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Deep Learning Scientist Jobs in Massachusetts (NOW HIRING)

Axon

Senior Machine Learning Scientist

Boston, MA · On-site

$99.40K - $135.80K/yr

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI ... Strong proficiency in programming languages such as Python, C/C++, experience with deep learning ...

Axon

Senior Machine Learning Scientist

Boston, MA · On-site

$99.40K - $135.80K/yr

Provide technical leadership to junior scientists, guiding the transition of R&D concepts into ... Strong proficiency in programming languages such as Python, C/C++, experience with deep learning ...

E Ink

Deep Learning Researcher 1900S-2

Billerica, MA · Hybrid

$130K - $160K/yr

Collaborate with scientists to integrate domain knowledge into deep learning architectures and interpret results. * Create tools that make models and predictions transparent and usable. * Promote ...

E Ink

Deep Learning Researcher 1900S-2

Billerica, MA · On-site

$130K - $160K/yr

Collaborate with scientists to integrate domain knowledge into deep learning architectures and interpret results. * Create tools that make models and predictions transparent and usable. * Promote ...

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How much do deep learning scientist jobs pay per year?

As of May 28, 2026, the average yearly pay for deep learning scientist in Massachusetts is $134,046.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,600.00 and $148,500.00 per year, depending on experience, location, and employer.

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

To thrive as a Deep Learning Scientist, you need a solid background in machine learning, statistics, and programming, often supported by an advanced degree in computer science or a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience with cloud computing platforms, and proficiency in Python are typically required. Strong problem-solving skills, creativity, and the ability to communicate complex ideas clearly set outstanding candidates apart. These capabilities are essential for developing innovative AI solutions, interpreting results, and collaborating effectively in multidisciplinary teams.

What are some typical challenges faced when working as a Deep Learning Scientist, and how can they be addressed?

Deep Learning Scientists often encounter challenges such as managing large datasets, tuning complex model architectures, and ensuring reproducibility of experiments. Handling these issues requires strong skills in data preprocessing, familiarity with version control systems, and experience with frameworks like TensorFlow or PyTorch. Collaborating closely with cross-functional teams—including data engineers, software developers, and domain experts—can also help in overcoming technical and project-related obstacles. Continuous learning and staying updated with the latest research is essential to excel in this rapidly evolving field.

What are Deep Learning Scientists?

Deep Learning Scientists are experts who design, develop, and implement advanced machine learning models inspired by the structure and function of the brain, known as artificial neural networks. They work with large datasets to train algorithms that can recognize patterns, make predictions, and solve complex problems in areas such as image recognition, natural language processing, and autonomous systems. Deep Learning Scientists often collaborate with software engineers, data scientists, and domain specialists to deploy models in real-world applications like healthcare, finance, and self-driving cars.

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

AspectDeep Learning ScientistMachine Learning Engineer
Required CredentialsMaster's or PhD in Computer Science, Data Science, or related fields; strong background in deep learning frameworksBachelor's or Master's in Computer Science or related fields; proficiency in machine learning algorithms and software engineering
Work EnvironmentResearch-focused, experimental, often in R&D teamsDevelopment and deployment-focused, working on production systems
Employer & Industry UsageTech companies, research labs, AI startupsTech firms, finance, healthcare, and industries deploying ML models

While both roles involve machine learning, Deep Learning Scientists focus on developing advanced neural network models and research, whereas Machine Learning Engineers implement, optimize, and deploy these models in real-world applications.

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

Scientist, Machine Learning (Principal Scientist - Associate Director)

Superluminal Medicines Inc.

Boston, MA • On-site

Full-time

Posted 7 days ago

Job description

Job Summary:
Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. They are seeking a Machine Learning Scientist to join their integrated discovery team and help advance small molecule drug discovery programs through applied ML.
Responsibilities:
• Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug-like properties
• Serve as the machine learning POC on cross functional projects partnering with medicinal chemists and structural biologists to refine SAR and structure informed modeling efforts
• Synthesize complex ML outputs into clear, actionable design hypotheses that cross-functional scientific stakeholders can use to make high-stakes program decisions
• May be responsible for management and development of internal team members
Qualifications:
Required:
• Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field
• 2+ years applying ML methods in a small molecule drug discovery programs in biotech or pharma environments
• Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions
• Demonstrated success in a cross-functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly influence achievement of program milestones
• Strong practical proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. Demonstrated ability to build and maintain robust, production-quality ML code and data workflows
Preferred:
• Proven experience with protein-ligand co-folding models (e.g., Boltz, OpenFold, AlphaFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines
• Expertise fine-tuning existing models with internally generated structural biology and biology data
• Strong knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context
• Experience mentoring and developing teams
Company:
Superluminal Medicines is a Boston-based generative biology and chemistry company developing a differentiated pipeline and revolutionizing the speed and accuracy of how medicine is created. Founded in 2022, the company is headquartered in Boston, USA, with a team of 11-50 employees. The company is currently Early Stage.

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