Rust Engineer

San Francisco, California, United States

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Sports Analytics, Betting, Fantasy Sports, Data ProductsIndustries

Requirements

Candidates should possess a Bachelor's Degree in Computer Science, Data Science, or a similar major, with a minimum of 1 year of software engineering experience in Rust, preferably 3 years. A minimum of 3 years of experience developing high-performance, scalable, and reliable production systems is required, along with proficiency in Python for data extraction, wrangling, and analysis, and strong SQL querying skills. The ability to work independently and take initiative is also essential. Preferred qualifications include experience with Apache Kafka and similar systems, exposure to the data science process and tech stack, and deep knowledge of football, basketball, or baseball.

Responsibilities

The Rust Engineer will develop high-performance, low-latency products for result verification and in-game reliability. Responsibilities include writing sophisticated, fast, and readable Rust code for complex data science infrastructure, designing core backend software components primarily in Rust, and building internal and external tools for the live trading platform. The engineer will also identify data inaccuracies within data pipeline dependencies and the Python codebase, build, test, debug, and deploy production-grade components, proactively improve the Rust and Python codebase, and conduct deep dives into production model features to explain project market lines.

Skills

Rust

Python

SQL

Data Extraction

Data Wrangling

Data Analysis

High-performance systems

Low-latency systems

Scalable systems

Reliable systems

Production-grade components

Apache Kafka

Data Science

Swish Analytics

Sports analytics and optimization tools provider

About Swish Analytics

Swish Analytics specializes in sports analytics and optimization tools for daily fantasy sports and sports betting, focusing on major U.S. leagues like the NFL, MLB, NBA, and NHL. The company uses an advanced machine learning system to analyze large datasets, providing accurate sports predictions and optimized lineups. This helps users, including individual bettors and professional operators, make informed decisions about their bets and fantasy picks. Swish Analytics differentiates itself by being an Authorized MLB Data Distributor, establishing trust in the sports betting community. Operating on a subscription-based model, users can access various levels of tools and analytics, starting with a free trial. The goal of Swish Analytics is to maximize return on investment for clients by identifying the best bets and balancing risk and reward for long-term success.

San Francisco, CaliforniaHeadquarters

2014Year Founded

$6.5MTotal Funding

EARLY_VCCompany Stage

Fintech, AI & Machine Learning, Financial ServicesIndustries

51-200Employees

Benefits

Remote Work Options

Risks

Increased competition from AI-driven startups could erode Swish Analytics' market share.

Consumer privacy concerns may impact Swish Analytics' data collection practices.

Potential regulation of sports betting advertising could affect Swish Analytics' revenue streams.

Differentiation

Swish Analytics uses proprietary algorithms for accurate sports predictions and optimized lineups.

The company is an Authorized MLB Data Distributor, enhancing its credibility in sports betting.

Swish Analytics offers a subscription model with free trials, attracting diverse user segments.

Upsides

Increased legalization of sports betting in the U.S. expands Swish Analytics' market opportunities.

The rise of AI-driven personalized betting experiences aligns with Swish Analytics' machine learning expertise.

Growing interest in micro-betting offers Swish Analytics a chance to expand its offerings.

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