Model Behavior Engineer Jobs

Manager, Customer Success As a Manager, Customer Success, you will hire and develop teams, coaching Customer Success Managers (CSMs) to success. This role impacts our customer experience and retention at scale, providing an opportunity to join a company that’s a leader in the space and see your contributions firsthand. At Datadog, we value our office culture—the relationships and collaboration it builds, and the creativity it inspires. We operate as a hybrid workplace to help our Datadogs create a work-life harmony that best fits them. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You’ll Do: Drive and influence the full renewal and expansion sales cycle Serve as an advisor and role model for exemplary customer service Balance autonomy with effective communication upwards Act as a regional/segment owner and escalation point for relevant matters Motivate CSMs to foster customer relationships that build trust and achieve consistent revenue growth Collaborate cross-functionally to drive positive customer outcomes and solve complex challenges Focus on talent attraction, retention, and development to create high-performance teams Who You Are: Experienced in Customer Success or Account Management, with 6+ years of experience preferred and at least 3 years in the SaaS space 3+ years of experience leading a high-performing team of Customer Success Managers A proven track record of success in negotiating contract terms and overcoming pricing objections for renewal and upsell opportunities Familiar with the B2B tech or subscription-based software space Comfortable in a fast-paced, scaling environment Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology a

About the Team OpenAI's Industrial Compute organization is building the world's most advanced AI infrastructure ecosystem. In partnership with leading cloud providers, hardware manufacturers, utilities, construction partners, and internal engineering organizations, we are delivering hyperscale AI campuses that power the next generation of frontier AI models. Infrastructure Delivery Operations sits at the center of this effort. Our team develops the operating model that connects infrastructure strategy, supply planning, manufacturing operations, and delivery into a single, integrated system that enables OpenAI to deploy AI infrastructure predictably at scale. We partner across Hardware Engineering, Network Engineering, Capacity Delivery, Hardware Operations, Security, Finance, Strategic Sourcing, and external infrastructure partners to create a single, integrated view of program health. Through governance, operational analytics, executive reporting, and scalable operating mechanisms, we enable leaders to proactively manage risk, optimize capacity, and deliver infrastructure predictably at Industrial Compute speed. About the Role We are seeking a Technical Program Manager, Infrastructure Delivery Operations to drive integrated strategy and delivery across OpenAI's rapidly expanding AI infrastructure portfolio. This role sits at the intersection of infrastructure strategy, New Product Introduction (NPI), supply planning, manufacturing operations, and infrastructure delivery. You will lead highly cross-functional programs spanning engineering, supply planning, manufacturing, logistics, construction, commissioning, and operations, ensuring technical and operational dependencies remain synchronized from planning through production readiness. Beyond driving program execution, you will leverage operational insights to improve capacity planning, infrastructure strategy, and deployment readiness. You will also help operationalize new technologies and suppliers by partnering w

The objective of the job is to handle project co-ordination with different EPCM- contractors, resolve technical queries, as per project specifications. Plan and track deliverables as per project planning. Knowledge of plant layout and piping concepts of stress analysis, optimize layout, contribute during model reviews, understanding of codes and standards, skill set of 3D-Model. Source: Adani Group | Job ID: 41654

The Predictive Analytics Data Scientist is responsible for designing, building, and optimizing AI-driven predictive models that enhance business intelligence, risk management, and operational efficiency. This role will focus on end-to-end execution, from data collection and model development to deployment and monitoring, ensuring AI models drive actionable insights for demand forecasting, process automation, and business optimization. Source: Adani Group | Job ID: 44281

The Predictive Analytics Data Scientist is responsible for designing, building, and optimizing AI-driven predictive models that enhance business intelligence, risk management, and operational efficiency. This role will focus on end-to-end execution, from data collection and model development to deployment and monitoring, ensuring AI models drive actionable insights for demand forecasting, process automation, and business optimization. Source: Adani Group | Job ID: 44282

About the Team Our mission at OpenAI is to discover and enact the path to safe, beneficial AGI. To do this, we believe that many technical breakthroughs are needed in generative modeling, reinforcement learning, large-scale optimization, active learning, and other areas. The team builds the performance-critical systems that allow OpenAI's models to run efficiently across a diverse set of AI accelerators. We work across the inference stack, from low-level kernels and compilers through model execution, to unlock the full capabilities of the underlying hardware. About the Role As a Software Engineer, Trainium, you will help bring OpenAI's inference workloads to AWS Trainium and build the software stack required to run cutting-edge frontier models efficiently on the platform. This is a deeply technical, cross-stack role spanning kernels, compilers, and model execution. You will work on the systems needed to support OpenAI's inference stack on Trainium, including developing and optimizing high-performance kernels, improving compiler support, and enabling efficient execution of the model forward pass. You'll work closely with engineers across inference, compilers, kernels, and ML systems to identify performance bottlenecks and build the software needed to take full advantage of Trainium. The work may range from low-level hardware-specific optimization to compiler and runtime improvements to integrating new model architectures into the inference stack. If you enjoy working at the intersection of ML systems, compilers, kernels, and accelerator hardware, this role is for you. We're looking for engineers who are self-directed, comfortable operating across abstraction layers, and excited to solve challenging performance problems for frontier-scale AI systems. In This Role, You Will Build and optimize OpenAI's inference stack for AWS Trainium. Develop high-performance kernels for critical model operations and workloads. Extend and improve compiler support to efficiently target

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. You will: Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the modern

About BlockTech BlockTech is a fast-paced algorithmic trading firm at the frontier of global cryptocurrency derivatives and spot markets. We trade 24/7 across some of the most data-rich, fast-moving venues in finance, and we use that data to build smarter models, sharper signals, and more adaptive systems. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL couldn’t be shorter. We’re growing fast, and we’re looking for a Quantitative Researcher with a strong machine learning toolkit to help us push that edge further. The role As a Quantitative Researcher on our trading floor, you’ll own ideas end-to-end from hypothesis and dataset construction through feature engineering, model training and backtesting, all the way to live deployment, monitoring, and iterative improvement. You’ll sit shoulder-to-shoulder with Quantitative Traders and Quantitative Analysts, and your work will directly drive how we price and trade. What you’ll work on Collaborating closely with traders to translate research insights into systematic trading strategies Designing, developing and deploying models for price prediction, signal generation, execution, and anomaly detection across crypto derivatives and spot markets using state-of-the-art AI and ML techniques. Building robust trading, backtesting and research infrastructure alongside our engineers, so promising ideas can move into production quickly and safely Owning models in production: monitoring live performance, diagnosing decay, and iterating on what you ship What we’re looking for A strong academic background in a quantitative discipline (Mathematics, Physics, Statistics, Computer Science, Econometrics, ML/AI, or similar), typically a PhD or an MSc with strong research experience Fluency in Python and the modern ML stack (PyTorch and/or TensorFlow, scikit-learn, NumPy, pandas) A deep, intuit

About BlockTech BlockTech is a fast-paced algorithmic trading firm at the frontier of global cryptocurrency derivatives and spot markets. We trade 24/7 across some of the most data-rich, fast-moving venues in finance, and we use that data to build smarter models, sharper signals, and more adaptive systems. Crypto is one of the few markets where a researcher can still meaningfully move the edge. The data is abundant, the microstructure is novel, and the feedback loop between a research idea and live PnL couldn’t be shorter. We’re growing fast, and we’re looking for a Quantitative Researcher with a strong machine learning toolkit to help us push that edge further. The role As a Quantitative Researcher on our trading floor, you’ll own ideas end-to-end from hypothesis and dataset construction through feature engineering, model training and backtesting, all the way to live deployment, monitoring, and iterative improvement. You’ll sit shoulder-to-shoulder with Quantitative Traders and Quantitative Analysts, and your work will directly drive how we price and trade. You will: Collaborate closely with traders to translate research insights into systematic trading strategies Design, develop and deploy models for price prediction, signal generation, execution, and anomaly detection across crypto derivatives and spot markets using state-of-the-art AI and ML techniques. Build robust trading, backtesting and research infrastructure alongside our engineers, so promising ideas can move into production quickly and safely Own models in production: monitoring live performance, diagnosing decay, and iterating on what you ship What we're looking for A strong academic background in a quantitative discipline (Mathematics, Physics, Statistics, Computer Science, Econometrics, ML/AI, or similar), typically a PhD or an MSc with strong research experience Fluency in Python and the modern ML stack (PyTorch and/or TensorFlow, scikit-learn, NumPy, pandas) A deep, intuitive grasp of overfitting, g

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto is one of the few markets where the gap between a good model and live PnL comes down to how fast and how reliably you can ship it. Abundant data, novel microstructure, and a short path to production mean the quality of our infrastructure directly moves the edge. We're looking for a Quantitative Developer to build that infrastructure and get our models into production. The role This is a hands-on engineering seat on our trading floor. You'll own the infrastructure that turns research models into production trading systems, working primarily in Python and Rust, the language our models and systems are written in. You'll build the frameworks and tooling that let quants ship their code to production and keep those systems running once they're live. You'll work shoulder-to-shoulder with researchers, traders, and fellow engineers, building the tooling, pipelines, and standards that let good ideas reach production quickly and safely, and helping raise the engineering bar across the floor. You will Build and own the infrastructure that takes models from research to production - data pipelines, backtesting, deployment, and monitoring. Turn research prototypes into robust, performant, production-grade code that runs reliably in live trading Work closely with quantitative researchers and traders to get new models live quickly and safely Own the systems you ship in production: monitor performance, diagnose issues, and keep latency and reliability high Improve the tools, standards, and pipelines the team relies on, raising the engineering bar across the floor What we're looking for 2+ years of software engineering experience, with a track record of shipping production-grade systems A strong academic foundation in a STEM discipline (computer science, mathematics, p

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. What you'll do Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the m

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Why This Role? North is Cohere’s AI workspace platform for enterprises: a secure, customizable environment where companies can use AI across their real workflows while maintaining control over sensitive data. North connects AI agents with workplace tools, applications, and business context, helping users delegate complex work, build automations, inspect outputs, and collaborate with AI in production environments. As North becomes more capable, one of the most important questions is also one of the hardest: how do we know whether the model is actually getting better for the workflows customers care about? This role is about being the voice of North inside modelling. You will build the evaluation systems, feedback loops, and applied modelling workflows that make sure model progress translates into better product outcomes for North users. You will work closely with North product teams, customer-facing teams, and modelling teams to define what “good” means across the product surface, turn real usage and product direction into high-quality evals, and use those evals to guide model selection, patches, and regular model updates. This i

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F , led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to to ship AI products. THE ROLE Forward Deployed Engineers work directly with the largest and fastest-growing AI companies in the world, owning their technical outcomes on Baseten and taking on the hardest problems in serving and improving models at scale. The work spans the model lifecycle: inference, post-training, and the systems that tighten the loop between them. Act as each account's de facto CTO on Baseten, with final accountability for how their workloads are designed, run, and scaled. Take customer objectives from vague to shipped: frame the problem, define the spec and success criteria, build the PoC, and carry it through to production quickly, using the right tools for the problem. Design the evals and benchmarks that isolate where quality or performance falls short, then close the gap yourself, whether that means optimizing inference, improving the model through post-training, or reworking the eval itself. Be the first responder to mission-critical failures including triage, owning the fix directly or route to the owning team and stay accountable until it ships. Build internal systems so that each engagement is faster than the last. This includes tooling and automation for eval and deployment infrastructure, and the recipes and reference implementations that make the product more self-serve. Shape the product itself, channeling what your accounts need into the roadmap and shipping fixes and features into Baseten's codebase yourse

GTM Operations & Strategy at MongoDB is a global team of builders and innovators focused on unleashing MongoDB’s sales greatness by pairing world‑class analytics with scalable operations. Within GTM Operations, the GTM Intelligence – Applied Science team turns complex GTM data into tools, models, and insights that help our sales organization make better, faster decisions across our people, segmentation, territory design, forecasting, and account prioritization. As a Senior Analyst on the Applied Science team, you will own high‑impact analytical workstreams end‑to‑end: from problem framing with senior GTM stakeholders, to data engineering and model design, through to productionalized workflows, dashboards, and executive‑ready narratives that drive concrete changes in the field. This role is based in Dublin, Ireland and supports a global stakeholder set across regions and GTM functions. We are looking to speak to candidates who are based in Dublin or Cork for our hybrid working model. What You’ll Do Translate GTM questions into analytical projects Partner with GTM Ops, Sales Strategy & Planning, Sales Leadership, and Central Analytics to scope problems, define success criteria, and prioritize work across areas like segmentation, territory design, account prioritization, and pipeline/forecast health. Structure ambiguous questions into hypotheses, analytical plans, and clear recommendations for senior stakeholders (SVPs, RVPs, functional leaders). Design and build scalable analytics & models Develop and maintain statistical and machine learning models (e.g., NARR prediction, deal qualification, account momentum, workload identification) that inform forecast expectations, territory assignments, and deal prioritization. Engineer robust data pipelines and features (SQL/Python) on top of our GTM data stack (Salesforce, product usage, call transcripts, marketing signals, etc.) in partnership with data and platform teams. Own core GTM analytics assets Contribute t

The HR Shared Services Compensation operations senior associate combines analytical rigour with a service-orientated mindset. They will be comfortable working in the details of HR systems and cases while also understanding the broader impact of compensation data and decisions on employees, managers, payroll, reporting, and business operations. We are looking to speak to candidates who are based in Gurugram for our hybrid working model. Key Responsibilities You will play a vital role in processing the backend transactions related to general compensation questions or tasks, along with making sure that the relevant SLA’s are met Review and process requests related to job architecture, job codes, career levels, compensation tiers, pay ranges, compa-ratio, salary positioning, bonus eligibility, and other compensation-related fields in the HRIS Pull ad hoc compensation reports, review data for accuracy and completeness, and help summarize basic findings for the compensation team and business partners Assist with program documentation, supporting records, data integrity, audit readiness, and compliance materials Partner with Compensation, HR Business Partners, Recruiting, Payroll, HRIS, Finance, and regional People Teams to investigate issues, resolve discrepancies, and coordinate end-to-end completion of transactions Adhere to and demonstrate high proficiency in agreed critical metrics & SLAs Create, maintain, and improve standard operating procedures, knowledge articles, checklists, process maps, and case-handling guidance for compensation processes Perform data validation, reconciliation, and quality-control checks across SuccessFactors, case-management tools, payroll inputs, compensation planning files, and downstream reporting systems Expected Skills Experience for 4+ years in HR Operations / Shared Services in an HR Shared Services role (EMEA and Americas) Strong Excel or spreadsheet skills, including data validation, lookups, filters, pivot tables, reconciliatio