The Senior Machine Learning Engineer will design, implement, and deploy ML models, contribute to engineering standards, and mentor junior engineers.
About Hive
Hive is the leading provider of cloud-based AI solutions to understand, search, and generate content, and is trusted by hundreds of the world's largest and most innovative organizations. The company empowers developers with a portfolio of best-in-class, pre-trained AI models, serving billions of customer API requests every month. Hive also offers turnkey software applications powered by proprietary AI models and datasets, enabling breakthrough use cases across industries. Together, Hive’s solutions are transforming content moderation, brand protection, sponsorship measurement, context-based ad targeting, and more.
Hive has raised over $120M in capital from leading investors, including General Catalyst, 8VC, Glynn Capital, Bain & Company, Visa Ventures, and others. We have over 250 employees globally in our San Francisco, Seattle, and Delhi offices. Please reach out if you are interested in joining the future of AI!
Senior Machine Learning Engineer
In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at the forefront of deep learning technology, prototyping state-of-the-art neural net models and launching these models into production. We value hard workers who have no qualms working with terabyte-scale datasets, who are interested in learning new technologies at all levels of the machine learning stack, and who move fast and take ownership of their projects. Our ideal candidate has experience creating a working machine learning-powered project from the ground up, contributes innovative ideas and ingenious implementations to the team, and is capable of planning out scalable, maintainable data pipelines.
Responsibilities
- Everything involved in applying a ML model to a production use case, including, designing and coding up the neural network, gathering and refining data, training and tuning the model, deploying it at scale with high throughput and uptime, and analyzing the results in the wild in order to continuously update and improve accuracy and speed
- Write and maintain scalable, performant and secure code that can be shared across platforms
- Meaningfully contribute to the product and core backend systems by suggesting and executing improvements
- Improve engineering standards, tooling, processes and security
- Develop novel, accurate, and performant ML algorithms for use at scale
- Conduct metric-driven research experiments to improve model performance
- Provide mentorship to and help onboard junior ML engineers
- Collaborate cross-functionally with other teams
- Utilize OWASP top 10 techniques to secure code from vulnerabilities
- Maintain awareness of industry best practices for data maintenance handling as it relates to your role
- Adhere to policies, guidelines and procedures pertaining to the protection of information assets
- Report actual or suspected security and/or policy violations/breaches to an appropriate authority
Requirements
- You have a Bachelor's Degree in computer science or a related field
- You have a minimum of 5 years of building production scale ML models
- You know the ins and outs modern machine learning frameworks, such as PyTorch or Tensorflow
- You are an expert in scripting languages such as Python and/or shell scripts, particularly for data analysis
- You have experience writing code and training across distributed systems
- You have an ability to understand and make well-reasoned tradeoffs in designing features
- You can lead end to end development of new products
- You are very knowledgeable in at least one focus area of machine learning, such as computer vision or NLP
- You strongly believe in high code quality, automated testing, and other engineering best practices
- You have attention to detail and a passion for correctness
- You are comfortable with ambiguity and scoping solutions with your teammates
- You have strong interpersonal and communication skills with a bias towards action
Who We Are
We are a group of ambitious individuals who are passionate about creating a revolutionary AI company. At Hive, you will have a steep learning curve and an opportunity to contribute to one of the fastest growing AI start-ups in San Francisco. The work you do here will have a noticeable and direct impact on the development of the company.
Thank you for your interest in Hive and we hope to meet you soon!
The current expected base salary for this position ranges from $160,000 - $250,000. Actual compensation may vary depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the total compensation package that is provided to compensate and recognize employees for their work; stock options may be offered in addition to the range provided here.
Similar Jobs
Cloud • Information Technology • Productivity • Security • Software • App development • Automation
Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.
Top Skills:
Agentic SystemsSparkAWSDatabricksDeep LearningEmbeddingsInformation RetrievalJavaKotlinLarge Language ModelsNlpPythonRagRankingRecommendationsSQLTypescript
Artificial Intelligence • Cloud • Computer Vision • Hardware • Internet of Things • Software
Build and operate production machine learning systems for Safety AI, including low-latency APIs, data pipelines, model serving, evaluation, monitoring, and rollout infrastructure. Process large-scale camera and telematics data, optimize cloud and edge-to-cloud execution, track model performance and drift, and partner with applied scientists, firmware engineers, platform teams, and product managers to deliver reliable safety features.
Top Skills:
SparkAWSAzureC++Ci/CdComputer VisionDockerGCPGoGpusGrafanaInfrastructure As CodeJavaKubernetesMachine LearningMlflowPythonPyTorchRayRay ServeScala
Artificial Intelligence • Cloud • HR Tech • Information Technology • Productivity • Software • Automation
Build and operate production machine-learning components for a novel exploitability engine that ranks security work by realistic attack paths. Responsibilities include evidence ingestion, entity resolution, graph construction, probabilistic scoring, testing, evaluation, observability, and data/model reliability. The role involves prototyping AI capabilities, collaborating with product and security teams, contributing to architecture and code reviews, and applying AI safety, security, and guardrails.
Top Skills:
Ai AgentsAi EvaluationAi ObservabilityAPIsCloud-Native TechnologiesDatabasesDistributed SystemsEmbeddingsGoGraph SystemsJavaLlmsPythonRagTypescriptVector Search
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
