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Staff Machine Learning Engineer

San Francisco, California
US$180000 - US$220000 per annum + Competitive Benefits
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Staff Machine Learning Engineer

San Francisco Bay Area, CA $180,000 - $220,000 + Competitive Benefits

THE COMPANY

  • COMPANY: A large and growing start up in the online internet space that is using machine learning to cater to users using personlization and recommendations.
  • TEAM: Machine learning and data science is the core part of the company. Join a strong team of 10 data scientists and 5 machine learning engineers.
  • CULTURE: Casual work environment along with a diverse and inclusive culture; flexible working hours and a work from home policy. Flexibility to work from the office in the SouthBay or San Francisco.

THE ROLE

As a Staff machine learning engineer you will…

  • Build end to end machine learning models from ideation to deployment and scalability
  • Create innovative solutions using machine learning, NLP, or deep learning for recommendations, personalization, fraud detection, or ads ranking
  • Act as tech lead on projects for one entire area within the company
  • Ensure delivery of the team and projects
  • Work directly with stakeholders and other teams

YOUR SKILLS AND EXPERIENCE

  • 4+ years of full time experience in machine learning and engineering
  • Experience with large-scale, streaming ML distributed systems
  • Extensive experience building end to end models and pushing production level code
  • Knowledge and experience in at least one of the following areas: ads ranking, search, recommendation, personalization, fraud detection, computer vision, or NLP
  • Experience leading teams is a huge plus.
  • Tools: Python, SQL, AWS, Spark, Hive

THE BENEFITS

As a Staff Machine Learning Engineer, you can expect a base salary between $180,000 to $220,000 (based on experience) plus competitive benefits.

HOW TO APPLY

Please register your interest by sending your CV to Kristianna Chung via the Apply link on this page

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74892/KC
San Francisco, California
US$180000 - US$220000 per annum + Competitive Benefits
  1. Permanent
  2. Deep Learning and AI

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Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

Black History Month: Ethical AI and the Bias Within

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Biases are unmasked with and without intent. But, when it comes to AI, those unintentional biases can have devastating consequences. From 2015 to 2019, use of AI grew by over 250 percent and is projected to boast a revenue of over $100 billion by 2025. As major businesses such as Amazon and IBM cancel and suspend their facial recognition programs amidst protests against racial inequality, some realize more than regulatory change is needed. Since 2014, algorithms have shown biases against people of color and between genders. In a recent article from Time.com, a researcher showed the inaccuracies of prediction for women of color, in particular. Oprah Winfrey, Michelle Obama, and Serena Williams skewed as male. Three of the most recognizable faces in the world and AI algorithms missed the mark. These are the same algorithm and machine learning principles used to challenge humans at strategy games such as Chess and Go. Where’s the disconnect? According to one author, it may be time to create a new field of study specific to AI. Though created in Computer Science and Computer Engineering labs, the complexities of human are more often discussed in the field of humanities. To expand further as well into business schools, race and gender studies, and political science departments. How Did We Get Here? At first blush, it may not seem comparable to consider human history with the rise of artificial intelligence and its applications. Yet it’s human history and its social construct which explains the racial and gender biases when it comes to ethics in AI. How deep seated are such biases? What drives the inequalities when AI-enabled algorithms pass over people of color and women in job searches, credit scores, or assume status quo in incarceration statistics? Disparities between rational and relational are the cornerstone from which to begin. 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It is the quintessence of ‘those who don’t know their history are doomed to repeat it’. The difference within this scientific equivalent is there is no history to repeat when it comes to technology. And so it is from the humanitarian angle AI is considered. The ability to do great things with technology is writ in books and screenplays, and so are its dangers. While it isn’t likely an overabundance of ‘Mr. Smiths’ will fill our world, it is important we continue to break out of the siloes of science versus social sciences. If AI is to help humanity move forward, it’s important to ensure humanity plays a role in teaching our machine learning systems how different we are from each other and to consider the whole person, not just their exoskeleton. If you’re interested in the Data Sciences, Data and Technology, Machine Learning, or Robotics just to name a few, Harnham may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more.  For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

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Puzzle and Problem-Solvers: Software Engineers Drive Business

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