Machine Learning Engineer

<div class="content-intro"><p><strong>At Inflection AI, our public benefit mission is to harness the power of AI to improve human well-being and productivity.</strong></p> <p>The next era of AI will be defined by agents we trust to act on our behalf.&nbsp;</p> <p>We’re pioneering this future with human-centered AI models that unite emotional intelligence (EQ) and raw intelligence (IQ)—transforming interactions from transactional to relational, to create enduring value for individuals and enterprises alike.</p> <p>Our work comes to life in two ways today:</p> <p><a href="https://pi.ai" target="_blank">Pi, your personal AI</a>, designed to be a kind and supportive companion that elevates everyday life with practical assistance and perspectives.</p> <p><a href="https://developers.inflection.ai" target="_blank">Platform</a> — large-language models (LLMs) and APIs that enable builders, agents, and enterprises to bring Pi-class emotional intelligence into experiences where empathy and human understanding matter most.</p> <p>We are building toward a future of AI agents that earn trust, deepen understanding, and create aligned, long-term value for all.</p></div><h2><strong>About the Role</strong></h2> <p>As a Senior Machine Learning Engineer on the AI Engineering team, you will be a key technical leader responsible for designing and scaling the systems that bring our models from research into reliable, production-grade deployments.</p> <p>You will work at the intersection of large-scale ML systems, low-latency inference, distributed infrastructure, and product integration. Your work will directly impact how intelligence is delivered to millions of users—ensuring performance, reliability, safety, and continuous improvement of our AI systems.</p> <h2><strong>What You’ll Do</strong></h2> <h3><strong>Production ML &amp; Model Serving</strong></h3> <ul> <li>Design and implement scalable, low-latency model-serving infrastructure for large language models and multimodal systems.</li> <li>Build and maintain robust APIs and services to support real-time conversational workloads.</li> <li>Optimize inference systems for throughput, latency, cost-efficiency, and reliability.<br><br></li> </ul> <h3><strong>MLOps &amp; Infrastructure</strong></h3> <ul> <li>Architect and improve end-to-end ML pipelines spanning training, evaluation, deployment, monitoring, and rollback.</li> <li>Develop model lifecycle management systems with strong observability and performance tracking.</li> <li>Partner with infrastructure teams to scale compute resources efficiently across distributed environments.</li> <li>Improve CI/CD workflows and automation for model releases and infrastructure updates.<br><br></li> </ul> <h3><strong>Research-to-Production Enablement</strong></h3> <ul> <li>Collaborate with ML researchers to productionize new model architectures and capabilities.</li> <li>Design abstractions that enable rapid experimentation while preserving safety, quality, and reliability.</li> <li>Implement evaluation frameworks and guardrails to ensure models meet performance and safety standards before deployment.<br><br></li> </ul> <h3><strong>Data &amp; Feedback Systems</strong></h3> <ul> <li>Define data requirements and feedback loops to enable continuous model improvement.</li> <li>Partner with product and safety teams to integrate telemetry, evaluation signals, and user feedback into training pipelines.</li> <li>Ensure high-quality data ingestion and metadata tracking for ML readiness.<br><br></li> </ul> <h3><strong>Architecture &amp; Technical Leadership</strong></h3> <ul> <li>Lead architectural decisions that balance performance, scalability, safety, and maintainability.</li> <li>Contribute to code reviews and engineering best practices across the team.</li> <li>Mentor engineers and raise the bar for production ML excellence.</li> <li>Help shape long-term technical strategy for deploying AI systems at global scale.<br><br></li> </ul> <h2><strong>What We’re Looking For</strong></h2> <h3><strong>Required Qualifications</strong></h3> <ul> <li>1-4 years of experience in machine learning engineering, backend systems, or distributed infrastructure.</li> <li>Proven experience deploying and operating ML models in production environments.</li> <li>Strong programming skills in Python and/or C++ (or equivalent systems language).</li> <li>Experience with large-scale model serving (LLMs, transformers, or similar architectures).</li> <li>Deep understanding of distributed systems, API design, and cloud infrastructure.</li> <li>Experience with MLOps tools and workflows (CI/CD, model monitoring, experiment tracking).<br><br></li> </ul> <h3><strong>Preferred Qualifications</strong></h3> <ul> <li>Experience scaling high-throughput, low-latency inference systems.</li> <li>Familiarity with GPU acceleration, model optimization (quantization, batching, caching), and performance tuning.</li> <li>Experience working with conversational AI systems or real-time user-facing AI products.</li> <li>Knowledge of ML evaluation methodologies, safety systems, and guardrail design.</li> <li>Background collaborating closely with research teams in fast-paced AI environments.<br><br></li> </ul> <h2><strong>Employee Pay Disclosures</strong></h2> <p>At Inflection AI, we aim to attract and retain the best employees and compensate them in a way that appropriately and fairly values their individual contributions to the company. For this role, Inflection AI estimates a starting annual base salary to fall within the range of <strong>$172,000.00 to $250,000.00</strong>, depending on a candidate’s qualifications and level of experience.&nbsp;This role also includes a meaningful equity component, allowing employees to share in the long-term success of the company.<br><br></p> <h3><strong>Benefits</strong></h3> <p>Inflection AI values and supports our team’s mental and physical health. We are focused on building a positive, safe, inclusive and inspiring place to work. Our benefits include:&nbsp;</p> <ul> <li>Diverse medical, dental and vision options&nbsp;</li> <li>401k matching program&nbsp;</li> <li>Unlimited paid time off&nbsp;</li> <li>Parental leave and flexibility for all parents and caregivers</li> <li>Support of country-specific visa needs for international employees living in the Bay Area</li> </ul>