Staff Research Engineer (LLM Pre-Training)

Databricks

P-1504 The Applied AI team at Databricks sits at the forefront of advancing GenAI-powered products. Over the past years, we’ve launched Databricks Assistant, AI/BI Genie, and Agent Bricks working with product teams, and made significant strides in LLM quality for these products. These products are used by 100s of thousands of Databricks users every day. We are tackling challenging problems like code suggestion, error detection and correction, text-to-sql generation, automatic pipeline generation, knowledge QA and many others. As our GenAI products continue to evolve, we are seeking multiple  GenAI Engineers from junior levels to more senior levels to drive the next phase of development. In 2025, we will focus on enhancing LLM quality, expanding GenAI capabilities across Databricks products, and strengthening our platform architecture to enable seamless AI interactions at scale. Key Responsibilities * Shape the direction of our applied AI areas and intelligence features in our products. Drive the development and deployment of state-of-the-art AI models and systems that directly impact the capabilities and performance of Databricks' products and services (e.g., Databricks Assistant and AI/BI Genie). * Develop novel data collection, fine-tuning, and LLM technologies that achieve optimal performance on specific tasks and domains. * Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid experimentation and iteration. * Work closely with cross-functional teams, including AI researchers, ML engineers, and product teams, to deliver impactful AI solutions that enhance user productivity and satisfaction. * Build scalable, reusable backend systems to support GenAI products across the company. Develop robust logging, telemetry, and evaluation harnesses to ensure reliable model performance. What We’re Looking For * 2-8 years of machine learning engineering experience in high-velocity, high-growth companies. Alternatively, a strong background in relevant ML research in academia will be considered as an equivalent qualification. * Strong track record of working with language modeling technologies. This could include the following: Developing generative and embedding techniques, modern model architectures, fine tuning / pre-training datasets, and evaluation benchmarks. * Proficiency in Python, TensorFlow/PyTorch, and scalable ML architectures. * Ability to drive end-to-end model development, from research and prototyping to deployment and monitoring. * Strong analytical and problem-solving skills, with a passion for improving AI-driven user experiences. * Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews and deployment. * Experience with LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) is a bonus. Why Join Us? At Databricks, we are building state-of-the-art AI solutions that redefine how users interact with data and our products. You’ll have the opportunity to shape the future of AI-driven products at Databricks, work with cutting-edge models, and collaborate with a world-class team of AI and ML experts. If you're excited about pushing the boundaries of AI in real-world applications, we’d love to hear from you!   Please note we are open to employees working from our Mountain View, CA office for this position.    Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.   Local Pay Range $190,000—$285,000 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook. Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here. Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

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.

Black Forest Labs

ABOUT BLACK FOREST LABS We're the team behind Latent Diffusion, Stable Diffusion, and FLUX—foundational technologies that changed how the world creates images and video. We’re creating the generative models that power how people make images and video—tools used by millions of creators, developers, and businesses worldwide. Our FLUX models are among the most advanced in the world, and we're just getting started. Headquartered in Freiburg, Germany with a growing presence in San Francisco, we’re scaling fast while staying true to what makes us different: research excellence, open science, and building technology that expands human creativity. WHY THIS ROLE Large-scale training is where research ideas become real, and where many of the hardest problems are no longer cleanly separated into “research” or “engineering.” A promising architecture only matters if we can train it stably, efficiently, and correctly across large GPU fleets. In this role, you will be embedded in production training and help where the hardest systems and performance problems arise: attention performance, custom kernels, low-precision training, profiling, memory behavior, data movement, distributed training stability, and throughput regressions. You will work directly with researchers, but your output will often be code, measurements, kernels, debugging tools, and training-system changes that make better research possible. We are open to a range of seniority for this role. The common thread is deep technical ownership: you should be able to make progress in ambiguous training-system problems, verify your results, and own the outcome. WHAT YOU’LL WORK ON * Improve the performance, reliability, and numerical stability of production training runs for large multimodal generative models * Profile full training steps across model code, attention, kernels, data loading, encoders, communication, optimizer steps, checkpointing, and memory pressure * Implement and validate GPU-level optimizations: fused kernels, attention paths, low-precision matmuls, quantization kernels, CUDA/Triton/CuTe/CUTLASS experiments, and no-compile alternatives where they make sense * Push lower-precision training forward, including FP8 / MXFP8 / FP4-style paths, weight and activation quantization, accumulation choices, convergence risk, and quality tradeoffs against baseline training runs * Work with researchers to translate architecture changes into efficient training implementations, and help distinguish real model-quality progress from changes that only look good in a microbenchmark * Debug distributed training failures: NaNs, loss spikes, silent numerical drift, memory leaks, stragglers, bad nodes, NCCL issues, and throughput cliffs * Build benchmarking and profiling harnesses that make performance claims trustworthy across hardware, shapes, sequence lengths, and training configurations * Help the training team move quickly when an urgent bottleneck appears, while turning repeated failures into better abstractions and tools WHAT WE’RE LOOKING FOR * Experience working deeply on large-scale training systems, ideally as part of a training group working closely with researchers * Strong PyTorch fluency, including comfort reading and modifying low-level training code rather than only using high-level APIs * Experience with distributed training concepts such as FSDP, tensor/model/context/sequence parallelism, activation checkpointing, NCCL, and overlapping compute and communication * Hands-on experience improving training throughput, memory footprint, or stability in real training runs * Experience profiling GPU workloads with tools like Nsight Systems, Nsight Compute, torch profiler, trace viewers, or custom telemetry * Practical GPU performance judgment: you may use modern coding agents and tools as much as you want, but you need the understanding to verify correctness, numerical behavior, and performance, and to own the result * Understanding of low-precision training and quantization tradeoffs: FP8, MXFP8, FP4/NVFP4-style formats, scaling, accumulation, numerical validation, and convergence risk * Good research judgment: you can partner with researchers on ablations, understand what the measurements do and do not prove, and keep optimization work tied to model-quality outcomes * Comfortable operating in ambiguity: sometimes the task is a clean implementation, sometimes it is a production fire, and sometimes it is figuring out which of three plausible explanations is actually true We'd be especially excited if you: * Have supported or co-owned training for a frontier foundation model that shipped or reached a major release * Have written or substantially improved forward/backward GPU kernels, or have shown you can make progress on kernel-level work with strong measurement and validation discipline * Have worked on attention performance, variable sequence length training, non-standard attention patterns * Have experience on Hopper or Blackwell-class GPUs * Have worked on low-precision training * Have experience with diffusion, flow matching, DiT, and multimodal generative model training; if your deepest background is autoregressive or LLM training systems, you are excited to learn the diffusion and multimodal modeling stack quickly * Can move naturally between profiler traces, kernel code, distributed systems failures, and research discussions HOW WE WORK TOGETHER We’re a distributed team with real offices that people actually use. Depending on your role, you’ll either join us in Freiburg or SF at least 2 days a week (or one full week every other week), or work remotely with a monthly in-person week to stay connected. We’ll cover reasonable travel costs to make this possible. We think in-person time matters, and we’ve structured things to make it accessible to all. We’ll discuss what this will look like for the role during our interview process. Everything we do is grounded in four values: * Obsessed. We are a frontier research lab. The science has to be right, the understanding deep, the product beautiful. * Low Ego. The work speaks. The best idea wins, no matter who said it. Credit is shared. Nobody is above any task. * Bold. We take the ambitious bet. We ship, we do not wait for conditions to be perfect. * Kind. People over politics. We treat each other with genuine warmth. Agency without empathy creates chaos. If this sounds like work you’d enjoy, we’d love to hear from you. Base Annual Salary: EU €130,000 - €240,000 + Equity US $180,000 - $290,000 + equity