Skip to content

Software Engineer – Machine Learning
Company | Meta |
---|
Location | Menlo Park, CA, USA |
---|
Salary | $187974 – $200200 |
---|
Type | Full-Time |
---|
Degrees | Master’s |
---|
Experience Level | Mid Level |
---|
Requirements
- Master’s degree (or foreign degree equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field
- Completion of a university-level course, research project, internship, or thesis in Machine Learning Framework(s): PyTorch, MXNet, or Tensorflow
- Experience in machine learning, recommendation systems, computer vision, natural language processing, pattern recognition, signal processing, data mining, artificial intelligence, or information retrieval
- Translating insights into business recommendations
- Hadoop, HBase, Pig, MapReduce, Sawzall, Bigtable, or Spark
- Developing and debugging in C, C++, and Java
- Scripting languages: Perl, Python, PHP, or shell scripts
- Relational databases and SQL
- Linux, UNIX, or other *nix-like OS as evidenced by file manipulation, advanced commands, and shell scripting
- Build highly-scalable performant solutions
- Data processing, programming languages, databases, networking, operating systems, computer graphics, or human-computer interaction
- Applying algorithms and core computer science concepts to real world systems as evidenced by recognizing and matching patterns from different areas of computer science in production systems
- Distributed systems
- Leveraging state-of-the-art neural networks and objectives to real world large-scale data and problem solving
- Developing highly scalable classifiers and embedding learners
- Similarity search and dimension reduction on massive scale of data and embeddings
- Tools leveraging machine learning, statistics, regression, rules-based models, or mathematical models
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 ranking, classification, recommendation, 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)
- Develop rule-, model-, online experiment, and human rater-based methods to collect training labels for data analysis and machine learning model iteration
- Without direct assistance, make the most of ability to recognize and match patterns from different areas of computer science in production systems and hone skill in making architectural decisions
Preferred Qualifications
No preferred qualifications provided.