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About Inflection AI Inflection AI is a Public Benefit Corporation empowering people with human-centered, emotionally intelligent AI. We’re shaping the future of AI by combining emotional intelligence (EQ) and raw intelligence (IQ) to elevate people’s potential. Inflection AI created Pi, the world’s first emotionally intelligent AI, to help people work through decisions, emotions, and challenges. Pi is a personal AI agent powered by Inflection AI’s foundation model, proving that AI can be personal, empathetic, and contextually aware. About the Role Inflection’s models are central to our product and platform strategy, and we are looking for a hands-on technical leader to own the model-improvement loop from data and training through evals, post-training, release criteria, and production feedback. This person will sit at the intersection of research, production engineering, and model release, with a mandate to ship models that are measurably better for users. The ideal candidate has led serious model training or post-training work before, can make principled tradeoffs across data, compute, architecture, and quality, around a clear technical roadmap. What You’ll Do Own the model-improvement roadmap across capability, reliability, emotional intelligence, tool use, safety, latency, cost, and enterprise readiness. Lead training and post-training strategy, including supervised fine-tuning, RLHF, DPO, GRPO, RLAIF, reward modeling, preference optimization, tool-use fine-tuning, distillation, synthetic data, and related methods. Drive model architecture and optimization decisions across modern transformer-based and hybrid architectures, including both training-time and inference-time performance. Lead large-scale training efforts on distributed GPU clusters, including systems operating at the scale of 1,000+ GPUs. Define and execute data strategy across data curation, mixture design, deduplication, decontamination, human-in-the-loop pipelines, preference data, evaluation data, synthetic data, and production feedback loops. Build and improve evaluation and release-quality systems, including model evals, quality gates, regression detection, release criteria, model-readiness reviews, and post-release monitoring. Partner closely with infrastructure and research engineering teams to improve distributed training reliability, checkpointing, fault tolerance, observability, reproducibility, and cost-performance tradeoffs. Debug and improve model behavior across the full stack: data, training, post-training, evaluation, infrastructure, product integration, and production feedback. What We’re Looking For Experience leading, or serving as a principal contributor to, large-scale LLM, multimodal, or foundation-model training or post-training programs. Deep experience with transformer-based models, hybrid architectures, modern deep-learning frameworks, and distributed training systems. Strong practical experience with post-training and alignment methods such as SFT, RLHF, DPO, GRPO, RLAIF, reward modeling, preference optimization, tool-use fine-tuning, or related approaches. Experience operating or partnering on large-scale training infrastructure, ideally including GPU clusters at the scale of 1,000+ GPUs. Strong systems instincts around throughput, cost, reliability, observability, debugging, checkpointing, reproducibility, and fault tolerance. Excellent judgment around data quality, evaluation design, model regressions, release readiness, and production model behavior. Ability to balance research ambition with product pragmatism, user impact, and operational discipline. Experience leading senior technical teams while continuing to contribute directly to technical decisions and implementation. PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field, or equivalent practical experience. Employee Pay Disclosures 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 $400,000 to $550,000 , depending on a candidate’s qualifications and level of experience. This role also includes a meaningful equity component, allowing employees to share in the long-term success of the company. Benefits Inflection AI values and supports our team’s mental, emotional, financial and physical health. We are focused on building a positive, safe, inclusive and inspiring place to work. Our benefits include: Robust medical, dental and vision options with employer contributions for HSA, FSA and DFSA 401k matching program Flexible Time Off, 10 paid holidays, 5 days sick leave Parental, Medical and Family care leave Generous cell-phone, wellness and office set up stipends Support of country-specific visa needs for international employees living in the Bay Area
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.