R&D Data Analysis and Machine Learning Software Engineer (Engineering ScientistAssociate)

University of Texas at Austin; Austin (TX); On-site; USD 89,000 - 120,000; Full time

Benefits offered by this job

Medical coverage

Retirement contributions

Paid vacation and sick time

Paid holidays

Job summary

University of Texas at Austin is seeking an R&D Data Analysis and Machine Learning Software Engineer (Engineering Scientist Associate) at the Pickle Research Campus to advance data-focused research in Environmental Sciences Laboratory (ESL). You will design and deploy ML and data analysis solutions, write robust software, and prepare technical reports.

You will collaborate with sponsors and team members, work in a UNIX/Linux environment using MATLAB or Python, and contribute to multiple projects

Qualifications

• Bachelor's degree in the natural or engineering sciences including computer science, electrical engineering, or related discipline.
• Proficiency working with data algorithms (regression, probability, statistics).
• Experience with machine learning algorithms and application libraries (Tensorflow, Keras, Theano, Torch, Infer.NET, or equivalent).
• Demonstrated strong math background.
• Experience developing applications in MATLAB or Python in a UNIX/Linux environment.

Responsibilities

• Design, develop, configure, apply, test, and support both data analysis and machine learning algorithms.
• Designing and writing flexible and maintainable software according to software designs and test to ensure software meets project requirements.
• Preparation of analysis presentations and technical reports.
• Communicate with project team members, supervisors, and sponsors for timely implementation of project requirements.
• Reviewing peer developed software to improve other developer's designs and implementations.
• Deploying and supporting software outside of ARL.
• Other related functions as assigned.

Skills

Data algorithms

Machine learning algorithms

Strong math background

Education

Bachelor's degree in natural or engineering sciences including computer science or electrical engineering

Tools

MATLAB

Python

TensorFlow

Keras

Theano

Torch

Infer.NET

SQL

C/C++

Job description

Job Posting Title: R&D Data Analysis and Machine Learning Software Engineer (Engineering Scientist Associate)

Hiring Department: Applied Research Laboratories

Position Open To: All Applicants

Weekly Scheduled Hours: 40

FLSA Status: Exempt from FLSA

Earliest Start Date: Immediately

Position Duration: Expected to Continue

Location: PICKLE RESEARCH CAMPUS

Job Details:

Research and development for both data analysis and machine learning applications, including data modeling and algorithm development and implementation. Software design, development, and testing to support research and development efforts within the Environmental Sciences Laboratory (ESL) of Applied Research Laboratories.

Responsibilities:

• Design, develop, configure, apply, test, and support both data analysis and machine learning algorithms.

• Designing and writing flexible and maintainable software according to software designs and test to ensure software meets project requirements.

• Preparation of analysis presentations and technical reports.

• Communicate with project team members, supervisors, and sponsors for timely implementation of project requirements.

• Reviewing peer developed software to improve other developer's designs and implementations.

• Deploying and supporting software outside of ARL.

• Other related functions as assigned.

Required Qualifications:

• Bachelor's degree in the natural or engineering sciences including computer science, electrical engineering, or related discipline.

• Proficiency working with data algorithms (regression, probability, statistics).

• Experience with machine learning algorithms and application libraries (Tensorflow, Keras, Theano, Torch, Infer.NET, or equivalent).

• Demonstrated strong math background.

• Experience developing applications in MATLAB or Python in a UNIX/Linux environment.

Applicant must have a dynamic skill set, be willing to work with new technologies, be highly organized and capable of planning and coordinating multiple tasks and managing their time. The position will require: attention to detail, effective problem-solving skills, sound engineering judgment, ability to work independently with sensitive and confidential information, ability to maintain a professional demeanor and work as a team member without daily supervision, and effectively communicate with various groups of clients; ability to work under pressure and accept supervision; regular and punctual attendance.

US Citizen: Applicant selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information at the level appropriate to the project requirements of the position.

Preferred Qualifications:

• Master's degree or Ph. D. in the natural or engineering sciences including computer science, electrical engineering, or related discipline.

• Advanced coursework or significant experience related to data analysis (statistics, pattern recognition, scalable machine learning, etc.).

• Current or recent eligibility for access to classified information.

• Two or more years of experience with one or more of the following:

  • Developing and evaluating data algorithms (regression, probability, statistics)

  • Machine learning algorithms and application libraries (Tensorflow, Keras, Theano, Torch, Infer.NET, or equivalent)

• Experience working in an applied research environment.

• Experience analyzing large, complex datasets using scalable techniques.

• Experience with MATLAB MEX objects.

• Experience with database integration and SQL programming.

• Experience with C/C++.

• Experience with signal processing algorithms.

• Experience with scripting languages (Python, Shell).

• Knowledge of version control systems or defect tracking systems.

• Prior work experience in professional or research-oriented software development.

• Proven ability to work independently, formulate research plans, take initiative, and mentor other staff.

• Demonstrated excellent interpersonal communication and presentation skills.

• Cumulative GPA of 3.0 or greater.

General Notes:

An agency designated by the federal government handles the investigation as to the requirement for eligibility for access to classified information. Factors considered during this investigation include but are not limited to allegiance to the United States, foreign influence, foreign preference, criminal conduct, security violations, drug involvement, the likelihood of continuation of such conduct, etc.

Benefits:

• 100% employer-paid basic medical coverage

• Retirement contributions

• Paid vacation and sick time

• Paid holidays

Salary Range

$88,500-$120,000+/negotiable depending on qualifications

Working Conditions:

• Standard office conditions

• Repetitive use of a keyboard at a workstation

• Use of manual dexterity

• Some weekend, evening and holiday work

• Possible Intrastate/interstate/international travel

Required Materials:

• Resume/CV

• 3 work references with their contact information; at least one reference should be from a supervisor

• Letter of interest

• Unofficial college transcript

Employment Eligibility:

Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible may apply for positions without supervisor approval.

Retirement Plan Eligibility:

The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length.

Background Checks:

A criminal history background check will be required for finalist(s) under consideration for this position.

Equal Opportunity Employer:

The University of Texas at Austin, as an equal opportunity/affirmative action employer,complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.

Pay Transparency:

The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information.

E-Verify:

• E-Verify Poster (English and Spanish) [PDF]
• Right to Work Poster (English) [PDF]
• Right to Work Poster (Spanish) [PDF]

Compliance:

Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031.

The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.