The ideal candidate will contribute to alpha research, signal development, and strategy implementation in a dynamic and fast-paced environment. This role offers significant career growth.

Principal Responsibilities:

Work closely with the Senior Portfolio Manager to develop systematic macro strategies, focusing on alpha research, including idea generation, data preprocessing, statistical analysis, backtesting, and implementation.

Contribute to and enhance the internal research platform, including data pipelines, statistical learning tools, alpha analytics, and backtesting frameworks.

Independently explore and develop new alpha ideas while collaborating in a transparent and team-oriented environment.

Preferred Technical Skillset:

Strong research and programming skills, with proficiency in Python.

Solid experience with data analytics libraries (e.g., Pandas, SciPy, NumPy, Polars); extensive library-building experience is a plus.

Masters or PhD degree in a quantitative subject such as Applied Mathematics, Statistics, Physics, Engineering, Financial Engineering, Computer Science, or related field from a top-ranked university. Strong candidates with Bachelor's degree will also be considered.

Exceptional problem-solving abilities, intellectual curiosity (especially in alpha research), and a proactive research mindset.

Creativity and out-of-the-box thinking, combined with rigorous quantitative analysis.

Preferred Experience:

2+ years of experience in quantitative research with a focus on systematic macro strategies.

Preferred experience in hedge fund alpha research in commodities, FX, equity, and bond futures.

Experience in macro intraday strategies is a strong plus.

Experience in trading cost analysis is a plus.

Experience in machine learning is a plus.

Target Start Date:

Up to 12 months (strong preference for candidates who can start sooner)

Millennium pays a total compensation package which includes a base salary, discretionary performance bonus, and a comprehensive benefits package. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future.

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About the Position

As a Data Scientist on the platform prediction team, you will translate our probability of success predictions into measurable portfolio-level outcomes. You will architect core systems that let us rigorously evaluate signals from our AI-driven predictions in public and private equities and our internal portfolio.

This role sits at the intersection of quantitative finance, healthcare data, and AI-driven drug development. If you're excited about applying portfolio construction and risk management fundamentals to one of the most consequential prediction problems in healthcare, this is the role.

Responsibilities

• Work with the team to implement and maintain core portfolio engine: order management system, execution simulation layer, portfolio construction service, and performance tracking
• Design risk frameworks that quantify exposure across a portfolio of drug development bets with radically different risk profiles, timelines, and failure modes
• Run rigorous backtesting experiments with strict temporal constraints to evaluate Formation strategies against baseline approaches and measure marginal signal from new evidence sources
• Coordinate across the organization to integrate internal Formation data sources (clinical trial data, genomic evidence, real-world data) and proprietary tooling into portfolio analytics pipelines
• Work with product and engineering teams to build dashboards and reporting that communicate portfolio performance, risk metrics, and strategy comparisons to both technical and executive stakeholders
• Collaborate with the broader data science team to ensure portfolio-level evaluation feeds back into model improvement and evidence prioritization

About You

We are looking for a highly motivated and experienced Data Scientist to join our team. The ideal candidate will have a strong background in data science, machine learning, and software development, with a proven track record of delivering high-quality results in a fast-paced environment.

Requirements

• MS or PhD in a quantitative field (statistics, finance, physics, computational science, engineering, or related)
• 1-3 years in a quantitative research, data science, or analytics role , finance, healthcare, academic research, or consulting all count; substantive internships qualify
• Strong Python programming skills with experience in data-intensive workflows (pandas, numpy, scipy)
• Solid grasp of core portfolio construction and risk concepts: position sizing, rebalancing, Sharpe ratio, drawdown, volatility, benchmark comparison
• Demonstrated ability to work with messy, real-world datasets , comfortable with data wrangling, deduplication, and quality assessment
• Clear communicator who can present quantitative results to both technical peers and business stakeholders

Preferred Qualifications

• Experience with backtesting frameworks or portfolio simulation (vectorbt, Backtrader, or custom implementations)
• Exposure to healthcare, pharma, or biotech data (clinical trials, claims data, -omics, real-world evidence)
• Familiarity with alternative data in a research or investment context
• Experience with probability-of-success modeling, drug development decision analysis, or health economics
• Comfort with LLMs or AI/ML pipelines in a production or research setting
• Familiarity with dashboard/visualization tools (Streamlit, Plotly, Dash) and pipeline orchestration (Dagster, Airflow)

Total Compensation Range: $154,500 - $202,000

**Compensation Individual compensation is determined by several factors, including role scope, geographic location, and skills & experience. Your offer will reflect where you fall within the range based on these considerations. In addition to base salary, we offer equity, comprehensive benefits, and generous perks. If the posted range doesn't match your expectations, we still encourage you to apply!

**Where We Hire Formation Bio is prioritizing hiring in key hubs, primarily the New York City and Boston metro areas, with a hybrid model requiring 3 days per week in office. Applicants from the Research Triangle (NC) and San Francisco Bay Area may also be considered. Please apply only if you reside in these locations or are willing to relocate.

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You will carefully listen to stakeholder needs and then design rigorous math and physics analyses leading to clear and compelling value propositions.

You will work closely with related teams, including Systems Engineering, GNC, Propulsion, Communications, Flight Software, Mission Operations, and others.

This role is directly tied to ongoing, funded programs within Anduril’s Space Business Line. The programs require building and fielding a resilient, software-defined spacecraft systems across numerous mission threads.

We work with mission partners and customers to deploy reliable and robust capabilities on operationally relevant fielding timelines to meet complex challenges across the DOD and IC.

Responsibilities

• Develop model-driven analysis (and often the models themselves)
• Develop a deep understanding of transformational value of Anduril platform, autonomy, simulation, and perception products
• Use our advanced internal M&S capabilities to give our analyses an asymmetric advantage over competitors
• Convey the value through life of AI-powered distributed systems over traditional industrial offerings
• Provide rigorous analytical products which will be used to guide hardware engineering and convince stakeholders of our value advantage

Requirements

• Currently possesses and is able to maintain an active U.S. Secret security clearance
• Space Mission MS&A experience
• For example, working knowledge of constellation design, space trajectory design, and physics-level modeling across a wide variety of space missions
• Spacecraft & Payload MS&A experience, ability to perform physics-based trade-space analyses for spacecraft propulsion, power, thermal management, and payloads such as RADAR, SATCOM, and/or EOIR
• Outstanding communication skills to include visual presentation of complex data
• Strong background in orbital mechanics and space environment
• Proficiency with physics-math scripting (e.g., MATLAB, SIMULINK, Python+numpy, scipy)
• Strong engineering background, preferably in Astrodynamics, Aerospace Engineering, Dynamics and Controls Engineering, or other related engineering field

Preferred Qualifications

• 3+ years in a relevant Science/Engineering field
• Rendezvous Proximity Operations (RPO) Trajectory Design & Space Payloads experience
• Exceptional proficiency with physics-math scripting of your choice (e.g., MATLAB, SIMULINK, Python+numpy, scipy)
• Proficiency with STK and Astrogator
• Experience with genetic algorithms, machine learning, AI, and reinforcement learning algorithms

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You will collaborate closely with technical teams to support the development and refinement of new AI capabilities, with a primary focus on quantitative financial risk management domains. Your expertise will drive the selection and rigorous resolution of complex risk-related problems, including market risk modeling, credit and counterparty risk, liquidity and funding risk, operational and model risk, stress testing & scenario analysis, Value at Risk (VaR)/Expected Shortfall (ES), risk attribution, capital allocation (economic/regulatory), and enterprise-wide risk frameworks under regulatory regimes (Basel, Dodd-Frank, IFRS 9, etc.).

This role requires exceptional quantitative rigor, rapid adaptation to evolving guidelines, and the ability to deliver precise, technically sound critiques, derivations, and solutions in a fast-paced environment. As a Finance Risk Expert, you will directly support xAI's mission by helping train and refine frontier AI models. You will teach the models how risk professionals quantify uncertainties, model tail events, assess portfolio vulnerabilities, ensure regulatory compliance, perform stress testing, and make data-driven decisions to protect capital and maintain financial stability.

Your tasks may include recording audio walkthroughs of risk models, participating in video-based scenario reasoning, or producing detailed quantitative risk analysis traces. All outputs are considered work-for-hire and owned by xAI.

Responsibilities:

• Use proprietary annotation and evaluation software to deliver accurate labels, rankings, critiques, and comprehensive solutions on assigned projects
• Consistently produce high-quality, curated data that adheres to strict quantitative and regulatory standards
• Collaborate with engineers and researchers to develop and iterate on new training tasks, risk-specific benchmarks, and evaluation frameworks
• Provide constructive feedback to improve the efficiency, precision, and usability of annotation and data-collection tools
• Select and solve challenging problems from financial risk domains where you have deep expertise

Basic Qualifications:

• Master’s or PhD in a quantitative discipline: Quantitative Finance, Financial Engineering, Financial Mathematics, Statistics, Applied Mathematics, Econometrics, Risk Management, Operations Research, Physics, Computer Science (with risk/finance focus), or closely related field or equivalent professional experience as a quantitative risk analyst, risk modeler, or risk quant
• Excellent written and verbal English communication (technical reports, regulatory documentation, explanatory breakdowns)
• Strong familiarity with financial risk data sources and platforms (Bloomberg, Refinitiv, Moody’s Analytics, S&P Capital IQ, RiskMetrics, internal bank risk systems, regulatory filings, Basel/FRB datasets, etc.)
• Exceptional analytical reasoning, attention to detail, and ability to exercise sound judgment with incomplete or ambiguous data

Preferred Skills and Experience:

• Professional experience in quantitative risk management, model development/validation, or risk analytics at a bank, hedge fund, asset manager, insurance company, regulator, or consulting firm
• Track record of publication(s) or contributions in refereed journals/conferences on risk, econometrics, statistics, or quantitative finance
• Prior teaching, mentoring, or training experience (university, industry workshops, regulatory training)
• Proficiency in Python/R for risk modeling (pandas, NumPy, SciPy, statsmodels, QuantLib, PyTorch/TensorFlow for ML risk models, etc.) and familiarity with risk systems (Murex, Calypso, Numerix, etc.)
• Experience with Monte Carlo simulation, copula models, stochastic processes, time-series analysis, extreme value theory, or machine learning for risk (anomaly detection, credit scoring, etc.)
• Knowledge of regulatory capital frameworks (Basel III/IV, FRB CCAR, SR 11-7 model risk guidance, IFRS 9/CECL, Solvency II)
• CFA, FRM, PRM, CQF, or similar risk-focused certifications
• Previous exposure to large language models, AI safety, or quantitative evaluation pipelines

Location and Other Expectations:

• Tutor roles may be offered as full-time, part-time, or contractor positions, depending on role needs and candidate fit
• For contractor positions, hours will vary widely based on project scope and contractor availability, with no fixed commitments required
• Tutor roles may be performed remotely from any location worldwide, subject to legal eligibility, time-zone compatibility, and role specific needs
• For US based candidates, please note we are unable to hire in the states of Wyoming and Illinois at this time
• We are unable to provide visa sponsorship
• For those who will be working from a personal device, your computer must meet xAI’s minimum hardware requirements

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You will collaborate closely with our technical teams to support the development and refinement of new AI capabilities, with a particular emphasis on quantitative trading domains.

Your expertise will help select and solve challenging problems in systematic and quantitative strategies , including statistical arbitrage, factor investing, market microstructure modeling, high-frequency / execution algorithms, risk premia harvesting, machine learning-based alpha generation, and portfolio optimisation under realistic constraints.

This role requires strong analytical thinking, rapid adaptation to evolving guidelines, and the ability to provide rigorous, technically sound critiques and solutions in a fast-moving environment.

As a Quantitative Trader, you will directly contribute to xAI's mission by helping train and refine our frontier AI models.

You will teach the models how quantitative traders reason, model markets, evaluate signals, manage risk, and interact with complex financial data and systems.

This involves providing high-quality data in various formats (text, voice, video), writing detailed annotations, critiquing model outputs, recording audio explanations, and occasionally participating in structured video sessions.

We are looking for individuals who are enthusiastic about these data-generation activities, as they form a core part of advancing xAI’s goals in scientific discovery and real-world reasoning.

Quantitative Traders provide labeling, annotation, evaluation, and expert reasoning services across text, voice, and video data modalities to support model training and evaluation.

The role may include recording audio responses, participating in video-based tasks, or producing step-by-step quantitative reasoning traces , all of which are essential job functions required to fulfill xAI’s mission.

All outputs are considered work-for-hire and owned by xAI.

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As a Modeling & Simulation Engineer, you will be responsible for improving and adding to our sensor and communications model suite so that our operator training and internal engineering pipelines have a seamless translation from sim to real results.

Responsibilities

• Develop and enhance radar sensor models for use in simulation and evaluation of aeronautical vehicles.
• Translate theoretical models into efficient, reliable C++ implementations with a focus on numerical accuracy and performance.
• Validate models against real-world data and authoritative references, including field test data and calibration procedures.
• Collaborate with simulation and training application teams to ensure models integrate cleanly into operator-facing tools.
• Design automated validation and regression testing strategies for mathematical models to ensure fidelity across releases.
• Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning–based surrogates) to push the state of the art.
• Document assumptions, equations, and validation results so that both engineers and operators can trust model outputs.

Requirements

• BS or higher in Aerospace Engineering, Applied Math, Physics, or related field with 5+ years of aerospace modeling experience.
• C++ foundation with experience implementing numerical methods.
• Demonstrated experience with aerospace models such as: Radar sensors, Radio communications systems
• Experience validating simulations against real-world or experimental data.
• Ability to write clear documentation explaining assumptions, limitations, and expected behaviors of models.

Preferences

• 1+ years of experience working on pilot/operator training systems.
• Experience with Eigen or SciPy for model prototyping and validation.
• Familiarity with state estimation sensor models (GPS, IMU, Gyro, etc) for simulation environments.
• Demonstrated experience with payload sensor models including: Laser sensors, IR and optical cameras
• Knowledge of uncertainty quantification and statistical analysis methods.
• Experience with parallelization or GPU acceleration for compute-heavy models.
• Strong problem-solving mindset with a collaborative and detail-oriented approach.
• Familiarity with Python for test automation and data analysis pipelines.
• Passion for aerospace and autonomous vehicle systems.

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You will lead environmental and stress testing efforts, including temperature cycling, vibration, HALT, and HASS, conduct failure analysis and materials characterization, and analyze root cause investigations for manufacturing non-conformances and field returns. You'll participate in design reviews and FMEA activities, shape material selection and manufacturing requirements, analyze test and field data using reliability modeling tools, and help develop corrective actions and process improvements that elevate hardware reliability across the program.

Responsibilities: ude and implement design-for-reliability best practices, conducting rigorous testing, shaping manufacturing requirements, selecting materials, and analyzing field data to enhance the robustness of VBAT hardware. Perform stress screening, environmental testing, and drive failure analysis to ensure flight hardware meets reliability and performance targets. Analyze designs and test results to identify potential failure modes and mitigations. Collaborate with design engineers to implement design for reliability best practices early in design. Act as a key stakeholder in reviewing and approving designs for release. Participate in design reviews and failure mode effects analysis (FMEA) to assess potential reliability issues. Investigate manufacturing non-conformances and field hardware failures to determine root cause. Travel as needed to perform deep dives into supplier processes. Develop and recommend corrective actions to address identified reliability issues. Utilize reliability modeling and simulation tools to predict system performance and lifespan. Stay current with industry trends, advancements, and best practices in hardware reliability engineering. Propose and implement process improvements to drive improvements in reliability across the program.

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As a Modeling & Simulation Engineer, you will be responsible for improving and adding to our world and aeronautical models so that our operator training and internal engineering pipelines have a seamless translation from sim to real results.

Responsibilities

• Develop and enhance aeronautical physics models (aerodynamics, propulsion, environmental, structural, etc.) for use in simulation and evaluation.
• Translate theoretical models into efficient, reliable C++ implementations with a focus on numerical accuracy and performance.
• Validate models against real-world data and authoritative references, including field test data and calibration procedures.
• Collaborate with simulation and training application teams to ensure models integrate cleanly into operator-facing tools.
• Design automated validation and regression testing strategies for mathematical models to ensure fidelity across releases.
• Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning–based surrogates) to push the state of the art.
• Document assumptions, equations, and validation results so that both engineers and operators can trust model outputs.

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Responsibilities:

• Design, characterize, and deliver algorithms such as channelizers, frequency agile detection, adaptive filters, MIMO, wideband detectors, and other algorithms related to signal sorting
• Write and contribute to existing Python repositories using CUDA and PyTorch
• Own requirements, ICDs, and verification from concept through delivery
• Stay current: Track and insert advances in signal processing techniques and associated technologies, adaptive beamforming, RF machine learning, and resilient PNT for GPS-denied ops.

Required Qualifications:

• Masters Degree in Electrical, Computer or Systems Engineering or related field with Graduate study emphasis in Signal Processing; OR a Bachelor’s Degree in an Engineering discipline with 3-5 years relevant working Signal Processing Experience
• Intermediate to advanced proficiency in Python
• Willingness to support critical test events that occasionally require extended hours/weekends.
• Ability to obtain and maintain a security clearance. Learn more about Security Clearances here.
• Must be a U.S. Person (see ITAR Regulations below) due to required access to U.S. export-controlled information or facilities

Bonus Points:

• PhD in Electrical Engineering, Computer Engineering, or related field
• 5+ years’ experience with EW subsystems and payloads.
• EA/ECM technique design (deception, Digital RF Memory, coherent/non-coherent techniques).
• Comms system design (LPI/LPD, Waveform-of-Interest exploitation)
• RF machine learning for emitter ID, modulation/classification, anomaly detection, PDW creation
• Tools Experience: ADS/AWR/SystemVue, MATLAB/Simulink, Python (NumPy/SciPy), GNU Radio/SDR (USRP/RFSoC), VITA-49; HDL/firmware experience also helpful (Vivado/Quartus/Libero).
• Clearance: Active Secret or ability to obtain and maintain; TS/SCI eligibility preferred. ITAR/EAR-controlled work.
• Field work: supporting periodic travel for flight tests and customer demonstrations/support
• Mindset: Builder-tester who loves first-principles RF, rapid lab iteration, and getting hardware flying fast.

What We Offer:

• Competitive salary, stock options and benefits, including health, vision and dental.
• 401K enrollment at 90 days.
• Generous PTO + most Federal Holidays observed.
• Collaborative and inclusive work environment.
• Access to the latest tools and technologies.
• High levels of responsibility and autonomy.
• Professional growth and development opportunities.
• Access to the hardest problems in electronic warfare.

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This role requires an onsite presence in our El Segundo, CA HQ; a remote work environment is not considered for this opportunity.

Key Responsibilities:

• Design, validate & deliver RF payload subsystems: Design, Integration and Testing of Antennas, RF front ends, SDRs and provide validation of operation. Support development and analysis of RF performance metrics.

• Own requirements, ICDs, and verification from concept through delivery

• Stay current: Track and insert advances in relevant technologies, SDRs, RF Systems, RF machine learning, and PNT.

Qualifications:

• BS/MS in Electrical Engineering, Computer Engineering, or related field

• Antenna and receiver design across analog/RF/mixed-signal domains: mixers, PLLs/LOs, gain/linearity/noise trades; ability to turn simulations into reality.

• Channelizers & wideband architectures: filter banks, analog/digital down conversion, sample-rate planning, clocking/synchronization.

• Willingness to support critical test events that occasionally require extended hours/weekends

• Ability to obtain and maintain a security clearance

• Must be a U.S. Person (see ITAR Regulations below) due to required access to U.S. export-controlled information or facilities

Bonus Points:

• PhD in Electrical Engineering, Computer Engineering, or related field

• 5+ years’ experience with EW subsystems and payloads

• EA/ECM technique design (deception, Digital RF Memory, coherent/non-coherent techniques)

• Digital Signal processing: Design characterize and deliver channelizers, wideband detection/classification, MIMO/digital-arrays, and algorithms in MATLAB/Python/C++

• Communication / Waveform system design

• RF machine learning for emitter ID, modulation/classification, anomaly detection, PDW creation

• Tools Experience: ADS/AWR/SystemVue, MATLAB/Simulink, Python (NumPy/SciPy), GNU Radio/SDR (USRP/RFSoC), VITA-49; HDL/firmware experience; also helpful (Vivado/Quartus/Libero).

• Clearance: Active Secret or ability to obtain and maintain; TS/SCI eligibility preferred. ITAR/EAR-controlled work. Learn more about Security Clearances here.

• Field work: supporting periodic travel for flight tests and customer demonstrations/support

• Mindset: Builder-tester who loves first-principles RF, rapid lab iteration, and getting hardware flying fast

What We Offer:

• Competitive salary, stock options and benefits, including health, vision and dental.

• 401K enrollment at 90 days.

• Generous PTO + most Federal Holidays observed.

• Collaborative and inclusive work environment.

• Access to the latest tools and technologies.

• High levels of responsibility and autonomy.

• Professional growth and development opportunities.

• Access to the hardest problems in electronic warfare.

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Your work may involve brainstorming new disruptive ideas that could become the next generation of frontier AI models, particularly within the text diffusion space. You will prototype and develop these ideas with the rest of the team, contributing directly to Gemini Diffusion research. You will solve key research challenges by designing and executing experimental research on text diffusion models, sharing analyses, and proposing next steps. You will rigorously validate the theoretical and practical impact of our work at a large scale. You will work collaboratively with other Generative AI teams to move the technologies we develop out of the lab and into production. You will advance the fundamental architecture, algorithmic design, and capabilities of large-scale diffusion models. You will bring deep scientific expertise into our projects, sharing your insights and knowledge with other researchers and engineers.

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