2,566 Machine Learning Scientist jobs in the United States

Job Description

About Us

Our mission is to cure cancer through high performance, accessible early cancer detection. That means saving lives.

Delfi Diagnostics is a Johns Hopkins spinoff focused on the non-invasive detection of cancer at earlier stages, when it is most curable. DELFI uses artificial intelligence and whole-genome sequencing to detect unique patterns of DNA fragmentation in the blood of patients with cancer. These analyses are performed through simultaneous examination of millions of DNA sequences using machine learning to identify tumor-specific abnormalities.

In our passionate pursuit to radically improve health outcomes, we serve humanity when we:

Lead with Science, Anchor in Pragmatism : We pioneer life-changing science by ensuring quality, transparency, and rigor at all times.

Build With & For All : We embrace diverse backgrounds to innovate and achieve together. We are not just building a product—we aim to disrupt the path of cancer for all, no matter geography or socioeconomic class.

Put We over I : We are a home for high-performing people. Through teamwork, we build collective intelligence. Each of us wins when those we serve and those who serve with us win. We show up with empathy, humility, and integrity at every step of the journey.

About the Role

In this role, you will develop, tune, and advance Delfi’s machine learning models for early cancer detection. You’ll focus on improving model performance through structured experimentation, creative modeling strategies, and rigorous benchmarking—pushing models up internal leaderboards and identifying what drives improvement.

You’ll also explore how the raw intelligence of large language models (LLMs) can be applied to improve model performance and feature representations—leveraging their reasoning capabilities. Working closely with bioinformaticians, engineers, and data scientists, you’ll operate at the intersection of machine learning and biology, translating genomic signals into clinically meaningful insight.

This role is ideal for a scientist who enjoys hands-on modeling, thrives on iteration and discovery, and seeks to combine deep technical understanding with curiosity about new forms of machine intelligence. While prior industry experience is preferred, we also welcome exceptional PhD graduates or postdocs who have demonstrated strong applied ML engineering skills and a track record of collaborative, reproducible work.

• You’ll design, implement, and optimize machine learning models for genomic and fragmentomic data, perform systematic benchmarking to assess model quality, and analyze the factors that drive predictive improvements.
• You’ll explore the use of LLMs to enhance feature representations and model architectures.
• You’ll ensure robust, reproducible experimentation through sound data practices and MLOps best practices such as versioning, model tracking, and environment management.
• You’ll collaborate closely with teams across computational biology, bioinformatics, and software engineering to build shared understanding and integrate insights from data.

• You will have led multiple modeling efforts that improved performance on Delfi’s internal leaderboards through careful experimentation and analysis.
• You will have evaluated and demonstrated how LLMs can strengthen feature representations or model architectures to achieve measurable performance gains.
• You will be recognized as a rigorous and creative contributor—someone who combines scientific depth with curiosity and collaboration to drive the frontier of ML for early cancer detection.

• MS or PhD in Computer Science, Machine Learning, Computational Biology, Applied Mathematics, or a related field
• Experience developing and evaluating ML models in applied or collaborative research settings, with a demonstrated ability to deliver high-quality, maintainable code and reproducible results
• Experience working in team-based environments with shared codebases and version control practices
• Proficiency in Python, including use of ML frameworks such as PyTorch, TensorFlow, or scikit-learn
• Experience applying MLOps best practices for experiment tracking, model versioning, or data pipeline reproducibility (e.g., MLflow, Weights & Biases, or equivalent)
• Demonstrated success improving model performance through experimentation, architecture design, or advanced optimization methods
• Familiarity with large language models (LLMs), including APIs, frameworks, and fine-tuning methods
• Strong grounding in statistics, data analysis, and reproducible experimentation
• Excellent communication skills and the ability to collaborate effectively across scientific and technical disciplines

Preferred

• Experience with genomic, sequencing, or other biological data
• Exposure to cloud-based ML environments (AWS, GCP) or large-scale data pipelines
• Background in deep learning, probabilistic modeling, or ensemble methods
• Record of research publication, technical presentations, or open-source contributions

Total Compensation at DELFI is a combination of salary, bonus, equity, and benefits. Actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skillset, years & depth of experience, certifications & relevant education, geography.

An Equal Opportunity Employer

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Strategically positioned in the Chief Technology Office, our work spans across Cybersecurity, Global Technology Infrastructure and the Software Development Lifecycle (SDLC). With this unparalleled access to technology groups in the firm, the role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the bank operates.

As a Machine Learning Scientist, you will apply sophisticated machine learning methods to a wide variety of complex tasks including data mining and exploratory data analysis and visualisation, text understanding and embedding, anomaly detection in time series and log data, large language models (LLMs) and generative AI for technology use-cases, reinforcement learning and recommendation systems. You must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. You must also have a passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. You must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.

Job Responsibilities

• Research and explore new machine learning methods through independent study, attending industry-leading conferences and experimentation
• Develop state-of-the art machine learning models to solve real-world problems and apply it to complex business critical problems in Cybersecurity, Software and Technology Infrastructure
• Collaborate with multiple partner teams in Cybersecurity, Software and Technology Infrastructure to deploy solutions into production
• Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
• Contribute to reusable code and components that are shared internally and also externally

• PhD in a quantitative discipline (e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science.) with 1 year experience Or Masters with 2 years of industry or research experience in the field.
• Hands-on experience and solid understanding of machine learning and deep learning methods
• Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
• Extensive experience with large language models (LLMs) and accompanying tools & techniques in the LLM ecosystem (e.g. LangChain, LangGraph, Vector databases, opensource Models, RAG, Agentic Systems & Workflows, LLM fine-tuning)
• Scientific thinking and the ability to invent
• Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
• Experience with big data and scalable model training
• Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
• Curious, hardworking and detail-oriented, and motivated by complex analytical problems
• Ability to work both independently and in highly collaborative team environments

• Strong background in Mathematics and Statistics
• Familiarity with the financial services industries
• Experience with A/B experimentation and data/metric-driven product development
• Experience with cloud-native deployment in a large scale distributed environment
• Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
• Ability to develop and debug production-quality code
• Familiarity with continuous integration models and unit test development

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans

Base Pay/Salary
Jersey City,NJ $128,250.00 - $195,000.00 / year

The VCV Science Team is pioneering the future of visual creativity through innovative AI. We're seeking an Applied ML Scientist who thrives at the intersection of computer vision and generative AI to help us build breakthrough experiences with image, video, and 3D generation technologies that will delight and inspire millions of users worldwide. As part of the team, you will have the opportunity to incubate powerful research ideas, partner with teams across Apple to push the boundaries of what's possible, and transform groundbreaking research into magical user experiences

Description

As an Applied ML Scientist on our team, you will: - Design and develop pioneering generative models for visual content such as image, video, and 3D. - Stay at the forefront of artificial intelligence and machine learning advancements. Continuously exploring and evaluating new technologies and methodologies to enhance the technical capabilities of our team. - Push boundaries by rapidly prototyping ideas, testing hypotheses, and exploring novel approaches to visual generation challenges, including exploring new architectures ranging from diffusion, auto-regressive, multi-modal generation and/or hybrid approaches. - Partner with world-class researchers, engineers, and designers to transform prototypes into robust, production-ready features that will delight users while maintaining Apple's standards for quality and privacy. - Explore distillation and optimization techniques, that will enable the models to run efficiently, while maintaining quality. - Present your work directly to executive leadership and shape the roadmap for how AI will transform creative tools across Apple's ecosystem.

Minimum Qualifications

• M.S. in Computer Science, Machine Learning, Computer vision or related field.
• Solid understanding and experience with recent visual generative AI models, such as multi-modal LLMs, and diffusion-based large vision models.
• Proficiency in Python, and experience in using ML toolkits, e.g., PyTorch, JAX etc.
• Knowledge of and proficiency with ML-based product lifecycle, and methods for model training, statistical analysis and data science.

Preferred Qualifications

• 5+ years of industry or academic experience in CVML and an advanced degree (M.Sc./Ph.D.) in CS, CVML or similar field.
• Hands-on experience with building, training from scratch and scaling innovative visual generation models, such as large image/video diffusion models, multi-modal LLMs, contrastive learning models, or other visual foundation models.
• Strong software engineering skills to build scalable and robust infrastructure for deep learning data, modeling, and evaluation systems.
• Comfort with researching current ML literature and math including optimization methods and modeling techniques.
• Proven track record in research, innovation and/or delivering ML products, demonstrated through publications in top-tier journals or conferences, patents, or impactful software developments.
• Strong collaboration skills in a multi-functional setting, and ability to leverage partnerships to drive outcomes and deliver impact at scale.

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $139,500 and $258,100, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .

Submit Resume

• Lead the design, development, and implementation of cutting-edge machine learning models and algorithms.
• Conduct advanced research in areas such as deep learning, reinforcement learning, natural language processing, or computer vision.
• Collaborate with domain experts to define research problems and translate them into machine learning frameworks.
• Develop and prototype novel ML solutions, rigorously evaluating their performance and scalability.
• Publish research findings in top-tier scientific journals and present at international conferences.
• Mentor and guide junior scientists and engineers, fostering a culture of innovation and technical excellence.
• Contribute to the strategic direction of the organization's AI research roadmap.
• Ensure the reproducibility and robustness of research methodologies and results.
• Stay abreast of the latest advancements in machine learning and artificial intelligence research.
• Optimize ML models for deployment in production environments, working closely with engineering teams.
• Contribute to the development of intellectual property through patents and publications.

• Ph.D. in Computer Science, Machine Learning, Statistics, Physics, Mathematics, or a related quantitative field.
• Minimum of 8 years of research experience in machine learning or artificial intelligence, with a strong publication record in leading venues.
• Deep theoretical understanding and practical experience with various ML techniques, including deep learning frameworks (e.g., TensorFlow, PyTorch).
• Proficiency in programming languages such as Python, C++, or Java.
• Experience with large-scale data analysis and distributed computing environments.
• Proven ability to lead research projects from conception to completion.
• Excellent analytical, problem-solving, and critical thinking skills.
• Exceptional communication and interpersonal skills, with the ability to articulate complex technical concepts clearly.
• Demonstrated ability to mentor and lead teams.

• Leading the design and implementation of novel machine learning algorithms and models for various applications.
• Conducting cutting-edge research in areas such as deep learning, natural language processing, computer vision, and reinforcement learning.
• Developing and optimizing ML pipelines for training, evaluation, and deployment of models at scale.
• Collaborating with software engineers and product managers to integrate ML solutions into existing and new products.
• Analyzing large, complex datasets to extract meaningful insights and identify opportunities for ML-driven improvements.
• Mentoring and guiding a team of machine learning scientists and engineers.
• Staying abreast of the latest advancements in AI and ML research and publishing findings in top-tier conferences and journals.
• Developing and maintaining robust ML infrastructure and tooling.
• Evaluating and selecting appropriate ML techniques and frameworks for specific problem domains.
• Contributing to the strategic roadmap for AI and ML development within the organization.

• Lead the research and development of novel machine learning algorithms and models.
• Design, implement, and validate complex ML systems for scientific applications.
• Analyze and interpret large, complex datasets to extract actionable insights.
• Collaborate with domain experts and other scientists to define research objectives and hypotheses.
• Develop and maintain robust ML pipelines for training, evaluation, and deployment.
• Stay abreast of the latest advancements in machine learning, deep learning, and AI research.
• Publish research findings in top-tier conferences and journals.
• Mentor junior scientists and engineers, fostering a culture of scientific excellence.
• Communicate complex technical concepts clearly to both technical and non-technical audiences.
• Contribute to the strategic direction of the company's AI and ML initiatives.

• Ph.D. in Computer Science, Statistics, Physics, or a related quantitative field, with a specialization in Machine Learning or Artificial Intelligence.
• 10+ years of research and development experience in machine learning, with a strong publication record.
• Demonstrated expertise in deep learning frameworks (e.g., TensorFlow, PyTorch) and advanced ML techniques.
• Proficiency in programming languages such as Python, with extensive experience in scientific computing libraries (NumPy, SciPy, Pandas).
• Experience with large-scale data processing and distributed computing frameworks (e.g., Spark).
• Strong understanding of statistical modeling, probability, and data mining.
• Excellent problem-solving, critical thinking, and communication skills.
• Proven ability to work independently and lead research projects in a remote setting.
• Experience in a specific scientific domain (e.g., genomics, astrophysics, drug discovery) is a plus.

Job Description

About Tahoe Therapeutics

Tahoe Therapeutics is a biotechnology company pioneering a fundamentally new approach to drug discovery — one that begins with the biology of real patients. Our Mosaic platform is the first to make in vivo data generation scalable, with single-cell resolution, allowing us to map how drugs affect patient-derived cells in the body across a wide range of biological contexts. We are building the world’s largest in vivo single-cell perturbation atlas — and using it to train multimodal foundation models that learn the context-dependent nature of gene function, disease progression, and drug response. By combining cutting-edge machine learning with the most biologically relevant datasets ever assembled in drug discovery, our mission is to find better drugs, faster — and bring them to more patients who need them.

Your role

As a Senior Machine Learning Scientist , you will play a leading role in designing the next generation of foundation models of gene regulatory networks powered by Tahoe's large scale single-cell datasets such as Tahoe-100M . This role is well-suited for someone with a strong background in machine learning and statistics, and an interest in applying cutting-edge breakthroughs in ML to meaningful problems in drug discovery. We are looking for non-incremental thinkers with the skills to help build models that can make a real impact on drug discovery.

• PhD or equivalent practical experience in a technical field.
• A proven track record of developing and applying deep learning methods, including experience with modern architectures such as transformers, state-space models, graph neural networks or diffusion-based generative models.
• Proficiency with modern ML frameworks (e.g., PyTorch, JAX, or TensorFlow) and core scientific computing libraries (e.g., NumPy, SciPy, Pandas).
• A genuine enthusiasm for applying cutting-edge ML research to real-world biological problems and a bias towards action.

• Prior experience with ML applied to problems in biology or chemistry.
• Familiarity with multimodal modeling, contrastive learning or self-supervised learning.
• Experience with large-scale distributed ML techniques (e.g., FSDP, TP, dMoE, flash attention)

• Develop and apply machine learning techniques towards building multi-modal foundation models that bridge the chemical and biological domains, i.e.: integrate models of chemical structure, target protein sequence and whole transcriptome scRNAseq.
• Stay at the forefront of ML and computational biology research and rapidly adopt state-of-the-art techniques to our problems and datasets.
• Collaborate with our team of biologists and engineers in cross-functional pods to test novel ML-driven hypotheses.

• Unlimited Paid Time Off (PTO).
• Monthly Lunch budget
• One-time Office set up budget
• US Employees: HMO Kaiser Platinum and PPO Anthem Gold medical as well as vision and dental plans for both the employee and dependents.

This hybrid role does not necessitate daily on-site attendance, but it does require the ability to access our offices in either South San Francisco, CA, or Toronto, ON; we welcome applications from candidates in these regions or those willing to relocate to the Bay Area or the Greater Toronto Area. Please note, we have one role open to two geographical locations.

Job Description

Description

The role: We are seeking a creative, accomplished Principal Machine Learning Scientist to advance the state of the art in ML-driven therapeutic antibody design.

At BigHat Biosciences, our full-stack antibody drug development platform uses ML to drive every stage from discovery to optimization. Our roboticized high-throughput wet-lab continually adds to our large proprietary datasets, which are piped through a custom LIMS+ data management and orchestration layer to automatically update and deploy the latest models. This makes the development of complex, next-gen therapeutics ‘trivially parallelizable’, at a pace that only accelerates as we develop better ML tooling.

You’re not interested in just git-cloning the latest NeurIPS pub and swapping out the dataset. Motivated by an enthusiasm for the possibility of addressing unmet patient needs and a curiosity about the underlying biology, you’ll apply your world-class ML skillset to refine and expand this state-of-the-art protein engineering platform. Success will mean not only hands-on methods development, but helping shape the direction for future ML research, and actively participating in the application of our platform to the accelerated design of new therapeutics.

• Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets of thousands to millions of antibodies.
• Identify opportunities for improvement in our ML tooling, and help to set strategy for ML research, driven by a strong high-level understanding of real-world drug development challenges
• Develop, refine, and deploy de novo design methods for generating initial hits to challenging, therapeutically interesting targets.
• Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems for validation and deployment in our high-throughput wet lab - at BigHat success is only declared upon synthesis of real antibodies with drug-like properties.
• Maintain an in-depth understanding of the current state-of-the-art in ML-driven protein engineering, both in the literature and at BigHat.
• Share your findings at top-tier conferences and publish in leading scientific journals to advance the field of protein engineering.
• Provide technical guidance and mentorship to other ML and data science FTEs and interns.
• Provide ML expertise and support for ongoing therapeutics programs, directly contributing to the development of new drugs.
• Collaborate with our engineering team to ensure maximal efficiency in the automated deployment of our latest models to ongoing drug development programs.
• Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc. to identify inefficiencies or potential improvements in BigHat’s platform, and plan and prioritize ML methods development accordingly.

• PhD in ML/CS or in the hard sciences with 5+ years experience developing and applying novel ML methods and a strong quantitative background.
• Publications in major ML conferences and/or leading journals, or extensive demonstrable track record developing and applying novel ML in industry.
• Strong competency in Python, familiarity with PyTorch, and experience with modern software engineering best practices.
• Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
• Enjoys a fast-paced environment and excels at executing across multiple projects.
• Familiarity with the current state-of-the-art in ML-driven protein engineering
• Nice-to-haves include experience with de novo design, NGS data, Bayesian optimization, familiarity with antibody biology and drug development, and experience training and deploying models on AWS.

The salary estimated for this position is $233,000 - $266,000 + bonus + options + benefits. Compensation will vary depending on job-related knowledge, skills, and experience. Actual compensation will be confirmed in writing at the time of the offer.

What BigHat Offers:

• Range of health insurance plan options through Anthem and Kaiser (monthly credit if benefit waived)
• Dental, and vision coverage through Guardian
• Additional well-being benefits through Nayya, OneMedical, Wagmo, Rula, and more
• 401(k) with company match 
• DTO, two weeks of company-wide shutdown, and 12 company holidays
• Paid parental leave