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Aerospace • Information Technology • Software • Cybersecurity • Design • Defense • Manufacturing
Serve as Engineering Application Captain for PCAS: capture customer requirements, develop change-request requirements, coordinate releases, lead RAM/kickoff/pre-lab meetings, coordinate SMEs, maintain application database, oversee testing/validation, and administer Teamcenter lab installations.
Top Skills:
Teamcenter
Artificial Intelligence • Big Data • Cloud • Machine Learning • Software • Business Intelligence • Data Privacy
The role involves designing cloud infrastructure, managing production Kubernetes clusters, optimizing CI/CD pipelines, enhancing developer experience, and ensuring reliable AI workloads. Candidates should have extensive experience in infrastructure and distributed systems engineering with strong coding skills and cloud expertise.
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AWSAzureDatadogDockerElkGCPGoGrafanaJavaKubernetesPrometheusPythonTerraform
Fintech • Mobile • Software • Financial Services
Lead Capital Markets product development by partnering with business lines, coordinating cross-functional launches, educating investors, negotiating term sheets, modeling product economics, and ensuring scalable infrastructure and reporting. Manage investor pipelines, validate readiness for regulatory and investor reporting, oversee execution and post-launch performance, and build repeatable frameworks to streamline future product development.
Top Skills:
ExcelGoogle Suite
We are expanding our team of motivated technologists with a proven track record of delivering results in technology consulting. We are looking for a Machine Learning Architect with experience in cloud (AWS preferred) who is passionate about helping customers build AI/ML solutions at scale. Being an experienced technologist with technical depth and breadth, aided with strong interpersonal skills, you will work directly with customers as part of a delivery team, helping to enable innovation by creating state of the art Machine Learning solutions that align to business goals.
This role includes responsibilities both as a Professional Services Machine Learning Architect and as a hands-on Machine Learning engineer on customer engagements.
The qualified Machine Learning Architect will have demonstrated the ability to think strategically about businesses, create technical definitions around customer objectives in complex situations, develop solution strategies, motivate & mobilize resources, and deliver results. The ability to connect technology with measurable business value is a critical component to be successful in this role. We seek team members who are self-motivated, driven, collaborative, passionate about machine learning, and want to have a direct positive impact on our customer's business. Strong communication skills and emotional intelligence are also needed to help develop a team that works with you.
Work Location: Remote
Key Responsibilities:
- Design machine learning solutions and execute machine learning projects end to end from proof-of-concept stage to deployment in production using cloud native technologies and state of the art machine learning models.
- Be technically focused but work directly with the business representatives/customers to understand the requirements driving the need for a solution to be developed.
- Be responsible for all phases of the project from problem definition, data annotation, model development, model deployment to end user documentation/training.
- Design the architecture of ML solutions on cloud platforms (AWS, Azure, GCP) including MLOPs.
- Stay abreast of the latest developments. Read the latest published machine learning research and adapt the models to solve customer’s problems.
- Establish credibility by demonstrating technical excellence and delivering value through solutions you build. Develop strong relationships with our customers.
Qualifications:
- Masters with 10+ years of experience or PhD with 6+ years of experience in Machine Learning, Natural Language Processing (NLP) and Deep Learning.
- Minimum 5+ years of experience architecting and building Machine Learning solutions.
- Minimum 5+ years of experience with cloud platforms (AWS, GCP, Azure).
- Experience building ML models and strong knowledge of ML techniques is required.
- Experience with hugging face, TensorFlow/pytorch, transformer architectures, prompt engineering, agentic systems, LLMs.
- Strong coding experience in Python and architectural patterns like microservices.
- Solid understanding of agile methodologies and experience in planning machine learning projects from inception to production deployment.
- Strong problem-solving skills and the ability to lead a team on “what’s next” when encountering a technical issue in a machine learning project.
- Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
Travel:
- As per business requirements
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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
