thinking with practical ML engineering, enabling breakthroughs in how scientific hypotheses are generated, tested, deployed and optimized.

What You'll Be Building

  • Design and formalize frameworks for scientific reasoning with LLMs, including structured prompting, reasoning chains, and test-time compute.
  • Explore and implement methods for in-context learning, self-reflection, and adaptive reasoning in scientific discovery workflows.
  • Build scalable model prototypes that can be deployed to solve frontier scientific problems.
  • Collaborate with scientists and engineers to encode domain knowledge into reasoning systems that integrate symbolic and statistical approaches.

What You'll Need to Succeed

  • PhD (preferred) or equivalent research/industry experience in Computer Science, Machine Learning, AI, Engineering, Materials Science or related fields.
  • Strong programming skills in Python with deep expertise in LLM frameworks (PyTorch, HuggingFace Transformers, LangChain, LlamaIndex, and related toolkits).
  • Expertise in LLM reasoning methods: in-context learning, test-time compute, chain-of-thought, or tool-augmented reasoning.
  • Ability to balance theoretical research with practical ML engineering to deliver scalable solutions.

Bonus Points For

  • Research experience in causal reasoning, symbolic AI, or probabilistic programming.
  • Contributions to open-source LLM reasoning frameworks.
  • Familiarity with scientific discovery pipelines in chemistry, biology, or materials science.
  • Experience with multimodal reasoning (e.g., combining text, image, and experimental data).
  • Publications in top ML/AI conferences (NeurIPS, ICML, ICLR, ACL).

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