ZipRecruiter

Log In

1

Machine Learning Postdoc Jobs in Dallas, TX (NOW HIRING)

next page

Showing results 1-20

People also search for

All JobsMachine Learning Postdoc JobsMachine Learning Postdoc Jobs in Dallas, TX

Machine Learning Postdoc information

See Dallas, TX salary details

$21.7K

$112.5K

$211.7K

How much do machine learning postdoc jobs pay per year?

As of Jul 7, 2026, the average yearly pay for machine learning postdoc in Dallas, TX is $112,490.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,233.00 and $154,924.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Machine Learning Postdoc position, and why are they important?

To thrive as a Machine Learning Postdoc, you need a deep understanding of machine learning algorithms, statistical modeling, and research methodology, typically supported by a completed PhD in a related field. Proficiency with programming languages like Python or R, experience with ML libraries (e.g., TensorFlow or PyTorch), and familiarity with large-scale datasets and cloud computing platforms are important. Strong analytical thinking, effective communication, and the ability to collaborate across multidisciplinary teams are standout soft skills in this position. These qualifications ensure innovative research contributions, successful project execution, and effective dissemination of findings in both academic and applied settings.

What is a Machine Learning Postdoc job?

A Machine Learning Postdoc is a research-focused position typically held after earning a Ph.D. in a related field. It involves conducting advanced research in machine learning, developing new algorithms, and publishing in top-tier conferences and journals. Postdocs often collaborate with faculty, industry partners, and other researchers to advance the state of the art in AI. The role may include mentoring students and contributing to grant proposals. It serves as a bridge between doctoral studies and a long-term academic or industry research career.

What are the typical responsibilities and collaborative aspects of a Machine Learning Postdoc position?

A Machine Learning Postdoc typically conducts original research, develops and tests new algorithms, and contributes to academic publications or patent applications. Daily tasks often involve data analysis, model building, and experimentation using advanced computational tools. Collaboration is key in this role, as postdocs frequently work alongside faculty, graduate students, and external industry partners to advance research objectives. Additionally, they may mentor junior researchers or students, present at conferences, and participate in grant writing or project planning. This mix of independent research and team collaboration fosters both professional growth and impactful scientific advancements.

What are the most commonly searched types of Machine Learning Postdoc jobs in Dallas, TX? The most popular types of Machine Learning Postdoc jobs in Dallas, TX are:
What are popular job titles related to Machine Learning Postdoc jobs in Dallas, TX? For Machine Learning Postdoc jobs in Dallas, TX, the most frequently searched job titles are:
What job categories do people searching Machine Learning Postdoc jobs in Dallas, TX look for? The top searched job categories for Machine Learning Postdoc jobs in Dallas, TX are:
What cities near Dallas, TX are hiring for Machine Learning Postdoc jobs? Cities near Dallas, TX with the most Machine Learning Postdoc job openings:
Machine Learning Postdoc jobs near you
Infographic showing various Machine Learning Postdoc job openings in Dallas, TX as of July 2026, with employment types broken down into 1% As Needed, 79% Full Time, 18% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $112,490 per year, or $54.1 per hour.
Data Scientist - Innovation - PhD (Irving, TX)

Data Scientist - Innovation - PhD (Irving, TX)

Caris Life Sciences

Irving, TX • On-site

Full-time

Re-posted 28 days ago

Job description

Job Summary:
Caris Life Sciences is transforming cancer care through precision medicine and cutting-edge molecular science. As a Data Scientist on the Innovation Team, you will develop machine learning and deep learning algorithms on molecular sequencing data to improve cancer diagnostics and treatment.
Responsibilities:
• Processing, manipulating, and analyzing large diverse datasets generated from NGS to develop biomarkers for cancer diagnosis, prognosis, and treatment.
• Developing novel algorithms for feature extraction and biomarker discovery from molecular sequencing data.
• Applying first-principles analysis to translate open research questions into tractable, well-defined problems.
• Applying state-of-the-art machine learning and deep learning methods to biological and clinical research questions.
• Creating rigorous evaluation frameworks and tracking experiments systematically using tools such as MLflow or Weights & Biases.
• Authoring peer-reviewed research publications and presenting findings at scientific conferences.
Qualifications:
Required:
• PhD in Data Science, Bioinformatics, Computational Biology, Genomics, Statistics, Computer Science, Engineering, Biophysics, or a related quantitative or biological field.
• PhD recently completed, or up to approximately 2 years of post-doctoral research experience (academic or industry).
• Demonstrated work on a cancer biology or translational research problem (PhD thesis chapter, peer-reviewed publication, or postdoc / industry role).
• Hands-on experience with molecular sequencing data (e.g., WGS, WES, RNA-seq, cfDNA) including production-grade pipelines and analysis.
• Hands-on experience with generative AI -- large language models, foundation models (e.g., genomic or protein language models), or agentic workflows applied to scientific or clinical data.
• Proficiency with PyTorch and modern deep learning architectures (transformers, attention mechanisms), with demonstrated application of ML/DL to biological or clinical data.
• First-author or co-first-author peer-reviewed publications in machine learning venues (e.g., NeurIPS, ICML, ICLR) or in bioinformatics / computational biology journals.
• Strong Python; comfortable in Linux; proficient with git and collaborative workflows.
Preferred:
• Multi-omics integration experience (genomics, transcriptomics, proteomics, methylation, etc.).
• Experience with epigenetics -- DNA methylation analysis, chromatin biology, or related.
• Interest in cell-free DNA, liquid biopsy, and next-generation early cancer diagnostics.
• Interest in novel algorithm development for biomedical signal extraction in sequencing data.
• Proficiency in cloud platforms (AWS EC2, S3, HealthOmics) and containerization (Docker).
Company:
Caris Life Sciences develops molecular profiling and AI-driven technologies to support precision medicine in oncology. Founded in 2008, the company is headquartered in Irving, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

Apply