Senior Machine Learning Engineer (Inference Platform)

Wizard

THE ROLE We’re looking for a Senior Software Engineer to build and scale the backend systems that power our AI agent. This role sits at the intersection of backend engineering, machine learning, and product, and is focused on turning AI capabilities into reliable, production-ready systems. You won’t be training models, but you will make them work in the real world. You’ll build APIs, services, and data systems that connect LLMs and ML models to user-facing experiences, ensuring performance, reliability, and scalability. WHAT YOU’LL DO * Design and build APIs and backend services that power AI-driven product experiences * Develop systems that integrate LLMs and ML models into production workflows * Build and maintain data pipelines supporting training, inference, and evaluation * Partner closely with ML Engineers, Data Scientists, and Product to ship end-to-end features * Improve system performance, reliability, and scalability across services * Contribute to experimentation and feedback loops that improve model and product performance * Debug complex production issues and drive root cause resolution * Raise the bar on code quality, system design, and engineering standards WHAT SUCCESS LOOKS LIKE * AI-powered features are reliably delivered through scalable, well-architected backend systems * ML and LLM capabilities are seamlessly integrated into product experiences with strong performance and uptime * Clear, maintainable APIs and services enable fast iteration across engineering and product teams * Systems are designed with strong observability, enabling rapid debugging and improvement * Engineering decisions consistently balance speed, quality, and long-term scalability IDEAL BACKGROUND * 5+ years of experience in software engineering, with strong backend focus * Strong proficiency in Python and experience building production-grade systems * Experience designing APIs and service-oriented architectures * Experience working with ML/AI systems in production environments (LLMs, ranking, recommendations, or similar) * Familiarity with databases (SQL and/or NoSQL) and data-intensive systems * Experience with cloud platforms (AWS, GCP, or Azure) and modern infrastructure * Exposure to containerization and orchestration (Docker, Kubernetes) * Ability to operate in ambiguity and take ownership of loosely defined problems * Strong product mindset with focus on real user outcomes * Clear communication and ability to collaborate across engineering, ML, and product COMPENSATION & BENEFITS The expected base salary range for this role is $200,000–$225,000 USD and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities. In addition to base salary, Wizard offers: * Equity in the form of stock options * Medical, dental, and vision coverage * 401(k) plan * Flexible PTO and company holidays * Fully remote work within the United States * Periodic company offsites and team gatherings Wizard is committed to fair, transparent, and competitive compensation practices.

Wizard

ABOUT WIZARD Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust. THE ROLE We’re looking for an Applied Scientist to own how we measure, understand, and improve the accuracy of our AI agent. This role sits at the intersection of applied ML, evaluation science, and product. You’ll define what “good” looks like for our agent, build the systems to measure it, and lead the science work to improve it, including fine-tuning the LLM judges that power our evaluation pipeline. You’ll partner with ML Engineering and AI Engineering. What you will do is bring scientific rigor to the most important question at Wizard: is our agent getting better, and how do we know? This is a foundational hire on our science team. Evaluation is the starting point, and the role is scoped to grow into broader applied science work as the surface area of the agent expands (recommendations, personalization, ranking, multimodal, conversational understanding). WHAT YOU’LL DO * Define and evolve accuracy metrics across the full shopping experience (retrieval, ranking, recommendations, outcomes) * Design and run experiments to measure improvements and regressions * Build and maintain evaluation datasets, benchmarks, and scoring frameworks * Improve the LLM judges that power our evaluation pipeline: prompting, calibration, and fine-tuning where it matters * Translate ambiguous product questions into clear, measurable hypotheses and analysis * Partner with ML Engineers to validate model changes and guide iteration * Identify failure modes and edge cases, and drive improvements through data * Make agent performance visible, trusted, and actionable across product and engineering FIRST 3 MONTHS * Go deep on the agent, the current eval pipeline, and the metrics we use today * Audit existing accuracy metrics and benchmarks; identify gaps, blind spots, and signals that aren’t trustworthy * Build relationships with ML, AI Engineering, and Product * Ship one quick win: a missing benchmark, an improved metric, or a fix to a misleading signal * Establish a baseline view of agent performance the team can rally around MONTHS 3 TO 6 * Own the evaluation framework: datasets, metrics, scoring, reporting, both offline and online * Drive measurable improvements to LLM judge quality (calibration, fine-tuning where appropriate) * Run experiments that influence at least one significant model or product change * Stand up automated evaluation the team trusts before and after every launch * Build dashboards and reporting that make agent performance legible to leadership BEYOND 6 MONTHS * Lead applied science work on the next frontier as the agent grows: multi-turn evaluation, multimodal, personalization, ranking quality, conversational understanding * Influence team-level strategy on what we measure, what we improve, and why * Mentor and help grow the science function as it expands WHAT SUCCESS LOOKS LIKE * Clear, trusted accuracy metrics are consistently used across product and engineering * A robust automated evaluation framework for both offline and live experiments * Model and product changes are consistently measured before and after launch * Demonstrable improvements in LLM judge quality and eval coverage * Science leadership that informs what we build, not just whether it works CAREER GROWTH * Depth track: become the org’s authority on AI evaluation: eval strategy, judge models, agent benchmarking * Breadth track: expand into other applied science problems (recommendations, personalization, ranking, multimodal, conversational understanding) as those areas come online * Leadership track: Senior / Staff Applied Scientist, with technical leadership across the science function * As the agent gets more capable, the science problems get richer IDEAL BACKGROUND * 5+ years in Applied ML, AI Research, or Applied Science (PhD or equivalent depth strongly preferred) * Hands-on experience evaluating modern AI/ML systems: LLMs, agents, ranking, or recommendations * Direct experience with LLM-based systems: judge models, RAG, prompt engineering, fine-tuning, RLHF, or similar * Strong experimentation foundations: A/B testing, causal inference, statistical rigor * Proven ability to operate in ambiguity: defining problems, not just solving pre-defined ones * Clear, structured communication that influences across ML, engineering, and product COMPENSATION & BENEFITS The expected base salary range for this role is $225,000 - $280,000 USD, and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities. In addition to base salary, Wizard offers: * Equity in the form of stock options * Medical, dental, and vision coverage * 401(k) plan * Flexible PTO and company holidays * Fully remote work within the United States * Periodic company offsites and team gatherings Wizard is committed to fair, transparent, and competitive compensation practices.

Isomorphic Labs

Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease. The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery.  Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture. The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you.    ABOUT ISO Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed. Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases. We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design. Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI.   Your Impact We are building the largest foundation models in biotech and applying them immediately to cure disease. You will play a pivotal role in ensuring the reliability and scalability of the foundations that make this possible. As a Principal Engineer, you will lead the efforts to harden our systems, ensuring our groundbreaking AI is built on an unshakeable base, working closely with the research team and the Applied ML teams to ensure the infrastructure is stable, reliable and can operate with more data and larger models as we grow.  What You Will Do * Develop and operate our inference platform, serving fleets of cutting-edge machine learning models to scientific applications. * Design strategy and build roadmaps for maturing the platform. * Architect and optimize our next-generation inference services. You will solve core scaling limits, ensuring high-throughput performance and feature parity across our model serving stack. * Contribute to core technical decisions on tooling and architectural design while partnering with science, product, and operations teams to align infrastructure with biotech R&D cycles. * Deliver high-quality and well-tested user-focused features. Skills and Qualifications Essential: * Proven experience in architecting and managing large-scale AI/ML workloads in a production environment. * Expertise in cloud compute design, specifically within Google Cloud Platform (GCP). * Orchestration: Significant experience deploying and managing complex workloads within Kubernetes (GKE). * Professional familiarity with NVIDIA GPU generations and the intricacies of high-performance compute. * Strong programming skills and a "reliability-first" approach to software development. Nice to Have: * A career history that spans both ML Software Engineering and Infrastructure SRE roles. * Experience leading multi-disciplinary projects and navigating complex stakeholder requirements in a fast-paced environment. * Familiarity with workload scheduling, ML efficiency research, and hardware benchmarking. * Experience with Google TPU generations and specialized ML-driven R&D cycles. CULTURE AND VALUES We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it.  Thoughtful Thoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day. Brave Brave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less. Determined Determined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we. Together Together at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere. CREATING AN EXTRAORDINARY COMPANY We believe that to be successful we need a team with a range of skills and talents. We're building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. HYBRID WORKING It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call. Please note that when you submit an application, your data will be processed in line with our privacy policy. >> Click to view other open roles at Isomorphic Labs