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Jobs/Machine Learning Engineer Role/Machine Learning Engineer

Machine Learning Engineer

Helm.aiRemote - USA$150k – $250k+ Equity1mo ago
RemoteSeniorNAArtificial IntelligenceMachine Learning EngineerPythonReportingOptimismBase

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Requirements

• A sense of practical optimism: not all experiments are successful, but the ones that are more than make up for it! • Proficiency in Python • Proven ability to thrive in fast-paced environment • Ability to communicate complex technical concepts to colleagues and a variety of audience • Introspection, thoughtfulness, and detail-orientation • The following are a plus, but not required: • Master’s or Ph.D. in a related field and/or 5+ years of experience in a directly related field • Experience reading research papers and implementing the techniques therein • Computer vision and deep learning experience • The pay range for this position is estimated to fall in the base range of approximately $150,000 and $250,000. Base compensation for this position will vary based on location, qualifications, and relevant experience. The offered base salary may be above or below this range and compensation for the position may include additional compensation in the form of equity or a bonus/commission.

Responsibilities

• Characterize neural network quality, failure modes, and edge cases based on research data • Maintain awareness of current trends in relevant areas of research and technology • Coordinate with researchers and accurately convey status of experiments • Manage a large number of concurrent experiments and make accurate time estimates with respect to deadlines • Review experimental results and suggest theoretical or process improvements for future iterations • Write technical reports indicating qualitative and quantitative results to external parties

Benefits

• Competitive health insurance options • 401K plan management • Free lunch and fully-stocked kitchen in our South Bay office • Additional perks: monthly wellness stipend, office set up allowance, company retreats, and more to come as we scale

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