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Software Engineer, Machine Learning at Meta
Menlo Park, CA 94025, USA -
Full Time

Start Date

Immediate

Expiry Date

09 Nov, 25

Salary

200200.0

Posted On

09 Aug, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Availability, Distributed Systems, Git, Computer Science, Computer Engineering, Mathematics, Haskell, Applied Sciences, Machine Learning, Relational Databases, Computer Vision, Vim, Linux, Completion, Sql, Emacs, Perforce, Subversion, Natural Language Processing, C, Unix, Php

Industry

Computer Software/Engineering

Description

MINIMUM QUALIFICATIONS

  • Bachelor’s degree (or foreign degree equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field
  • Requires completion of a university-level course, research project, internship, or thesis in the following:

    1. Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow


      1. Machine learning, recommendation systems, ranking systems, computer vision, natural language processing, data mining, or distributed systems


        1. Developing and debugging in C/C++ and Java


          1. Scripting languages such as Perl, Python, PHP, or shell scripts


            1. C, C++, C#, or Java


              1. Python, PHP, or Haskell


                1. Relational databases and SQL


                  1. Software development tools: Code editors (VIM or Emacs), and revision control systems (Subversion, GIT, or Perforce)


                    1. Linux, UNIX, or other *nix-like OS including file manipulation and simple commands

                    • and 10. Distributed systems including sharding, consistency, and availability
                      For those who live in or expect to work from California if hired for this position.
                    Responsibilities
                    • Research, design, develop, and test operating systems-level software, compilers, and network distribution software for massive social data and prediction problems.
                    • Have industry experience working on a range of classification and optimization problems, e.g. payment fraud, click-through or conversion rate prediction, click-fraud detection, ads/feed/search ranking, text/sentiment classification, collaborative filtering/recommendation, or spam detection.
                    • Working on problems of moderate scope, develop highly scalable systems, algorithms and tools leveraging deep learning, data regression, and rules based models.
                    • Suggest, collect, analyze and synthesize requirements and bottleneck in technology, systems, and tools.
                    • Develop solutions that iterate orders of magnitude with a higher efficiency, efficiently leverage orders of magnitude and more data, and explore state-of-the-art deep learning techniques.
                    • Receiving general instruction from supervisor, code deliverables in tandem with the engineering team.
                    • Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU).