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Machine Learning Assistant Jobs in New York (NOW HIRING)

Crowe LLP

Machine Learning Engineer

New York, NY

$62K - $100K/yr

Write high-quality, production-ready code and assist in code reviews to maintain standards of excellence. * Develop and implement comprehensive testing protocols, including smoke tests and unit tests ...

Crowe LLP

Machine Learning Engineer

Livingston, NJ · On-site

$62K - $100K/yr

Write high-quality, production-ready code and assist in code reviews to maintain standards of excellence. * Develop and implement comprehensive testing protocols, including smoke tests and unit tests ...

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Machine Learning Assistant information

What is a machine learning assistant?

A Machine Learning Assistant is a professional who supports the development, implementation, and maintenance of machine learning models and systems. They assist data scientists and engineers by preparing datasets, conducting preliminary data analysis, running experiments, and helping to optimize algorithms. This role often involves coding, testing models, and ensuring the quality and reliability of machine learning solutions. Machine Learning Assistants play a key role in streamlining workflows and enabling faster progress in AI projects.

What are the key skills and qualifications needed to thrive as a machine learning assistant?

To thrive as a Machine Learning Assistant, a solid background in mathematics, statistics, programming (often Python), and foundational knowledge of machine learning algorithms is essential, typically supported by a relevant degree or coursework. Familiarity with tools like TensorFlow, scikit-learn, Jupyter Notebooks, and version control systems such as Git is commonly required. Strong problem-solving abilities, attention to detail, and the capability to communicate findings effectively are standout soft skills in this role. These skills ensure accurate data analysis, effective model building, and successful collaboration within multidisciplinary teams.

What are some common challenges a machine learning assistant may face when supporting data preparation and model training?

Machine Learning Assistants often encounter challenges such as cleaning large, unstructured datasets, identifying and handling missing or inconsistent data, and ensuring data privacy compliance. They also need to communicate effectively with data scientists and engineers to understand project requirements and adapt to evolving priorities. Staying organized and managing multiple tasks simultaneously—such as data preprocessing, feature engineering, and running model experiments—is crucial for success in this role.

What are the most commonly searched types of Machine Learning jobs in New York?

The most popular types of Machine Learning jobs in New York are:

What are popular job titles related to Machine Learning Assistant jobs in New York?

For Machine Learning Assistant jobs in New York, the most frequently searched job titles are:

What job categories do people searching Machine Learning Assistant jobs in New York look for?

The top searched job categories for Machine Learning Assistant jobs in New York are:

What cities in New York are hiring for Machine Learning Assistant jobs?

Cities in New York with the most Machine Learning Assistant job openings:

Machine Learning Assistant jobs near you
Infographic showing various Machine Learning Assistant job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 2% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Principal Machine Learning Engineer, Foundation Models, AI for Drug Discovery

New York, NY • On-site

Genentech, Inc.
Scientific Research and Development Services • 10K+ employees
Learn more about Genentech, Inc.external

Full-time

Posted 4 days ago

Genentech rating

8.8

Company rating: 8.8 out of 10

Based on 22 frontline employees who took The Breakroom Quiz

12th of 86 rated pharmaceutical

Job description

A healthier future. It's what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That's what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche's Research and Early Development organizations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximizing these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Opportunity:
At Roche's AI for Drug Discovery (AIDD) group (Prescient Design), we are revolutionizing drug discovery with cutting-edge machine learning. We are seeking a Principal Machine Learning Engineer to join our Foundation Models team. In this role, you will drive the engineering, scaling, and operationalization of our internal reasoning Large Language Models (LLMs) and agentic systems, enabling them to succeed at complex biomolecular design and autonomous scientific workflows.
You will work at the intersection of engineering and research, spanning the full stack: from the agent orchestration logic that makes these systems scientifically useful, to the distributed infrastructure and MLOps/AgentOps that make them robust at scale.
In this role, you will:
  • Own the MLOps/AgentOps Stack for Foundation Models: Establish rigorous evaluation harnesses that check agent output against scientific ground truth, and the experiment tracking, system observability and monitoring, CI/CD, and infrastructure those systems depend on.
  • Agentic Systems Engineering: Architect and deploy autonomous agents that utilize tools, retrieve scientific evidence, and execute multi-step reasoning across drug discovery workflows. Design and implement advanced agent memory architectures and context management for long-horizon scientific tasks. Build reliable interfaces between agents and genomic, chemical, and clinical data sources.
  • Scalable ML Systems & Productionization: Design, build, and optimize large-scale distributed training and inference systems for foundation models. Own the production Python/PyTorch codebases that turn fast-moving research into enterprise-grade software.
  • Technical Strategy & Leadership: Define the long-term engineering roadmap for AI4DD's agentic and foundation models. Serve as a technical authority on ML infra for Genentech leadership, architect cross-functional platforms, and elevate the engineering bar across gRED.
  • Research-to-Production Translation: Partner closely with ML Scientists and domain experts to translate open-ended scientific problems and complex reasoning objectives into scoped, shippable, and highly efficient systems.

Who You Are:
  • Education & Experience: BS, MS, or PhD in Computer Science, Machine Learning, Engineering, or a related quantitative field. You have a demonstrated track record of technical leadership with increasing levels of experience based on degree: PhD with 5+ years, MS with 8+ years, or BS with 10+ years of industry experience building, shipping, and owning large-scale ML systems and infrastructure end-to-end.
  • Engineering Foundation: Exceptional Python programming skills and rigorous software engineering fundamentals (Git, automated testing, CI/CD, documentation, architecture design).
  • Deep Learning & Distributed Systems: Extensive hands-on experience with modern deep learning frameworks (PyTorch, JAX) and deploying ML infrastructure on AWS or HPC environments, including distributed training tools.
  • Agentic Systems: Practical experience designing agent orchestration frameworks (e.g., LangGraph, MCP-based tool integration), managing persistent agent memory, and building self-improving loops.
  • Domain Interest: Strong passion for applying frontier AI and agentic science to AI for Drug Discovery (AI4DD), biology, and chemistry.

Preferred Qualifications:
  • Inference-Time Optimization: Deep expertise in LLM serving, test-time compute, sampling/search strategies, model routing, batching, caching, and latency/cost/quality tradeoffs.
  • Scientific Context: Experience working with molecular modalities (e.g., protein sequences, chemical graphs, and structured molecular data).
  • Open Source: A public portfolio of significant technical contributions to open-source ML, systems, or MLOps libraries.

Onsite presence on our campus is expected in compliance with Roche/Genentech policy.
Relocation benefits are not available for this job posting.
The expected salary range for this position based on the primary location of New York is $192,500 - $357,500. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
#ComputationCoE
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.

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About Genentech

Sourced by ZipRecruiter

A member of the Roche Group, Genentech has been at the forefront of the biotechnology industry for more than 40 years, using human genetic information to develop novel medicines for serious and life-threatening diseases. Genentech has multiple therapies on the market for cancer & other serious illnesses. Please take this opportunity to learn about Genentech where we believe that our employees are our most important asset & are dedicated to remaining a great place to work.

Industry

Scientific research and development services

Company size

10,000+ Employees

Headquarters location

South San Francisco, CA, US

Year founded

1976

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