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Deep Learning Scientist Jobs (NOW HIRING)

Deep Learning Engineer

Palo Alto, CA · On-site

$150 - $190/hr

Masters in Computer Science, Software Engineering, Mathematics, or equivalent * Passion for computer vision and deep learning; you are excited to adapt the latest multimodal LLMs, or implement a ...

Deep Learning Engineer

Palo Alto, CA · On-site

$170K - $300K/yr

Masters in Computer Science, Software Engineering, Mathematics, or equivalent * Passion for computer vision and deep learning; you are excited to adapt the latest multimodal LLMs, or implement a ...

DEEP LEARNING ENGINEER

Windham, ME · On-site

$90 - $130/hr

We are seeking a talented and driven Deep Learning Engineer to join our team and play a critical ... Collaborate closely with data scientists to deploy models into production environments, integrating ...

New

Overview We're looking for a talented and intensely curious Machine Learning Scientist with deep expertise in building and deploying production machine learning models, particularly reinforcement ...

For more information about Spotter, please visit Overview We're looking for a talented and intensely curious Machine Learning Scientist with deep expertise in building and deploying production ...

Deep Learning Engineer

San Francisco, CA · On-site

$161K - $175K/yr

About the Deep Learning Team The Deep learning team's work is at the crux of Hayden AI's solutions ... Bachelors or Masters in Computer Science or related field * Nice to Have : Experience working in ...

Showing results 41-60

Deep Learning Scientist information

See salary details

$37.5K

$122.7K

$196.5K

How much do deep learning scientist jobs pay per year?

As of Sep 3, 2026, the average yearly pay for deep learning scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is a deep learning scientist?

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 are the key skills and qualifications needed to thrive as a deep learning scientist?

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 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.

More about Deep Learning Scientist jobs

What cities are hiring for Deep Learning Scientist jobs?

Cities with the most Deep Learning Scientist job openings:

What states have the most Deep Learning Scientist jobs?

States with the most job openings for Deep Learning Scientist jobs include:

Infographic showing various Deep Learning Scientist job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Machine Learning Scientist

Flagship Pioneering, Inc.

Somerville, MA • On-site

Full-time

Medical, Retirement

Posted 6 days ago


Job description

What if you could join a rapidly growing company and play a critical role in bringing new medicines to patients through looking at and treating disease in a revolutionary way.

What this position is all about:

We seek a Machine Learning Scientist to join our ML team and lead focused efforts in applying and adapting proprietary foundation models to enable Cellarity's predictive drug discovery platform. As a lead ML Scientist the candidate will develop and apply AI methods to identify novel interventions and targets to accelerate early drug discovery.

This role involves hands-on modeling of high-dimensional biological data, leveraging and fine-tuning state-of-the-art foundation models, while enabling interpretability and biological reasoning. Ideal candidate will have demonstrated application of deep learning and computational biology to biological problems.

The successful candidate will work closely with researchers in biology, chemistry, and omics technology in a collaborative environment.

What you would be responsible for:

  • Model Development

    • Apply, fine-tune, and post-train foundation models (e.g., transformer-based, diffusion, VAE architectures) on single-cell RNA-seq data and other modalities to model disease cellular states
    • Build state-of-the-art perturbation models using multi-modal perturbation data (small molecules, CRISPR, cytokines) and phenotypic data, with emphasis on CRISPR screen data (e.g., Perturb-seq).
    • Develop mechanistic interpretability methods to infer gene networks and regulatory mechanisms via attention, graph-based, and/or causal representation methods, supporting downstream applications such as target identification.
    • Deploy and run inference on generative AI models using proprietary datasets on cloud platforms.
    • Establish clinically relevant benchmarking and evaluation frameworks to assess context generalization and guide model improvements.
    • Stay current with the latest research in foundation models, representation learning (across biology, NLP, vision, and audio), and perturbation modeling.

    Scientific Collaboration

    • Collaborate with interdisciplinary scientists from biology, chemistry, and technology teams to translate research questions into cutting-edge ML solutions.
    • Opportunity to collaborate with and co-develop platform modules alongside other Flagship Pioneering companies.
    • Communicate technical concepts clearly to diverse scientific audiences.

What experiences will you need:

  • PhD in Computer Science, Computational Biology, or related field, OR Master's degree with 3+ years or Master's or Bachelor's degree with 6+ years of relevant ML research experience for drug discovery.
  • Strong foundation in statistics, deep learning and generative AI.
  • Experience with high-dimensional biological data analysis (bulk/single-cell RNA-seq, gene regulatory networks, PPI networks, multi-omics integration).
  • Experience with chemical / CRISPR perturbation screen data (e.g., Perturb-seq), including analysis and modeling.
  • Familiarity with single-cell foundation models (e.g., Geneformer, scGPT, scFoundation) and their downstream applications.
  • Experience applying or fine-tuning pretrained foundation models or deep generative models for downstream biological tasks.
  • Experience with cloud computing (AWS/GCP) and MLOps best practices.
  • Excellent communication skills and ability to work in interdisciplinary teams.

What sets you apart:

  • Experience building agentic AI systems (e.g., LLM-based agents, tool use, multi-step reasoning workflows) for scientific discovery or data analysis.
  • Familiarity with target identification and prioritization in drug discovery.

What it's like to work at Cellarity
At Cellarity, we

  • Push Boundaries: We create a legacy with breakthrough science in service of patients.
  • Inject Energy: We build strengths from different perspectives and tell it like it is
  • Own it: We transcend our job descriptions and relentlessly follow through on our commitments.
  • Go all out: We work quickly and with conviction.

Company Summary: Cellarity is a privately held, clinical-phase drug discovery startup using AI and single-cell omics to develop life-changing medicines that are unreachable by traditional methods of drug discovery. Our pipeline spans multiple exploratory programs across different indications, offering broad opportunities to apply machine learning to diverse disease areas. Cellarity is a product of Flagship Pioneering's venture creation engine, which has conceived and created companies such as Moderna (NASDAQ: MRNA), Generate:Biomedicines, and Lila Sciences.

Cellarity is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Recruitment & Staffing Agencies: Cellarity does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Cellarity or its employees is strictly prohibited unless contacted directly by Cellarity's internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Cellarity, and Cellarity will not owe any referral or other fees with respect thereto.

The salary range for this role is $132,000 - $209,000. Compensation for the role will depend on a number of factors, including a candidate's qualifications, skills, competencies, and experience. Cellarity currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Cellarity's good faith estimate as of the date of publication and may be modified in the future.