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Research Machine Learning Federated Learning Jobs in New York

Point72

Machine Learning Engineer

New York, NY

The core of our effort is rigorous research into a wide range of market anomalies, fueled by our ... Role/Responsibilities: We are seeking a Machine Learning Engineer to join the High Frequency ...

Jane Street

Machine Learning Researcher

New York, NY · On-site

On our Machine Learning team, you'll build the deep learning models that power our trading ... Intellectual curiosity and excitement about state-of-the-art research across many ML problem ...

Point72

Machine Learning Engineer

New York, NY · On-site

The core of our effort is rigorous research into a wide range of market anomalies, fueled by our ... Role/Responsibilities: We are seeking a Machine Learning Engineer to join the High Frequency ...

Jane Street

Machine Learning Researcher

New York, NY · On-site

On our Machine Learning team, you'll build the deep learning models that power our trading ... Intellectual curiosity and excitement about state-of-the-art research across many ML problem ...

ExaCare Inc

Machine Learning Engineer

Manhattan, NY · On-site

$100 - $130/hr

Machine Learning Engineer ExaCare Inc - New York, New York, United States About this position ... You will partner closely with research-oriented ML teammates and help turn their work into scalable ...

Hang

Machine Learning Engineer

New York, NY · On-site +1

Research and Innovation: Stay up-to-date with the latest developments in machine learning and AI, and explore new techniques and technologies that could benefit Hang. * Technical Leadership: Provide ...

Goodfire

Machine Learning Engineer

Manhattan, NY · On-site

Goodfire is a research company focused on building safe and powerful AI systems through interpretability. They are seeking Machine Learning Engineers to develop their platform for training ...

exacare ai

Machine Learning Engineer

Manhattan, NY · On-site

They are seeking a Machine Learning Engineer focused on MLOps to operationalize and scale their machine learning systems, working closely with research-oriented teammates to create reliable ...

Goodfire

Machine Learning Engineer

Manhattan, NY · On-site

Goodfire is a research company focused on understanding and designing AI systems. They are seeking Machine Learning Engineers to build a platform for training, evaluating, and deploying interpretable ...

ExaCare AI

Machine Learning Engineer

Manhattan, NY · On-site

$110 - $150/hr

Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems. * Rapid Prototyping & Iteration: Build and manage efficient pipelines ...

SysMind Tech

Machine Learning

Manhattan, NY · On-site

$85/hr

Title - Machine Learning ( F2F interview is required) Location - New York, NY ( Hybrid 2-3 days onsite) Rate - $85/hr Analyze large and complex datasets to derive actionable insights and inform ...

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All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in New York

Research Machine Learning Federated Learning information

What are the key skills and qualifications needed to thrive as a researcher in machine learning federated learning?

To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.

What are some common challenges faced when implementing federated learning in a research environment?

One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.

What is a researcher in machine learning federated learning?

A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.

What is the difference between Research Machine Learning Federated Learning vs Data Scientist?

AspectResearch Machine Learning Federated LearningData Scientist
CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, academic institutions, tech companies focusing on privacy-preserving MLBusiness environments, analytics teams, data-driven departments
Industry UsageDeveloping federated algorithms, privacy-preserving ML modelsData analysis, modeling, reporting, and insights generation

Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.

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

Retrosynthesis Researcher, Machine Learning

Schrodinger

New York, NY

$120K - $145K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 10 days ago

Job description

Schrodinger seeks a Retrosynthesis Researcher in Machine Learning (ML) to join us in our mission to transform the discovery of therapeutics and materials.

Schrodinger has pioneered a physics-based software platform that enables discovery of high-quality, novel molecules for drug development and materials applications more rapidly and at lower cost compared to traditional methods. The software platform is used by biopharmaceutical and industrial companies, academic institutions, and government laboratories around the world. Our multidisciplinary drug discovery team also leverages the software platform to advance collaborative programs and its own pipeline of novel therapeutics to address unmet medical needs.

As a member of our Machine Learning team, you'll work at the forefront of computational chemistry and AI, contributing to high-impact research with real-world applications in small molecule drug discovery and materials science.
 
Who will love this job:
  • An ML expert who has applied AI tools to chemical reaction prediction or retrosynthesis (e.g., reaction templates, template-free approaches) and understands organic synthesis and reaction mechanisms
  • An experienced user of cheminformatics tools (e.g., RDKit, Open Babel)
  • A proficient Python programmer who's familiar with ML tools like Pytorch, Tensorflow, and JAX
  • An excellent problem-solver who's comfortable working collaboratively in a multidisciplinary research environment

What you'll do:

  • Develop and implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic pathway prediction
  • Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions, and identify novel synthetic routes
  • Curate and manage reaction datasets from literature, patents, and proprietary sources to train and validate predictive models
  • Integrate retrosynthesis tools with cheminformatics platforms and molecular modeling software
  • Collaborate with synthetic chemists to experimentally validate predicted retrosynthetic routes and optimize laboratory workflows
  • Contribute to scholarly publications in high-impact journals and represent the research group in conferences and workshops

What you should have:

  • PhD in Chemistry, Computational Chemistry, Cheminformatics, or a related field
  • A solid publication record that demonstrates expertise in retrosynthesis algorithms and computational chemistry

We'd prefer to hire someone who has:

  • Familiarity with chemical reaction databases (e.g., Reaxys, USPTO, Pistachio)
  • Knowledge of computer-aided synthesis planning (CASP) tools and retrosynthetic analysis software (e.g., AiZynthFinder, ASKCOS, IBM RXN)
  • A background in graph-based learning, attention mechanisms, and transformer architectures applied to chemical data
  • Familiarity with reaction condition prediction and reaction yield optimization.
  • Experience with Schrodinger Suite and LiveDesign
  • Experience with de novo design and generative machine learning methods
  • Experience with cloud computing and/or high-performance computing (HPC) resources
  • Exposure to quantum chemistry (DFT) is a plus
 
Pay and perks:
Schrodinger understands it's people that make a company great. Because of this, we're prepared to offer a competitive salary, equity-based compensation, and a wide range of benefits that include healthcare (with dental and vision), a 401k, pre-tax commuter benefits, a flexible work schedule, and a parental leave program. We have regular catered meals in the office, a company culture that is relaxed but engaged, and over a month of paid vacation time.  Our Office Management team also plans a myriad of fun company-wide events. New York is home to our largest office, but we have teams all over the world. Schrodinger is honored to have been included in Crain's New York Best Places to Work, BuiltIn's NYC Best Place to Work, and Newsweek's list of America's 100 Most Loved Workplaces. 
 
Estimated base salary range: $120,000 - $145,000. Actual compensation package is dependent on a number of factors, including, for example, experience, education, degrees held, market data, and business needs. If you have any questions regarding the compensation for this role, do not hesitate to reach out to a member of our Strategic Growth team.
 
Sound exciting? Apply today and join us!
 
As an equal opportunity employer, Schrodinger hires outstanding individuals into every position in the company. People who work with us have a high degree of engagement, a commitment to working effectively in teams, and a passion for the company's mission. We place the highest value on creating a safe environment where our employees can grow and contribute, and refuse to discriminate on the basis of race, color, religious belief, sex, age, disability, national origin, alienage or citizenship status, marital status, partnership status, caregiver status, sexual and reproductive health decisions, gender identity or expression, sexual orientation, or any other protected characteristic. To us, "diversity" isn't just a buzzword, but an important element of our core principles and key business practices. We believe that diverse companies innovate better and think more creatively than homogenous ones because they take into account a wide range of viewpoints. For us, greater diversity doesn't mean better headlines or public images - it means increased adaptability and profitability.

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